Comparison of sperm motility subpopulation structure among wild anadromous and farmed male Atlantic salmon (Salmo salar) parr using a CASA system.

PubMed

Caldeira, Carina; García-Molina, Almudena; Valverde, Anthony; Bompart, Daznia; Hassane, Megan; Martin, Patrick; Soler, Carles

2018-04-13

Atlantic salmon (Salmo salar) is an endangered freshwater species that needs help to recover its wild stocks. However, the priority in aquaculture is to obtain successful fertilisation and genetic variability to secure the revival of the species. The aims of the present work were to study sperm subpopulation structure and motility patterns in wild anadromous males and farmed male Atlantic salmon parr. Salmon sperm samples were collected from wild anadromous salmon (WS) and two generations of farmed parr males. Sperm samples were collected from sexually mature males and sperm motility was analysed at different times after activation (5 and 35s). Differences among the three groups were analysed using statistical techniques based on Cluster analysis the Bayesian method. Atlantic salmon were found to have three sperm subpopulations, and the spermatozoa in ejaculates of mature farmed parr males had a higher velocity and larger size than those of WS males. This could be an adaptation to high sperm competition because salmonid species are naturally adapted to this process. Motility analysis enables us to identify sperm subpopulations, and it may be useful to correlate these sperm subpopulations with fertilisation ability to test whether faster-swimming spermatozoa have a higher probability of success.

Conformational Transition Pathways of Epidermal Growth Factor Receptor Kinase Domain from Multiple Molecular Dynamics Simulations and Bayesian Clustering.

PubMed

Li, Yan; Li, Xiang; Ma, Weiya; Dong, Zigang

2014-08-12

The epidermal growth factor receptor (EGFR) is aberrantly activated in various cancer cells and an important target for cancer treatment. Deep understanding of EGFR conformational changes between the active and inactive states is of pharmaceutical interest. Here we present a strategy combining multiply targeted molecular dynamics simulations, unbiased molecular dynamics simulations, and Bayesian clustering to investigate transition pathways during the activation/inactivation process of EGFR kinase domain. Two distinct pathways between the active and inactive forms are designed, explored, and compared. Based on Bayesian clustering and rough two-dimensional free energy surfaces, the energy-favorable pathway is recognized, though DFG-flip happens in both pathways. In addition, another pathway with different intermediate states appears in our simulations. Comparison of distinct pathways also indicates that disruption of the Lys745-Glu762 interaction is critically important in DFG-flip while movement of the A-loop significantly facilitates the conformational change. Our simulations yield new insights into EGFR conformational transitions. Moreover, our results verify that this approach is valid and efficient in sampling of protein conformational changes and comparison of distinct pathways.

Water chemistry and its effects on the physiology and survival of Atlantic salmon Salmo salar smolts

USGS Publications Warehouse

Liebich, T.; McCormick, S.D.; Kircheis, D.; Johnson, Kevin; Regal, R.; Hrabik, T.

2011-01-01

The physiological effects of episodic pH fluctuations on Atlantic salmon Salmo salar smolts in eastern Maine, U.S.A., were investigated. During this study, S. salar smolts were exposed to ambient stream-water chemistry conditions at nine sites in four catchments for 3 and 6 day intervals during the spring S. salar smolt migration period. Plasma chloride, plasma glucose, gill aluminium and gill Na+- and K+-ATPase levels in S. salar smolts were assessed in relation to ambient stream-water chemistry during this migration period. Changes in both plasma chloride and plasma glucose levels of S. salar smolts were strongly correlated with stream pH, and S. salar smolt mortality occurred in one study site with ambient stream pH between 5??6 and 5??8 during the study period. The findings from this study suggest that physiological effects on S. salar smolts are strongly correlated with stream pH and that in rivers and streams with low dissolved organic carbon (DOC) concentrations the threshold for physiological effects and mortality probably occurs at a higher pH and shorter exposure period than in rivers with higher DOC. Additionally, whenever an acidification event in which pH drops below 5??9 coincides with S. salar smolt migration in eastern Maine rivers, there is potential for a significant reduction in plasma ions of S. salar smolts. ?? 2011 The Fisheries Society of the British Isles.

Quantile regression and Bayesian cluster detection to identify radon prone areas.

PubMed

Sarra, Annalina; Fontanella, Lara; Valentini, Pasquale; Palermi, Sergio

2016-11-01

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys. Copyright © 2016 Elsevier Ltd. All rights reserved.

Verification of Bayesian Clustering in Travel Behaviour Research – First Step to Macroanalysis of Travel Behaviour

NASA Astrophysics Data System (ADS)

Satra, P.; Carsky, J.

2018-04-01

Our research is looking at the travel behaviour from a macroscopic view, taking one municipality as a basic unit. The travel behaviour of one municipality as a whole is becoming one piece of a data in the research of travel behaviour of a larger area, perhaps a country. A data pre-processing is used to cluster the municipalities in groups, which show similarities in their travel behaviour. Such groups can be then researched for reasons of their prevailing pattern of travel behaviour without any distortion caused by municipalities with a different pattern. This paper deals with actual settings of the clustering process, which is based on Bayesian statistics, particularly the mixture model. An optimization of the settings parameters based on correlation of pointer model parameters and relative number of data in clusters is helpful, however not fully reliable method. Thus, method for graphic representation of clusters needs to be developed in order to check their quality. A training of the setting parameters in 2D has proven to be a beneficial method, because it allows visual control of the produced clusters. The clustering better be applied on separate groups of municipalities, where competition of only identical transport modes can be found.

Farmed Atlantic salmon Salmo salar L. parr may reduce early survival of wild fish.

PubMed

Sundt-Hansen, L; Huisman, J; Skoglund, H; Hindar, K

2015-06-01

The study examined the density-mediated effects on growth, survival and dispersal of wild and farmed Atlantic salmon Salmo salar offspring in the period immediately following emergence, using a substitutive design. In small confined stream channels, wild parr coexisting with farmed parr had a significantly poorer survival, than wild parr alone. Density did not affect this relationship. In larger unconfined stream channels, wild parr coexisting with farmed parr entered a downstream trap in higher numbers than wild parr in allopatry. The results suggests that during the earliest life stages, farmed S. salar can outcompete wild S. salar, resulting in a reduced survival of wild S. salar. © 2015 The Fisheries Society of the British Isles.

The pre-spawning migratory behaviour of Atlantic salmon Salmo salar in a large lacustrine catchment.

PubMed

Kennedy, R J; Allen, M

2016-09-01

The movements of adult Atlantic salmon Salmo salar were determined as they migrated to spawning habitats in a large lacustrine catchment, Lough Neagh, in Northern Ireland. The minimum average ground speed of S. salar through the lake was 2·1 km day(-1) and the mean residence time was 11 days. Tagged S. salar tended to actively migrate through the lake which represented a transitory habitat for adult S. salar. Migration time from the release site, through the lake, to a spawning tributary decreased during the migratory period. During the 4 year study period between 20·5 and 41·6% of tagged S. salar which entered the lake each year, explored at least one other channel before ascending the final spawning tributary. Exploratory behaviour was more likely in S. salar which spawned in the tributaries furthest from the sea. Exploratory behaviour was also more likely to occur during periods of reduced discharge in the natal stream. The fishery management implications of complex pre-spawning behaviour in a mixed stock lacustrine system, are discussed. © 2016 The Fisheries Society of the British Isles.

Bayesian investigation of isochrone consistency using the old open cluster NGC 188

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hills, Shane; Courteau, Stéphane; Von Hippel, Ted

2015-03-01

This paper provides a detailed comparison of the differences in parameters derived for a star cluster from its color–magnitude diagrams (CMDs) depending on the filters and models used. We examine the consistency and reliability of fitting three widely used stellar evolution models to 15 combinations of optical and near-IR photometry for the old open cluster NGC 188. The optical filter response curves match those of theoretical systems and are thus not the source of fit inconsistencies. NGC 188 is ideally suited to this study thanks to a wide variety of high-quality photometry and available proper motions and radial velocities thatmore » enable us to remove non-cluster members and many binaries. Our Bayesian fitting technique yields inferred values of age, metallicity, distance modulus, and absorption as a function of the photometric band combinations and stellar models. We show that the historically favored three-band combinations of UBV and VRI can be meaningfully inconsistent with each other and with longer baseline data sets such as UBVRIJHK{sub S}. Differences among model sets can also be substantial. For instance, fitting Yi et al. (2001) and Dotter et al. (2008) models to UBVRIJHK{sub S} photometry for NGC 188 yields the following cluster parameters: age = (5.78 ± 0.03, 6.45 ± 0.04) Gyr, [Fe/H] = (+0.125 ± 0.003, −0.077 ± 0.003) dex, (m−M){sub V} = (11.441 ± 0.007, 11.525 ± 0.005) mag, and A{sub V} = (0.162 ± 0.003, 0.236 ± 0.003) mag, respectively. Within the formal fitting errors, these two fits are substantially and statistically different. Such differences among fits using different filters and models are a cautionary tale regarding our current ability to fit star cluster CMDs. Additional modeling of this kind, with more models and star clusters, and future Gaia parallaxes are critical for isolating and quantifying the most relevant uncertainties in stellar evolutionary models.« less

Nonparametric Bayesian clustering to detect bipolar methylated genomic loci.

PubMed

Wu, Xiaowei; Sun, Ming-An; Zhu, Hongxiao; Xie, Hehuang

2015-01-16

With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events. Among these methylation patterns, bipolar patterns are important as they may originate from allele-specific methylation (ASM) or cell-specific methylation (CSM). Utilizing nonparametric Bayesian clustering followed by hypothesis testing, we have developed a novel statistical approach to identify bipolar methylated genomic regions in bisulfite sequencing data. Simulation studies demonstrate that the proposed method achieves good performance in terms of specificity and sensitivity. We used the method to analyze data from mouse brain and human blood methylomes. The bipolar methylated segments detected are found highly consistent with the differentially methylated regions identified by using purified cell subsets. Bipolar DNA methylation often indicates epigenetic heterogeneity caused by ASM or CSM. With allele-specific events filtered out or appropriately taken into account, our proposed approach sheds light on the identification of cell-specific genes/pathways under strong epigenetic control in a heterogeneous cell population.

Seawater tolerance and post-smolt migration of wild Atlantic salmon Salmo salar × brown trout S. trutta hybrid smolts.

PubMed

Urke, H A; Kristensen, T; Arnekleiv, J V; Haugen, T O; Kjærstad, G; Stefansson, S O; Ebbesson, L O E; Nilsen, T O

2013-01-01

High levels of hybridization between Atlantic salmon Salmo salar and brown trout Salmo trutta have been reported in the River Driva. This study presents the underlying mechanisms of development of seawater (SW) tolerance and marine migration pattern for S. salar×S. trutta hybrids. Migrating S. salar×S. trutta hybrid smolts caught in the River Driva, Norway (a river containing Gyrodactylus salaris), displayed freshwater (FW) gill Na(+), K(+) -ATPase (NKA) activity levels of 11·8 µmol ADP mg protein h(-1), which were equal to or higher than activity levels observed in S. salar and S. trutta smolts. Following 4 days of SW exposure (salinity 32·3), enzyme activity remained high and plasma ion levels were maintained within the normal physiological range observed in S. salar smolts, indicating no signs of ion perturbations in S. salar×S. trutta hybrids. SW exposure induced an increase in NKA α1b-subunit mRNA levels with a concurrent decrease in α1a levels. Salmo salar×S. trutta post-smolts migrated rapidly through the fjord system, with increasing speed with distance from the river, as is often seen in S. salar smolts. The present findings suggest that S. salar×S. trutta smolts, as judged by the activity and transcription of the NKA system, regulation of plasma ion levels and migration speed more closely resemble S. salar than S. trutta. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Upstream migratory behaviour of wild and ranched Atlantic salmon Salmo salar at a natural obstacle in a coastal spate river.

PubMed

Kennedy, R J; Moffett, I; Allen, M M; Dawson, S M

2013-09-01

The upstream migratory behaviour of wild and ranched Atlantic salmon Salmo salar in a small Irish coastal spate river was investigated using acoustic telemetry. Prespawning migratory behaviour was investigated including movement patterns at a large natural waterfall in the lower reaches of the river. A strong diurnal pattern was observed for upstream migrants at the waterfall indicative of the need for daylight to ascend this complex natural obstacle to migration. Successful passage of the waterfall was also associated with distinct environmental conditions and no difference in migratory ability was detected between wild and ranched origin S. salar. Wild S. salar tended to exhibit a non-erratic, stepwise upstream migration pattern after ascending the waterfall while ranched S. salar had an increased probability of displaying more erratic migratory behaviour. Wild S. salar penetrated further into the river catchment than ranched S. salar, although male ranched S. salar exhibited the greatest cumulative distance moved prior to the spawning period. The management implications of escaped or released ranched S. salar and movement at natural obstacles are discussed. © 2013 The Fisheries Society of the British Isles.

A practical Bayesian stepped wedge design for community-based cluster-randomized clinical trials: The British Columbia Telehealth Trial.

PubMed

Cunanan, Kristen M; Carlin, Bradley P; Peterson, Kevin A

2016-12-01

Many clinical trial designs are impractical for community-based clinical intervention trials. Stepped wedge trial designs provide practical advantages, but few descriptions exist of their clinical implementational features, statistical design efficiencies, and limitations. Enhance efficiency of stepped wedge trial designs by evaluating the impact of design characteristics on statistical power for the British Columbia Telehealth Trial. The British Columbia Telehealth Trial is a community-based, cluster-randomized, controlled clinical trial in rural and urban British Columbia. To determine the effect of an Internet-based telehealth intervention on healthcare utilization, 1000 subjects with an existing diagnosis of congestive heart failure or type 2 diabetes will be enrolled from 50 clinical practices. Hospital utilization is measured using a composite of disease-specific hospital admissions and emergency visits. The intervention comprises online telehealth data collection and counseling provided to support a disease-specific action plan developed by the primary care provider. The planned intervention is sequentially introduced across all participating practices. We adopt a fully Bayesian, Markov chain Monte Carlo-driven statistical approach, wherein we use simulation to determine the effect of cluster size, sample size, and crossover interval choice on type I error and power to evaluate differences in hospital utilization. For our Bayesian stepped wedge trial design, simulations suggest moderate decreases in power when crossover intervals from control to intervention are reduced from every 3 to 2 weeks, and dramatic decreases in power as the numbers of clusters decrease. Power and type I error performance were not notably affected by the addition of nonzero cluster effects or a temporal trend in hospitalization intensity. Stepped wedge trial designs that intervene in small clusters across longer periods can provide enhanced power to evaluate comparative

Evidence for episodic acidification effects on migrating Atlantic salmon Salmo salar smolts

USGS Publications Warehouse

Kelly, John T; Lerner, Darrren T.; O'Dea, Michael F.; Regish, Amy M.; Monette, Michelle Y.; Hawkes, J.P.; Nislow, Keith H.; McCormick, Stephen

2015-01-01

Field studies were conducted to determine levels of gill aluminium as an index of acidification effects on migrating Atlantic salmon Salmo salar smolts in the north-eastern U.S.A. along mainstem river migration corridors in several major river basins. Smolts emigrating from the Connecticut River, where most (but not all) tributaries were well buffered, had low or undetectable levels of gill aluminium and high gill Na+/K+-ATPase (NKA) activity. In contrast, smolts emigrating from the upper Merrimack River basin where most tributaries are characterized by low pH and high inorganic aluminium had consistently elevated gill aluminium and lower gill NKA activity, which may explain the low adult return rates of S. salar stocked into the upper Merrimack catchment. In the Sheepscot, Narraguagus and Penobscot Rivers in Maine, river and year-specific effects on gill aluminium were detected that appeared to be driven by underlying geology and high spring discharge. The results indicate that episodic acidification is affecting S. salar smolts in poorly buffered streams in New England and may help explain variation in S. salar survival and abundance among rivers and among years, with implications for the conservation and recovery of S. salar in the north-eastern U.S.A. These results suggest that the physiological condition of outmigrating smolts may serve as a large-scale sentinel of landscape-level recovery of atmospheric pollution in this and other parts of the North Atlantic region.

Evidence for episodic acidification effects on migrating Atlantic salmon Salmo salar smolts.

PubMed

Kelly, J T; Lerner, D T; O'Dea, M F; Regish, A M; Monette, M Y; Hawkes, J P; Nislow, K H; McCormick, S D

2015-11-01

Field studies were conducted to determine levels of gill aluminium as an index of acidification effects on migrating Atlantic salmon Salmo salar smolts in the north-eastern U.S.A. along mainstem river migration corridors in several major river basins. Smolts emigrating from the Connecticut River, where most (but not all) tributaries were well buffered, had low or undetectable levels of gill aluminium and high gill Na(+) /K(+) -ATPase (NKA) activity. In contrast, smolts emigrating from the upper Merrimack River basin where most tributaries are characterized by low pH and high inorganic aluminium had consistently elevated gill aluminium and lower gill NKA activity, which may explain the low adult return rates of S. salar stocked into the upper Merrimack catchment. In the Sheepscot, Narraguagus and Penobscot Rivers in Maine, river and year-specific effects on gill aluminium were detected that appeared to be driven by underlying geology and high spring discharge. The results indicate that episodic acidification is affecting S. salar smolts in poorly buffered streams in New England and may help explain variation in S. salar survival and abundance among rivers and among years, with implications for the conservation and recovery of S. salar in the north-eastern U.S.A. These results suggest that the physiological condition of outmigrating smolts may serve as a large-scale sentinel of landscape-level recovery of atmospheric pollution in this and other parts of the North Atlantic region. © 2015 The Fisheries Society of the British Isles.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

Morphometric of blastomeres in Salmo salar.

PubMed

Effer, Brian R; Sánchez, Rubén R; Ubilla, Andrea M; Figueroa, Elías V; Valdebenito, Iván I

2014-11-01

For Salmo salar, there is a lack of information on the morphology of the first blastomeres formed during embryonic development and which could be used as a diagnostic tool for the first stages of development. The purpose of this investigation, therefore, was to characterize morphometrically the first blastomeres of S. salar. From a pool of eggs incubated at 7.5°C, 100 microphotographs of blastodiscs were extracted and analyzed at different incubation periods: 12, 14, 16, 20 or 24 h. Blastodiscs were characterized morphologically after 16, 20 or 24 h incubation, and classified into symmetric or asymmetric groups according to their morphology. The ratio of length (L) versus width (W) of each blastomere was determined, to establish its symmetry. In addition, 20 microphotographs of blastodiscs of normal appearance were analysed morphologically (control blastodisc: CB) for comparison (20 or 24 h). Results show that the first cleavage ends after 16 h of development. Seven categories were established during blastomere characterization: 47% normal (G1); 27% with dispersed margins (G2); 10% unequal (G3); 9% 'pie-shaped' (G4); 3% amorphous (G5); 2% three equal blastomeres and one different one (G6); and 2% with eccentric cleavage (G7). Although the incidence of abnormal cleavage in S. salar is uncertain, there is a potential for some asymmetries to be corrected during embryogenesis to generate viable individuals. More studies are necessary to correlate these abnormal cleavage patterns with indicators of quality in the later stages of embryogenesis in this species, to establish a quality assessment tool for gametes and/or embryos in salmonid species.

SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss

PubMed Central

Di Génova, Alex; Aravena, Andrés; Zapata, Luis; González, Mauricio; Maass, Alejandro; Iturra, Patricia

2011-01-01

SalmonDB is a new multiorganism database containing EST sequences from Salmo salar, Oncorhynchus mykiss and the whole genome sequence of Danio rerio, Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes and Takifugu rubripes, built with core components from GMOD project, GOPArc system and the BioMart project. The information provided by this resource includes Gene Ontology terms, metabolic pathways, SNP prediction, CDS prediction, orthologs prediction, several precalculated BLAST searches and domains. It also provides a BLAST server for matching user-provided sequences to any of the databases and an advanced query tool (BioMart) that allows easy browsing of EST databases with user-defined criteria. These tools make SalmonDB database a valuable resource for researchers searching for transcripts and genomic information regarding S. salar and other salmonid species. The database is expected to grow in the near feature, particularly with the S. salar genome sequencing project. Database URL: http://genomicasalmones.dim.uchile.cl/ PMID:22120661

SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss.

PubMed

Di Génova, Alex; Aravena, Andrés; Zapata, Luis; González, Mauricio; Maass, Alejandro; Iturra, Patricia

2011-01-01

SalmonDB is a new multiorganism database containing EST sequences from Salmo salar, Oncorhynchus mykiss and the whole genome sequence of Danio rerio, Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes and Takifugu rubripes, built with core components from GMOD project, GOPArc system and the BioMart project. The information provided by this resource includes Gene Ontology terms, metabolic pathways, SNP prediction, CDS prediction, orthologs prediction, several precalculated BLAST searches and domains. It also provides a BLAST server for matching user-provided sequences to any of the databases and an advanced query tool (BioMart) that allows easy browsing of EST databases with user-defined criteria. These tools make SalmonDB database a valuable resource for researchers searching for transcripts and genomic information regarding S. salar and other salmonid species. The database is expected to grow in the near feature, particularly with the S. salar genome sequencing project. Database URL: http://genomicasalmones.dim.uchile.cl/

Multi-temporal remote sensing analysis of salars in El Loa Province, Chile: Implications for water resource management

NASA Astrophysics Data System (ADS)

Markovich, K.; Pierce, S. A.

2011-12-01

Salar de Ascotán and Salar de Carcote are internally drained, evaporative basins located in the Atacama Desert, 200 km northeast of Antofogasta in Region II, Chile. The two salars are part of a regional groundwater system that recharges in the adjacent uplands to the east and terminates in the regional topographic low at Salar de Uyuni, Bolivia. This regional groundwater system is discharged locally as spring-fed perennial surface water that flows across the salar surface and either evaporates, or reinfiltrates, in lagoon-like environments. This perennial surface water supports diverse flora and fauna in the salar basins, including flamingo, vicuña, and the endemic fish species Orestias ascotanensis. Mining projects in the region began pumping the groundwater system in the Ascotán basin in the mid-1990's, leading to concern about the preservation of spring-fed surface flows. While hydrologic and ecologic monitoring efforts have been coordinated, data collection is limited to in-situ measurements and antecedent records precede extraction by approximately six months. Remote sensing can provide a means for large scale monitoring of the salars, as well as providing additional historical data to support environmental management of the systems. This comparative study utilizes satellite imagery to detect changes in surface water extent in the two salars and evaluate the results for possible correlation with climatic and/or anthropogenic factors. Landsat TM and ETM+ images from the time period of 1986-2011 are analyzed for surface water extent, and geographic information technologies are used to integrate the remotely sensed data with in-situ measurements. Early results indicate that surface water extent on the salar surface has diminished from 1986 and present day conditions. The decrease is most pronounced in the Ascotán basin, suggesting a possible correlation to anthropogenic influences. Also, the rate of decrease in surface water presence is most elevated in the

Effects of hydropeaking on the spawning behaviour of Atlantic salmon Salmo salar and brown trout Salmo trutta.

PubMed

Vollset, K W; Skoglund, H; Wiers, T; Barlaup, B T

2016-06-01

An in situ camera set-up was used to study the spawning activity of Atlantic salmon Salmo salar and brown trout Salmo trutta throughout two consecutive seasons in a spawning area affected by hydropower-related pulse flows due to hydropeaking. The purpose was to test whether the flow variation discouraged spawning in shallow areas or motivated spawning into areas with elevated risk of incubation mortality. There were more S. salar observed on the spawning ground during days with high discharge. The presence of S. salar in the spawning grounds was not affected by the hydropeaking cycles of the preceding night. Female S. salar were observed preparing nests within the first hour after water discharge had increased to levels suitable for spawning. In contrast, the number of S. trutta was not correlated with flow and nest preparation was also observed at a discharge corresponding to the lowest discharge levels during a hydropeaking cycle. Survival was generally high in nests excavated the following winter, with only 5·4% suffering mortality due to dewatering. The results suggest that S. salar may respond rapidly to variable-flow conditions and utilize short windows with suitable flows for spawning. Smaller S. trutta may utilize low-flow conditions to spawn in areas that are not habitable by larger S. salar during low flow. © 2016 The Fisheries Society of the British Isles.

An immunological method for quantifying antibacterial activity in Salmo salar (Linnaeus, 1758) skin mucus.

PubMed

Narvaez, Edgar; Berendsen, Jorge; Guzmán, Fanny; Gallardo, José A; Mercado, Luis

2010-01-01

Antimicrobial peptides (AMPs) are a pivotal component of innate immunity in lower vertebrates. The aim of this study was to develop an immunological method for quantifying AMPs in Salmo salar skin mucus. A known antimicrobial peptide derived from histone H1 previously purified and described from S. salar skin mucus (SAMP H1) was chemically synthesized and used to obtain antibodies for the quantification of the molecule via ELISA. Using skin mucus samples, a correlation of bacterial growth inhibition versus SAMP H1 concentration (ELISA) was established. The results provide the first evidence for quantifying the presence of active AMPs in the skin mucus of S. salar through the use of an immunological method. Copyright 2009 Elsevier Ltd. All rights reserved.

Environmental change influences the life history of salmon Salmo salar in the North Atlantic Ocean.

PubMed

Jonsson, B; Jonsson, N; Albretsen, J

2016-02-01

Annual mean total length (LT) of wild one-sea-winter (1SW) Atlantic salmon Salmo salar of the Norwegian River Imsa decreased from 63 to 54 cm with a corresponding decrease in condition factor (K) for cohorts migrating to sea from 1976 to 2010. The reduction in LT is associated with a 40% decline in mean individual mass, from 2 to 1·2 kg. Hatchery fish reared from parental fish of the same population exhibited similar changes from 1981 onwards. The decrease in LT correlated negatively with near-surface temperatures in the eastern Norwegian Sea, thought to be the main feeding area of the present stock. Furthermore, S. salar exhibited significant variations in the proportion of cohorts attaining maturity after only one winter in the ocean. The proportion of S. salar spawning as 1SW fish was lower both in the 1970s and after 2000 than in the 1980s and 1990s associated with a gradual decline in post-smolt growth and smaller amounts of reserve energy in the fish. In wild S. salar, there was a positive association between post-smolt growth and the sea survival back to the River Imsa for spawning. In addition, among smolt year-classes, there were significant positive correlations between wild and hatchery S. salar in LT, K and age at maturity. The present changes may be caused by ecosystem changes following the collapse and rebuilding of the pelagic fish abundance in the North Atlantic Ocean, a gradual decrease in zooplankton abundance and climate change with increasing surface temperature in the Norwegian Sea. Thus, the observed variation in the life-history traits of S. salar appears primarily associated with major changes in the pelagic food web in the ocean. © 2016 The Fisheries Society of the British Isles.

Modeling the Salar de Uyuni, Bolivia as an Equipotential Surface of Earth's Gravity Field

NASA Technical Reports Server (NTRS)

Borsa, Adrian; Bills, Bruce

2004-01-01

The salar de Uyuni is a massive dry salt lake that lies at the lowest point of an internal/drainage basin in the Bolivian Altiplano. Its topography is remarkable for its extraordinary flatness over almost a full degree of latitude and longitude. We surveyed a 54 x 45 km region of the salar with kinematic GPS in September, 2002 and found a topographic range of only 80 cm over the entire surveyed area. Furthermore, the survey revealed distinct surface features with several dominant wavelengths and orientations. Some of these appear to be aligned with orographic features that intersect the salar, leading us to conjecture that they are the surface expression of high-density mountains that have been buried by low-density basin sediments. Over the oceans, a similar correspondence between basin bathymetry and surface topography is exploited to map the seafloor using sea-surface satellite altimetry measurements, with the sea surface following geoid undulations due to the underwater mass distribution. On the salar, annual flooding creates a shallow lake whose surface also lies on a equipotential surface shaped by the distribution of underlying mass. The link to the actual salar surface is via the dissolution and redeposition of salt by the lake waters, which appears to push the system to an equilibrium of constant water depth and the coincidence of the shapes of the lake surface and bottom. To test our hypothesis about the origin of the surface features on the salar, we compare our GPS survey elevations with the equipotential surface generated from local gravity measurements in conjunction with gravity and potential values from the EGM96 global geopotential model. 50% of the variance of the GPS elevations can be explained by equipotential surface undulations from the EGM96 model alone, and an additional 40% is explained by the shorter-wavelength equipotential surface derived from local gravity. We examine the unexplained 10% of elevation variance from the standpoint of

Development of seawater tolerance and subsequent downstream migration in wild and stocked young-of-the-year derived Atlantic salmon Salmo salar smolts.

PubMed

Urke, H A; Arnekleiv, J V; Nilsen, T O; Nilssen, K J

2014-01-01

This study investigated the development of hypo-osmoregulatory capacity and timing of downstream migration in wild Atlantic salmon Salmo salar smolts from the River Stjørdalselva and stocked young-of-the-year (YOY), derived S. salar smolts from the tributary River Dalåa. Both wild and stocked S. salar smolts developed seawater (SW) tolerance in early May, persisting through June, measured as their ability to regulate plasma osmolality and chloride following 24 h SW (salinity = 35) exposure. Although the majority of downstream migration among the stocked S. salar smolts occurred later than observed in their wild counterparts, the development of SW tolerance occurred concurrently. The wild S. salar from Stjørdalselva and stocked YOY smolts from the River Dalåa started to migrate on the same cumulative day-degrees (D°). The study revealed no downstream migration before development of SW tolerance. This emphasizes the importance of incorporating physiological status when studying environmental triggers for downstream migration of S. salar smolts. Overall, these findings suggest that the onset of smolt migration in stocked S. salar smolts was within the smolt window from an osmoregulatory point of view. © 2014 The Fisheries Society of the British Isles.

The origin of brines and salts in Chilean salars: a hydrochemical review

NASA Astrophysics Data System (ADS)

Risacher, François; Alonso, Hugo; Salazar, Carlos

2003-11-01

Northern Chile is characterized by a succession of north-south-trending ranges and basins occupied by numerous saline lakes and salt crusts, collectively called salars. Fossil salt crusts are found to the west in the extremely arid Central Valley, while active salars receiving permanent inflows fill many intravolcanic basins to the east in the semiarid Cordillera. Sea salts and desert dust are blown eastward over the Cordillera, where they constitute an appreciable fraction of the solute load of very dilute waters (salt content<0.1 g/l). The weathering of volcanic rocks contributes most components to inflow waters with salt content ranging from 0.1 to 0.6 g/l. However, the average salt content of all inflows is much higher: about 3.2 g/l. Chemical composition, Cl/Br ratio, and 18O- 2H isotope contents point to the mixing of very dilute meteoric waters with present lake brines for the origin of saline inflows. Ancient gypsum in deep sedimentary formations seems to be the only evaporitic mineral recycled in present salars. Saline lakes and subsurface brines are under steady-state regime. The average residence time of conservative components ranges from a few years to some thousands years, which indicates a permanent leakage of the brines through bottom sediments. The infiltrating brines are recycled in the hydrologic system where they mix with dilute meteoric waters. High heat flow is the likely driving force that moves the deep waters in this magmatic arc region. Active Chilean salars cannot be considered as terminal lakes nor, strictly speaking, as closed basin lakes. Almost all incoming salts leave the basin and are transported elsewhere. Moreover, the dissolution of fossil salt crusts in some active salars also carries away important fluxes of components in percolating brines. Evaporative concentration of inflow waters leads to sulfate-rich or calcium-rich, near-neutral brines. Alkaline brines are almost completely lacking. The alkalinity/calcium ratio of inflow

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

Clustering and Bayesian hierarchical modeling for the definition of informative prior distributions in hydrogeology

NASA Astrophysics Data System (ADS)

Cucchi, K.; Kawa, N.; Hesse, F.; Rubin, Y.

2017-12-01

In order to reduce uncertainty in the prediction of subsurface flow and transport processes, practitioners should use all data available. However, classic inverse modeling frameworks typically only make use of information contained in in-situ field measurements to provide estimates of hydrogeological parameters. Such hydrogeological information about an aquifer is difficult and costly to acquire. In this data-scarce context, the transfer of ex-situ information coming from previously investigated sites can be critical for improving predictions by better constraining the estimation procedure. Bayesian inverse modeling provides a coherent framework to represent such ex-situ information by virtue of the prior distribution and combine them with in-situ information from the target site. In this study, we present an innovative data-driven approach for defining such informative priors for hydrogeological parameters at the target site. Our approach consists in two steps, both relying on statistical and machine learning methods. The first step is data selection; it consists in selecting sites similar to the target site. We use clustering methods for selecting similar sites based on observable hydrogeological features. The second step is data assimilation; it consists in assimilating data from the selected similar sites into the informative prior. We use a Bayesian hierarchical model to account for inter-site variability and to allow for the assimilation of multiple types of site-specific data. We present the application and validation of the presented methods on an established database of hydrogeological parameters. Data and methods are implemented in the form of an open-source R-package and therefore facilitate easy use by other practitioners.

SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

NASA Astrophysics Data System (ADS)

Frank, Philipp; Jasche, Jens; Enßlin, Torsten A.

2016-11-01

This work describes the implementation and application of a correlation determination method based on self organizing maps and Bayesian inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters in unstructured cosmological or astrophysical surveys by automatically identifying data clusters in high-dimensional datasets via the self organizing map neural network algorithm. Parameter relations are then revealed by means of a Bayesian inference within respective identified data clusters. Specifically such relations are assumed to be parametrized as a polynomial of unknown order. The Bayesian approach results in a posterior probability distribution function for respective polynomial coefficients. To decide which polynomial order suffices to describe correlation structures in data, we include a method for model selection, the Bayesian information criterion, to the analysis. The performance of the SOMBI algorithm is tested with mock data. As illustration we also provide applications of our method to cosmological data. In particular, we present results of a correlation analysis between galaxy and active galactic nucleus (AGN) properties provided by the SDSS catalog with the cosmic large-scale-structure (LSS). The results indicate that the combined galaxy and LSS dataset indeed is clustered into several sub-samples of data with different average properties (for example different stellar masses or web-type classifications). The majority of data clusters appear to have a similar correlation structure between galaxy properties and the LSS. In particular we revealed a positive and linear dependency between the stellar mass, the absolute magnitude and the color of a galaxy with the corresponding cosmic density field. A remaining subset of data shows inverted correlations, which might be an artifact of non-linear redshift distortions.

Geochemical evolution of brines in the Salar of Uyuni, Bolivia.

USGS Publications Warehouse

Rettig, S.L.; Jones, B.F.; Risacher, F.

1980-01-01

Recent analyses of brines from the Salars of Uyuni and Coipasa have been compared with published data for Lakes Titicaca and Poopo to evaluate solute compositional trends in these remnants of two large Pleistocene lakes once connected by overflow from the N to the S of the Bolivian Altiplano. From Titicaca to Poopo the water shows an increase in Cl and N somewhat greater than the total solutes. Ca and SO4 increase to a lesser extent than total dissolved solids, and carbonate species are relatively constant. Between Poopo and Coipasa proportions of Ca, SO4 and CO3 continue to decrease. At Coipasa and Uyuni, the great salars frequently evaporate to halite saturation. Halite crystallization is accompanied by an increased K, Mg and SO4 in residual brines. - from Authors

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Counterintuitive migration patterns by Atlantic salmon Salmo salar smolts in a large lake.

PubMed

Honkanen, H M; Rodger, J R; Stephen, A; Adams, K; Freeman, J; Adams, C E

2018-06-21

What little is known about the seaward migration of Salmo salar smolt migration through standing waters indicates that it is both slow and results in high mortality rates, compared with riverine migration. This may be partly because smolts in lakes need to swim more actively and require more complex directional cues than they do in rivers. In this telemetry study of smolt migration through Loch Lomond, S. salar smolts made repeated movements in directions away from the outflowing river, which considerably increased migration time. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The biogeography of the atlantic salmon (Salmo salar) gut microbiome.

PubMed

Llewellyn, Martin S; McGinnity, Philip; Dionne, Melanie; Letourneau, Justine; Thonier, Florian; Carvalho, Gary R; Creer, Simon; Derome, Nicolas

2016-05-01

Although understood in many vertebrate systems, the natural diversity of host-associated microbiota has been little studied in teleosts. For migratory fishes, successful exploitation of multiple habitats may affect and be affected by the composition of the intestinal microbiome. We collected 96 Salmo salar from across the Atlantic encompassing both freshwater and marine phases. Dramatic differences between environmental and gut bacterial communities were observed. Furthermore, community composition was not significantly impacted by geography. Instead life-cycle stage strongly defined both the diversity and identity of microbial assemblages in the gut, with evidence for community destabilisation in migratory phases. Mycoplasmataceae phylotypes were abundantly recovered in all life-cycle stages. Patterns of Mycoplasmataceae phylotype recruitment to the intestinal microbial community among sites and life-cycle stages support a dual role for deterministic and stochastic processes in defining the composition of the S. salar gut microbiome.

Microbial diversity of the hypersaline and lithium-rich Salar de Uyuni, Bolivia.

PubMed

Haferburg, Götz; Gröning, Janosch A D; Schmidt, Nadja; Kummer, Nicolai-Alexeji; Erquicia, Juan Carlos; Schlömann, Michael

2017-06-01

Salar de Uyuni, situated in the Southwest of the Bolivian Altiplano, is the largest salt flat on Earth. Brines of this athalassohaline hypersaline environment are rich in lithium and boron. Due to the ever- increasing commodity demand, the industrial exploitation of brines for metal recovery from the world's biggest lithium reservoir is likely to increase substantially in the near future. Studies on the composition of halophilic microbial communities in brines of the salar have not been published yet. Here we report for the first time on the prokaryotic diversity of four brine habitats across the salar. The brine is characterized by salinity values between 132 and 177 PSU, slightly acidic to near-neutral pH and lithium and boron concentrations of up to 2.0 and 1.4g/L, respectively. Community analysis was performed after sequencing the V3-V4 region of the 16S rRNA genes employing the Illumina MiSeq technology. The mothur software package was used for sequence processing and data analysis. Metagenomic analysis revealed the occurrence of an exclusively archaeal community comprising 26 halobacterial genera including only recently identified genera like Halapricum, Halorubellus and Salinarchaeum. Despite the high diversity of the halobacteria-dominated community in sample P3 (Shannon-Weaver index H'=3.12 at 3% OTU cutoff) almost 40% of the Halobacteriaceae-assigned sequences could not be classified on the genus level under stringent filtering conditions. Even if the limited taxonomic resolution of the V3-V4 region for halobacteria is considered, it seems likely to discover new, hitherto undescribed genera of the family halobacteriaceae in this particular habitat of Salar de Uyuni in future. Copyright © 2017 Elsevier GmbH. All rights reserved.

Evidence of Atlantic salmon Salmo salar fry movement between fresh water and a brackish environment.

PubMed

Taal, I; Rohtla, M; Saks, L; Svirgsden, R; Kesler, M; Matetski, L; Vetemaa, M

2017-08-01

This study reports descent of Atlantic salmon Salmo salar fry from their natal streams to brackish waters of the Baltic Sea and their use of this environment as an alternative rearing habitat before ascending back to freshwater streams. To the authors' knowledge, residency in a brackish environment has not previously been demonstrated in S. salar fry. Recruitment success and evolutionary significance of this alternative life-history strategy are presently not known. © 2017 The Fisheries Society of the British Isles.

Are antipredator behaviours of hatchery Salmo salar juveniles similar to wild juveniles?

PubMed

Salvanes, A G V

2017-05-01

This study explores how antipredator behaviour of juvenile Atlantic salmon Salmo salar developed during conventional hatchery rearing of eggs from wild brood stock, compared with the behaviour of wild-caught juveniles from the same population. Juveniles aged 1+ years were tested in two unfamiliar environments; in one S. salar were presented with simulated predator attacks and in the other they were given the opportunity to explore an open-field arena. No difference was found in their spontaneous escape responses or ventilation rate (reflex responses) after simulated predator attacks. Hatchery-reared juveniles were more risk-prone in their behaviours than wild-caught individuals. Hatchery juveniles stayed less time in association with shelter. In the open-field arena, hatchery juveniles were more active than wild juveniles. Hatchery juveniles were also immobile for less time and spent a shorter amount of time than wild juveniles in the fringe of the open-field arena. Salmo salar size had no effect on the observed behaviour. Overall, this study provides empirical evidence that one generation of hatchery rearing does not change reflex responses associated with threats, whereas antipredator behaviour, typically associated with prior experience, was less developed in hatchery-reared than in wild individuals. © 2017 The Fisheries Society of the British Isles.

The biogeography of the atlantic salmon (Salmo salar) gut microbiome

PubMed Central

Llewellyn, Martin S; McGinnity, Philip; Dionne, Melanie; Letourneau, Justine; Thonier, Florian; Carvalho, Gary R; Creer, Simon; Derome, Nicolas

2016-01-01

Although understood in many vertebrate systems, the natural diversity of host-associated microbiota has been little studied in teleosts. For migratory fishes, successful exploitation of multiple habitats may affect and be affected by the composition of the intestinal microbiome. We collected 96 Salmo salar from across the Atlantic encompassing both freshwater and marine phases. Dramatic differences between environmental and gut bacterial communities were observed. Furthermore, community composition was not significantly impacted by geography. Instead life-cycle stage strongly defined both the diversity and identity of microbial assemblages in the gut, with evidence for community destabilisation in migratory phases. Mycoplasmataceae phylotypes were abundantly recovered in all life-cycle stages. Patterns of Mycoplasmataceae phylotype recruitment to the intestinal microbial community among sites and life-cycle stages support a dual role for deterministic and stochastic processes in defining the composition of the S. salar gut microbiome. PMID:26517698

Predictability of multispecies competitive interactions in three populations of Atlantic salmon Salmo salar.

PubMed

Houde, A L S; Wilson, C C; Neff, B D

2015-04-01

Juvenile Atlantic salmon Salmo salar from three allopatric populations (LaHave, Sebago and Saint-Jean) were placed into artificial streams with combinations of four non-native salmonids: brown trout Salmo trutta, rainbow trout Oncorhynchus mykiss, Chinook salmon Oncorhynchus tshawytscha and coho salmon Oncorhynchus kisutch. Non-additive effects, as evidenced by lower performance than predicted from weighted summed two-species competition trials, were detected for S. salar fork length (LF ) and mass, but not for survival, condition factor or riffle use. These data support emerging theory on niche overlap and species richness as factors that can lead to non-additive competition effects. © 2015 The Fisheries Society of the British Isles.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap.

PubMed

Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trivellore E

2016-06-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in "Delta-V," a key crash severity measure.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap

PubMed Central

Zhou, Hanzhi; Elliott, Michael R.; Raghunathan, Trivellore E.

2017-01-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in “Delta-V,” a key crash severity measure. PMID:29226161

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on

Buried landmine detection using multivariate normal clustering

NASA Astrophysics Data System (ADS)

Duston, Brian M.

2001-10-01

A Bayesian classification algorithm is presented for discriminating buried land mines from buried and surface clutter in Ground Penetrating Radar (GPR) signals. This algorithm is based on multivariate normal (MVN) clustering, where feature vectors are used to identify populations (clusters) of mines and clutter objects. The features are extracted from two-dimensional images created from ground penetrating radar scans. MVN clustering is used to determine the number of clusters in the data and to create probability density models for target and clutter populations, producing the MVN clustering classifier (MVNCC). The Bayesian Information Criteria (BIC) is used to evaluate each model to determine the number of clusters in the data. An extension of the MVNCC allows the model to adapt to local clutter distributions by treating each of the MVN cluster components as a Poisson process and adaptively estimating the intensity parameters. The algorithm is developed using data collected by the Mine Hunter/Killer Close-In Detector (MH/K CID) at prepared mine lanes. The Mine Hunter/Killer is a prototype mine detecting and neutralizing vehicle developed for the U.S. Army to clear roads of anti-tank mines.

Influence of the marine feeding area on the muscle and egg fatty-acid composition of Atlantic salmon Salmo salar spawners estimated from the scale stable isotopes.

PubMed

Torniainen, J; Kainz, M J; Jones, R I; Keinänen, M; Vuorinen, P J; Kiljunen, M

2017-05-01

Fatty acids in muscle tissue and eggs of female Atlantic salmon Salmo salar spawners were analysed to evaluate the dietary quality of their final feeding areas in the Baltic Sea. The final likely feeding area was identified by comparing stable carbon and nitrogen isotope composition of the outermost growth region (final annulus) of scales of returned S. salar with that of reference S. salar caught from different feeding areas. Some overlap of stable-isotope reference values among the three areas, in addition to prespawning fasting, decreased the ability of muscle tri-acylglycerols to discriminate the final likely feeding area and the area's dietary quality. Among three long-chained polyunsaturated fatty acids, docosahexaenoic acid (DHA; 22:6n-3), eicosapentaenoic acid (EPA; 20:5n-3) and arachidonic acid (ARA; 20:4n-6), the proportions of ARA in total lipids of spawning S. salar muscle and eggs showed a significant negative correlation with increasing probability of S. salar having returned from the Baltic Sea main basin (i.e. the Baltic Sea proper). The results suggest that ARA in muscle and eggs is the best dietary indicator for dietary characteristics of final marine feeding area dietary characteristics among S. salar in the Baltic Sea. © 2017 The Fisheries Society of the British Isles.

Effects of rearing density and dietary fat content on burst-swim performance and oxygen transport capacity in juvenile Atlantic salmon Salmo salar.

PubMed

Hammenstig, D; Sandblom, E; Axelsson, M; Johnsson, J I

2014-10-01

The effects of hatchery rearing density (conventional or one third of conventional density) and feeding regime (high or reduced dietary fat levels) on burst-swim performance and oxygen transport capacity were studied in hatchery-reared Atlantic salmon Salmo salar, using wild fish as a reference group. There was no effect of rearing density or food regime on swimming performance in parr and smolts. The maximum swimming speed of wild parr was significantly higher than that of hatchery-reared conspecifics, while no such difference remained at the smolt stage. In smolts, relative ventricle mass was higher in wild S. salar compared with hatchery-reared fish. Moreover, wild S. salar had lower maximum oxygen consumption following a burst-swim challenge than hatchery fish. There were no effects of hatchery treatment on maximum oxygen consumption or relative ventricle mass. Haemoglobin and haematocrit levels, however, were lower in low-density fish than in fish reared at conventional density. Furthermore, dorsal-fin damage, an indicator of aggression, was similar in low-density reared and wild fish and lower than in S. salar reared at conventional density. Together, these results suggest that reduced rearing density is more important than reduced dietary fat levels in producing an S. salar smolt suitable for supplementary release. © 2014 The Fisheries Society of the British Isles.

Prolactin-releasing peptide is a potent mediator of the innate immune response in leukocytes from Salmo salar.

PubMed

Romero, Alex; Manríquez, René; Alvarez, Claudio; Gajardo, Cristina; Vásquez, Jorge; Kausel, Gudrun; Monrás, Mónica; Olavarría, Víctor H; Yáñez, Alejandro; Enríquez, Ricardo; Figueroa, Jaime

2012-06-30

Prolactin (PRL)-releasing peptide (PrRP) is a strong candidate stimulator of pituitary PRL transcription and secretion in teleosts. However, the role in control of extrapituitary PRL expression or its effects on innate immunity are unclear even in mammals. To study the possible presence of PrRP in peripheral organs, PrRP expression patterns and their effect on innate immunity were characterised in SHK-1 cells and head kidney (HK) leukocytes purified from the salmonid, Salmo salar. We detected immunoreactive cells in leukocytes from blood and HK of S. salar and found that PrRP mRNA was abundantly expressed in these cells. We have recently reported that physiological concentrations of native PRL, downstream of neuropeptide PrRP were able to induce expression of pro-inflammatory cytokines and the production of reactive oxygen species (ROS) in HK leukocytes and macrophages from S. salar and Sparus aurata. It is of interest to note that in this work we have revealed that synthetic PrRP was able to induce expression of pro-inflammatory cytokines (interleukins) IL-1β, IL-6, IL-8, IL-12 and PRL. We also show here that PrRP increased both (ROS) production and phagocytosis. Taken together, our results demonstrate for the first time that PrRP may be a local modulator of innate immune responses in leukocytes from S. salar. Copyright © 2012 Elsevier B.V. All rights reserved.

Effects of training on functional variables of muscles in reared Atlantic salmon Salmo salar smolts: connection to downstream migration pattern.

PubMed

Anttila, K; Jokikokko, E; Erkinaro, J; Järvilehto, M; Mänttäri, S

2011-02-01

The relative amount of muscle contraction regulating dihydropyridine and ryanodine receptors in the swimming muscles of trained reared Atlantic salmon Salmo salar smolts was compared with those of untrained and wild smolts. After an optimized 2 week training period, i.e. swimming with a velocity of 1·5 body lengths per second for 6 h per day, the level of both receptors was significantly higher in the muscles of trained S. salar than in the untrained ones before they were released into the natural environment. This difference persisted after downstream migration in the river. The highest level of receptors was observed in wild S. salar. Swimming performance was also higher in trained fish compared to untrained ones. Furthermore, swimming performance was positively associated with the level of receptors in both red and white muscle types. Downstream migration after release into the wild was significantly slower in trained smolts than in untrained fish. This indicates that trained smolts were most probably swimming harder against the current in the river than untrained smolts. The possible advantages for a slower migration in the river are discussed. This study shows that the prerequisites for effective contraction of the swimming muscles are better met in trained S. salar compared to untrained fish, and the muscles of trained smolts more closely resemble those of wild smolts. The results also imply that the capacity of untrained, reared smolts to swim against the current is not equal to that of their trained or wild counterparts which affects the downstream migration pattern of S. salar smolts. © 2011 The Authors. Journal of Fish Biology © 2011 The Fisheries Society of the British Isles.

Plasticity in response to feed availability: Does feeding regime influence the relative growth performance of domesticated, wild and hybrid Atlantic salmon Salmo salar parr?

PubMed

Harvey, A C; Solberg, M F; Glover, K A; Taylor, M I; Creer, S; Carvalho, G R

2016-09-01

Growth of farmed, wild and F1 hybrid Atlantic salmon parr Salmo salar was investigated under three contrasting feeding regimes in order to understand how varying levels of food availability affects relative growth. Treatments consisted of standard hatchery feeding (ad libitum), access to feed for 4 h every day, and access to feed for 24 h on three alternate days weekly. Mortality was low in all treatments, and food availability had no effect on survival of all groups. The offspring of farmed S. salar significantly outgrew the wild S. salar, while hybrids displayed intermediate growth. Furthermore, the relative growth differences between the farmed and wild S. salar did not change across feeding treatments, indicating a similar plasticity in response to feed availability. Although undertaken in a hatchery setting, these results suggest that food availability may not be the sole driver behind the observed reduced growth differences found between farmed and wild fishes under natural conditions. © 2016 The Fisheries Society of the British Isles.

Density-dependent effects of Caligus rogercresseyi infestation on the immune responses of Salmo salar.

PubMed

Boltaña, Sebastian; Sanchez, Marcos; Valenzuela, Valentina; Gallardo-Escárate, Cristian

2016-12-01

Sea lice infestations are a particular concern in the salmonid aquaculture industry due to damaging effects on fish growth, disease/infection susceptibility, and survival. Despite the impacts of sea lice parasitism, few studies have determined corresponding physiological thresholds, or the quantity of sea lice that can trigger measurable effects in the host immune response. The present study evaluated the mRNA expressions of immune-related genes in Salmo salar (Atlantic salmon) under infestation challenges with contrasting loads of the sea louse Caligus rogercresseyi. Specifically, two groups of S. salar were infected with either 35 (i.e. low parasitic load) or 100 (i.e. high parasitic load) copepodids per fish. At 14 days post-infestation, the mRNA levels of immune-related genes (e.g. related to oxidative stress, pro- and inflammatory responses, and the adaptive T H 1/T H 2 pathways) were assessed through RT-qPCR. Significant differences were found in relation to parasitic load, suggesting density-dependent effects that activated the S. salar immune system. Higher parasitic load promoted strong inflammatory and oxidative stress responses that were correlated with the T H 1 immune response. This study highlights the molecular signatures for distinct parasitic loads, providing new perspectives towards fully understanding parasite-host interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stress response of Salmo salar (Linnaeus 1758) when heavily infested by Caligus rogercresseyi (Boxshall & Bravo 2000) copepodids.

PubMed

González, Margarita P; Vargas-Chacoff, Luis; Marín, Sandra L

2016-02-01

The year-round presence of ovigerous females of the parasite Caligus rogercresseyi in the fish farms of southern Chile results in a continuous source of the copepodid (infestive) stage of this louse. The short generation time in spring-summer could lead to high abundances of this copepodid, potentially leading to high infestation levels for fish. Knowing how heavy lice infestations affect Salmo salar can help determine how to time antiparasitic treatments so as to both minimize the treatment impact and reduce lice infestation levels for fish. This study aimed to describe the effects of high infestations of the copepodid stage of C. rogercresseyi on the physiology of S. salar. Two groups of S. salar were used: an infested group (75 copepodids per fish) and a control group (not infested). Sixty-five days after the first infestation, the infested fish group was re-infested at an infestation pressure of 200 copepodids per fish. Sampling was done prior to and following the second infestation, at 56 and 67 days (the latter 2 days following the second infestation). Several physiological variables were measured: cortisol (primary stress response) and glucose, proteins, amino acids, triglycerides, lactate, osmolality levels, and number and diameter of skin mucous cells (secondary stress responses). The plasma cortisol, glucose, and triglyceride levels were altered in the heavily infested fish, as was the diameter of skin mucous cells. These results suggest that heavy infestations of C. rogercresseyi lead to an acute stress response, metabolic reorganization, and increased mucus production in S. salar under heavy infestation conditions.

Growth of Atlantic salmon, Salmo salar fed diets containing barley protein concentrate

USDA-ARS?s Scientific Manuscript database

Atlantic salmon (Salmo salar) is an important cultured carnivorous species that in the past has not tolerated high levels of most plant protein feed ingredients in the diet. In order to increase efficiency, sustainability and production to meet global demand, new sources of protein must be incorpo...

Kinetics of arsenite removal by halobacteria from a highland Andean Chilean Salar

PubMed Central

2013-01-01

Background The purpose of this study was to identify arsenite-oxidizing halobacteria in samples obtained from Salar de Punta Negra, II Region of Chile. Seven bacterial isolates, numbered as isolates I to VII, grown in a culture medium with 100 ppm as NaAsO2 (As (III)) were tested. Bacterial growth kinetics and the percent of arsenite removal (PAR) were performed simultaneously with the detection of an arsenite oxidase enzyme through Dot Blot analysis. Results An arsenite oxidase enzyme was detected in all isolates, expressed constitutively after 10 generations grown in the absence of As (III). Bacterial growth kinetics and corresponding PAR values showed significant fluctuations over time. PARs close to 100% were shown by isolates V, VI, and VII, at different times of the bacterial growth phase; while isolate II showed PAR values around 40%, remaining constant over time. Conclusion Halobacteria from Salar de Punta Negra showed promising properties as arsenite removers under control conditions, incubation time being a critical parameter. PMID:23547876

Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.

2010-01-01

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

Effects of feed quality and quantity on growth, early maturation and smolt development in hatchery-reared landlocked Atlantic salmon Salmo salar.

PubMed

Norrgård, J R; Bergman, E; Greenberg, L A; Schmitz, M

2014-10-01

The effects of feed quality and quantity on growth, early male parr maturation and development of smolt characteristics were studied in hatchery-reared landlocked Atlantic salmon Salmo salar. The fish were subjected to two levels of feed rations and two levels of lipid content from first feeding until release in May of their second year. Salmo salar fed high rations, regardless of lipid content, grew the most and those fed low lipid feed with low rations grew the least. In addition, fish fed low lipid feed had lower body lipid levels than fish fed high lipid feed. Salmo salar from all treatments showed some reduction in condition factor (K) and lipid levels during their second spring. Smolt status was evaluated using both physiological and morphological variables. These results, based on gill Na(+) , K(+) -ATPase (NKA) enzyme activity, saltwater tolerance challenges and visual assessments, were consistent with each other, showing that S. salar from all treatments, except the treatment in which the fish were fed low rations with low lipid content, exhibited characteristics associated with smolting at 2 years of age. Sexually mature male parr from the high ration, high lipid content treatment were also subjected to saltwater challenge tests, and were found to be unable to regulate plasma sodium levels. The proportion of sexually mature male parr was reduced when the fish were fed low feed rations, but was not affected by the lipid content of the feed. Salmo salar fed low rations with low lipid content exhibited the highest degree of severe fin erosion. © 2014 The Fisheries Society of the British Isles.

Evidence for long-term change in length, mass and migration phenology of anadromous spawners in French Atlantic salmon Salmo salar.

PubMed

Bal, G; Montorio, L; Rivot, E; Prévost, E; Baglinière, J-L; Nevoux, M

2017-06-01

This study provides new data on Atlantic salmon Salmo salar life-history traits across France. Using a long-term recreational angling database (1987-2013) covering 34 rivers in three regions (genetic units), a decline in individual length, mass and a delayed adult return to French rivers was reported. Temporal similarities in trait variations between regions may be attributed to common change in environmental conditions at sea. The relative rate of change in phenotypic traits was more pronounced in early maturing fish [1 sea-winter (1SW) fish] than in late maturing fish (2SW fish). Such contrasted response within populations highlights the need to account for the diversity in life histories when exploring mechanisms of phenotypic change in S. salar. Such detailed life-history data on returning S. salar have not previously been reported from France. This study on French populations also contributes to reducing the gap in knowledge by providing further empirical evidence of a global pattern in S. salar across its distribution range. Results are consistent with the hypothesis that the observed changes in life-history traits are primarily associated with environmental changes in the North Atlantic Ocean. They also emphasize the presence of less important, but still significant contrasts between region and life history. © 2017 The Fisheries Society of the British Isles.

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Divergent trends in life-history traits between Atlantic salmon Salmo salar of wild and hatchery origin in the Baltic Sea.

PubMed

Vainikka, A; Kallio-Nyberg, I; Heino, M; Koljonen, M-L

2010-02-01

Four Atlantic salmon Salmo salar stocks in the Baltic Sea, varying in their breeding history, were studied for changes in life-history traits over the years 1972-1995. Total length (L(T)) at age of captured (L(TC)) fish had increased throughout the study period, partly due to increased temperature and increased L(T) at release, (L(TR)) but also due to remaining cohort effects that could represent unaccounted environmental or genetic change. Simultaneously, maturation probabilities controlled for water temperature, L(TC) and L(TR) had increased in all stocks. The least change was observed in the River Tornionjoki S. salar that was subject only to supportive stockings originating from wild parents. These results suggest a long-term divergence between semi-natural and broodstock-based S. salar stocks. Increased L(T) at age explained advanced maturation only marginally, and it remains an open question to what extent the generally increased probabilities to mature at early age reflected underlying genetic changes.

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

PubMed

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

The complete genome sequence of CrRV-Ch01, a new member of the family Rhabdoviridae in the parasitic copepod Caligus rogercresseyi present on farmed Atlantic salmon (Salmo salar) in Chile.

PubMed

Økland, Arnfinn Lodden; Skoge, Renate Hvidsten; Nylund, Are

2018-06-01

We have determined the complete genome sequence of a new rhabdovirus, tentatively named Caligus rogercresseyi rhabdovirus Ch01 (CrRV-Ch01), which was found in the parasite Caligus rogercresseyi, present on farmed Atlantic salmon (Salmo salar) in Chile. The genome encodes the five canonical rhabdovirus proteins in addition to an unknown protein, in the order N-P-M-U (unknown)-G-L. Phylogenetic analysis showed that the virus clusters with two rhabdoviruses (Lepeophtheirus salmonis rhabdovirus No9 and Lepeophtheirus salmonis rhabdovirus No127) obtained from another parasitic caligid, Lepeophtheirus salmonis, present on farmed Atlantic salmon on the west coast of Norway.

Does size matter? A test of size-specific mortality in Atlantic salmon Salmo salar smolts tagged with acoustic transmitters.

PubMed

Newton, M; Barry, J; Dodd, J A; Lucas, M C; Boylan, P; Adams, C E

2016-09-01

Mortality rates of wild Atlantic salmon Salmo salar smolts implanted with acoustic transmitters were assessed to determine if mortality was size dependent. The routinely accepted, but widely debated, '2% transmitter mass: body mass' rule in biotelemetry was tested by extending the transmitter burden up to 12·7% of body mass in small [mean fork length (LF ) 138·3 mm, range 115-168 mm] downstream migrating S. salar smolts. Over the short timescale of emigration (range 11·9-44·5 days) through the lower river and estuary, mortality was not related to S. salar size, nor was a relationship found between mortality probability and transmitter mass: body mass or transmitter length: LF ratios. This study provides further evidence that smolt migration studies can deviate from the '2% rule' of thumb, to more appropriate study-specific measures, which enables the use of fishes representative of the body size in natural populations without undue effects. © 2016 The Fisheries Society of the British Isles.

MC 2: A Deeper Look at ZwCl 2341.1+0000 with Bayesian Galaxy Clustering and Weak Lensing Analyses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benson, B.; Wittman, D. M.; Golovich, N.

ZwCl 2341.1+0000, a merging galaxy cluster with disturbed X-ray morphology and widely separated (~3 Mpc) double radio relics, was thought to be an extremely massive (10 - 30 X 10 14M⊙) and complex system with little known about its merger history. We present JVLA 2-4 GHz observations of the cluster, along with new spectroscopy from our Keck/DEIMOS survey, and apply Gaussian Mixture Modeling to the three-dimensional distribution of 227 con rmed cluster galaxies. After adopting the Bayesian Information Criterion to avoid over tting, which we discover can bias total dynamical mass estimates high, we nd that a three-substructure model withmore » a total dynamical mass estimate of 9:39 ± 0:81 X 10 14M⊙ is favored. We also present deep Subaru imaging and perform the rst weak lensing analysis on this system, obtaining a weak lensing mass estimate of 5:57±2:47X10 14M⊙. This is a more robust estimate because it does not depend on the dynamical state of the system, which is disturbed due to the merger. Our results indicate that ZwCl 2341.1+0000 is a multiple merger system comprised of at least three substructures, with the main merger that produced the radio relics occurring near to the plane of the sky, and a younger merger in the North occurring closer to the line of sight. Dynamical modeling of the main merger reproduces observed quantities (relic positions and polarizations, subcluster separation and radial velocity difference), if the merger axis angle of ~10 +34 -6 degrees and the collision speed at pericenter is ~1900 +300 -200 km/s.« less

MC 2: A Deeper Look at ZwCl 2341.1+0000 with Bayesian Galaxy Clustering and Weak Lensing Analyses

DOE PAGES

Benson, B.; Wittman, D. M.; Golovich, N.; ...

2017-05-16

ZwCl 2341.1+0000, a merging galaxy cluster with disturbed X-ray morphology and widely separated (~3 Mpc) double radio relics, was thought to be an extremely massive (10 - 30 X 10 14M⊙) and complex system with little known about its merger history. We present JVLA 2-4 GHz observations of the cluster, along with new spectroscopy from our Keck/DEIMOS survey, and apply Gaussian Mixture Modeling to the three-dimensional distribution of 227 con rmed cluster galaxies. After adopting the Bayesian Information Criterion to avoid over tting, which we discover can bias total dynamical mass estimates high, we nd that a three-substructure model withmore » a total dynamical mass estimate of 9:39 ± 0:81 X 10 14M⊙ is favored. We also present deep Subaru imaging and perform the rst weak lensing analysis on this system, obtaining a weak lensing mass estimate of 5:57±2:47X10 14M⊙. This is a more robust estimate because it does not depend on the dynamical state of the system, which is disturbed due to the merger. Our results indicate that ZwCl 2341.1+0000 is a multiple merger system comprised of at least three substructures, with the main merger that produced the radio relics occurring near to the plane of the sky, and a younger merger in the North occurring closer to the line of sight. Dynamical modeling of the main merger reproduces observed quantities (relic positions and polarizations, subcluster separation and radial velocity difference), if the merger axis angle of ~10 +34 -6 degrees and the collision speed at pericenter is ~1900 +300 -200 km/s.« less

The effect of catch-and-release angling at high water temperatures on behaviour and survival of Atlantic salmon Salmo salar during spawning migration.

PubMed

Havn, T B; Uglem, I; Solem, Ø; Cooke, S J; Whoriskey, F G; Thorstad, E B

2015-08-01

In this study, behaviour and survival following catch-and-release (C&R) angling was investigated in wild Atlantic salmon Salmo salar (n = 75) angled on sport fishing gear in the River Otra in southern Norway at water temperatures of 16.3-21.1 °C. Salmo salar were tagged externally with radio transmitters and immediately released back into the river to simulate a realistic C&R situation. The majority of S. salar (91%) survived C&R. Most S. salar that were present in the River Otra during the spawning period 3-4 months later were located at known spawning grounds. Downstream movements (median furthest position: 0.5 km, range: 0.1-11.0 km) during the first 4 days after release were recorded for 72% of S. salar, presumably stress-induced fallback associated with C&R. Individuals that fell back spent a median of 15 days before commencing their first upstream movement after release, and 34 days before they returned to or were located above their release site. Mortality appeared to be somewhat elevated at the higher end of the temperature range (14% at 18-21 °C), although sample sizes were low. In conclusion, C&R at water temperatures up to 18 °C had small behavioural consequences and was associated with low mortality (7%). Nevertheless, low levels of mortality occur due to C&R angling and these losses should be accounted for by management authorities in rivers where C&R is practised. Refinement of best practices for C&R may help to reduce mortality, particularly at warmer temperatures. © 2015 The Fisheries Society of the British Isles.

Assessment of interbreeding and introgression of farm genes into a small Scottish Atlantic salmon Salmo salar stock: ad hoc samples - ad hoc results?

PubMed

Verspoor, E; Knox, D; Marshall, S

2016-12-01

An eclectic set of tissues and existing data, including purposely collected samples, spanning 1997-2006, was used in an ad hoc assessment of hybridization and introgression of farmed wild Atlantic salmon Salmo salar in the small Loch na Thull (LnT) catchment in north-west Scotland. The catchment is in an area of marine farm production and contains freshwater smolt rearing cages. The LnT S. salar stock was found to be genetically distinctive from stocks in neighbouring rivers and, despite regular reports of feral farm S. salar, there was no evidence of physical or genetic mixing. This cannot be completely ruled out, however, and low level mixing with other local wild stocks has been suggested. The LnT population appeared underpinned by relatively smaller effective number of breeders (N eb ) and showed relatively low levels of genetic diversity, consistent with a small effective population size. Small sample sizes, an incomplete farm baseline and the use of non-diagnostic molecular markers, constrain the power of the analysis but the findings strongly support the LnT catchment having a genetically distinct wild S. salar population little affected by interbreeding with feral farm escapes. © 2016 The Fisheries Society of the British Isles.

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

PubMed Central

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

OBJECTIVE: To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. METHODS: Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. FINDINGS: A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. CONCLUSION: Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic. PMID:12973640

A primary phosphorus-deficient skeletal phenotype in juvenile Atlantic salmon Salmo salar: the uncoupling of bone formation and mineralization.

PubMed

Witten, P E; Owen, M A G; Fontanillas, R; Soenens, M; McGurk, C; Obach, A

2016-02-01

To understand the effect of low dietary phosphorus (P) intake on the vertebral column of Atlantic salmon Salmo salar, a primary P deficiency was induced in post-smolts. The dietary P provision was reduced by 50% for a period of 10 weeks under controlled conditions. The animal's skeleton was subsequently analysed by radiology, histological examination, histochemical detection of minerals in bones and scales and chemical mineral analysis. This is the first account of how a primary P deficiency affects the skeleton in S. salar at the cellular and at the micro-anatomical level. Animals that received the P-deficient diet displayed known signs of P deficiency including reduced growth and soft, pliable opercula. Bone and scale mineral content decreased by c. 50%. On radiographs, vertebral bodies appear small, undersized and with enlarged intervertebral spaces. Contrary to the X-ray-based diagnosis, the histological examination revealed that vertebral bodies had a regular size and regular internal bone structures; intervertebral spaces were not enlarged. Bone matrix formation was continuous and uninterrupted, albeit without traces of mineralization. Likewise, scale growth continues with regular annuli formation, but new scale matrix remains without minerals. The 10 week long experiment generated a homogeneous osteomalacia of vertebral bodies without apparent induction of skeletal malformations. The experiment shows that bone formation and bone mineralization are, to a large degree, independent processes in the fish examined. Therefore, a deficit in mineralization must not be the only cause of the alterations of the vertebral bone structure observed in farmed S. salar. It is discussed how the observed uncoupling of bone formation and mineralization helps to better diagnose, understand and prevent P deficiency-related malformations in farmed S. salar. © 2015 The Authors.Journal of Fish Biology published by John Wiley & Sons Ltd on behalf of The Fisheries Society of the

Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective

PubMed Central

Qian, Xiaoning; Dougherty, Edward R.

2017-01-01

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models. PMID:28824268

Identification and expressional analysis of NLRC5 inflammasome gene in smolting Atlantic salmon (Salmo salar).

PubMed

Pontigo, Juan Pablo; Agüero, María José; Sánchez, Patricio; Oyarzún, Ricardo; Vargas-Lagos, Carolina; Mancilla, Jorge; Kossmann, Hans; Morera, Francisco J; Yáñez, Alejandro J; Vargas-Chacoff, Luis

2016-11-01

The NOD-like receptors (NLRs) were recently identified as an intracellular pathogen recognition receptor family in vertebrates. While the immune system participation of NLRs has been characterized and analyzed in various mammalian models, few studies have considered NLRs in teleost species. Therefore, this study analyzed the Atlantic salmon (Salmo salar) NLRC5. Structurally, Atlantic salmon NLRC5 presented leucine-rich repeat subfamily genes. Phylogenetically, NLRC5 was moderately conserved between S. salar and other species. Real-time quantitative PCR revealed NLRC5 expression in almost all analyzed organs, with greatest expressions in the head kidney, spleen, and hindgut. Furthermore, NLRC5 gene expression decreased during smolt stage. These data suggest that NLRC5 participates in the Atlantic salmon immune response and is regulated, at least partly, by the smoltification process, suggesting that there is a depression of immune system from parr at smolt stage. This is the first report on the NLRC5 gene in salmonid smolts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Interaction between migration behaviour and estuarine mortality in cultivated Atlantic salmon Salmo salar smolts.

PubMed

Vollset, K W; Mahlum, S; Davidsen, J G; Skoglund, H; Barlaup, B T

2016-10-01

Migration behaviour and estuarine mortality of cultivated Atlantic salmon Salmo salar smolts in a 16 km long estuary were studied using two methods: (1) acoustic telemetry and (2) group tagging in combination with trap nets. Progression rates of surviving individuals through the estuary were relatively slow using both methods [0·38 L T (total length) s -1 v. 0·25 L T  s -1 ]. In 2012, the progression rate was slow from the river to the estuary (0·55 L T  s -1 ) and the first part of the estuary (0·31 L T  s -1 ), but increased thereafter (1·45-2·21 L T  s -1 ). In 2013, the progression rate was fast from the river to the estuary (4·31 L T  s -1 ) but was slower thereafter (0·18-0·91 L T  s -1 ). Survival to the fjord was higher in 2012 (47%) compared to 2013 (6%). Fast moving individuals were more likely to migrate successfully through the estuary compared to slower moving individuals. Adult recapture of coded-wire-tagged S. salar was generally low (0·00-0·04%). Mortality hot spots were related to topographically distinct areas such as the river outlet (in 2012) or the sill separating the estuary and the fjord (in 2013). At the sill, an aggregation of cod Gadus morhua predating on cultivated smolts was identified. The results indicate that slow progression rates through the estuary decreases the likelihood of smolts being detected outside the estuary. The highly stochastic and site-specific mortality patterns observed in this study highlight the complexity in extrapolating mortality patterns of single release groups to the entire smolt run of wild S. salar. © 2016 The Fisheries Society of the British Isles.

Bayesian Nonparametric Inference – Why and How

PubMed Central

Müller, Peter; Mitra, Riten

2013-01-01

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP. PMID:24368932

Bayesian performance metrics and small system integration in recent homeland security and defense applications

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Kostrzewski, Andrew; Patton, Edward; Pradhan, Ranjit; Shih, Min-Yi; Walter, Kevin; Savant, Gajendra; Shie, Rick; Forrester, Thomas

2010-04-01

In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland Security and defense systems called binary sensors, including both (internal) system performance and (external) CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters ("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.

Seasonal shift in the effects of predators on juvenile Atlantic salmon (Salmo salar) energetics

Treesearch

Darren M. Ward; Keith H. Nislow; Carol L. Folt; James Grant

2011-01-01

Predator effects on prey populations are determined by the number of prey consumed and effects on the traits of surviving prey. Yet the effects of predators on prey traits are rarely evaluated in field studies. We measured the effects of predators on energetic traits (consumption and growth rates) of juvenile Atlantic salmon (Salmo salar) in a...

Passing a seawater challenge test is not indicative of hatchery-reared Atlantic salmon Salmo salar smolts performing as well at sea as their naturally produced conspecifics.

PubMed

Jensen, A J; Berg, M; Bremset, G; Finstad, B; Hvidsten, N A; Jensås, J G; Johnsen, B O; Lund, E

2016-06-01

Despite satisfactory reactions to seawater challenge tests indicative of appropriate physiological state, hatchery-reared Atlantic salmon Salmo salar smolts stocked in the Eira River in Norway between 2001 and 2011 performed less well at sea in terms of growth, age at maturity and survival than smolts of natural origin. The mean rates of return to the river for hatchery-reared and naturally produced S. salar were 0·98 and 2·35%. In the Eira River, c. 50 000 hatchery-reared S. salar smolts of local origin were stocked annually to compensate for reduced natural smolt production following regulation for hydroelectric purposes, while a mean of 17 262 smolts were produced naturally in the river. This study demonstrates that, although captive S. salar perform well in seawater challenge tests, hatchery-reared smolts are not necessarily as adaptable to marine life as their naturally produced counterparts. These findings suggest that production of hatchery-reared smolts more similar to naturally produced individuals in morphology, physiology and behaviour will be necessary to improve success of hatchery releases. Where possible, supplementary or alternative measures, including habitat restoration, could be implemented to ensure the long-term viability of wild stocks. © 2016 The Fisheries Society of the British Isles.

Bayesian network meta-analysis for cluster randomized trials with binary outcomes.

PubMed

Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard

2017-06-01

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Bayesian data analysis for newcomers.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

Epithelial Label-Retaining Cells Are Absent during Tooth Cycling in Salmo salar and Polypterus senegalus.

PubMed

Vandenplas, Sam; Willems, Maxime; Witten, P Eckhard; Hansen, Tom; Fjelldal, Per Gunnar; Huysseune, Ann

2016-01-01

The Atlantic salmon (Salmo salar) and African bichir (Polypterus senegalus) are both actinopterygian fish species that continuously replace their teeth without the involvement of a successional dental lamina. Instead, they share the presence of a middle dental epithelium: an epithelial tier enclosed by inner and outer dental epithelium. It has been hypothesized that this tier could functionally substitute for a successional dental lamina and might be a potential niche to house epithelial stem cells involved in tooth cycling. Therefore, in this study we performed a BrdU pulse chase experiment on both species to (1) determine the localization and extent of proliferating cells in the dental epithelial layers, (2) describe cell dynamics and (3) investigate if label-retaining cells are present, suggestive for the putative presence of stem cells. Cells proliferate in the middle dental epithelium, outer dental epithelium and cervical loop at the lingual side of the dental organ to form a new tooth germ. Using long chase times, both in S. salar (eight weeks) and P. senegalus (eight weeks and twelve weeks), we could not reveal the presence of label-retaining cells in the dental organ. Immunostaining of P. senegalus dental organs for the transcription factor Sox2, often used as a stem cell marker, labelled cells in the zone of outer dental epithelium which grades into the oral epithelium (ODE transition zone) and the inner dental epithelium of a successor only. The location of Sox2 distribution does not provide evidence for epithelial stem cells in the dental organ and, more specifically, in the middle dental epithelium. Comparison of S. salar and P. senegalus reveals shared traits in tooth cycling and thus advances our understanding of the developmental mechanism that ensures lifelong replacement.

Epithelial Label-Retaining Cells Are Absent during Tooth Cycling in Salmo salar and Polypterus senegalus

PubMed Central

Vandenplas, Sam; Willems, Maxime; Witten, P. Eckhard; Hansen, Tom; Fjelldal, Per Gunnar; Huysseune, Ann

2016-01-01

The Atlantic salmon (Salmo salar) and African bichir (Polypterus senegalus) are both actinopterygian fish species that continuously replace their teeth without the involvement of a successional dental lamina. Instead, they share the presence of a middle dental epithelium: an epithelial tier enclosed by inner and outer dental epithelium. It has been hypothesized that this tier could functionally substitute for a successional dental lamina and might be a potential niche to house epithelial stem cells involved in tooth cycling. Therefore, in this study we performed a BrdU pulse chase experiment on both species to (1) determine the localization and extent of proliferating cells in the dental epithelial layers, (2) describe cell dynamics and (3) investigate if label-retaining cells are present, suggestive for the putative presence of stem cells. Cells proliferate in the middle dental epithelium, outer dental epithelium and cervical loop at the lingual side of the dental organ to form a new tooth germ. Using long chase times, both in S. salar (eight weeks) and P. senegalus (eight weeks and twelve weeks), we could not reveal the presence of label-retaining cells in the dental organ. Immunostaining of P. senegalus dental organs for the transcription factor Sox2, often used as a stem cell marker, labelled cells in the zone of outer dental epithelium which grades into the oral epithelium (ODE transition zone) and the inner dental epithelium of a successor only. The location of Sox2 distribution does not provide evidence for epithelial stem cells in the dental organ and, more specifically, in the middle dental epithelium. Comparison of S. salar and P. senegalus reveals shared traits in tooth cycling and thus advances our understanding of the developmental mechanism that ensures lifelong replacement. PMID:27049953

Bayesian Probability Theory

NASA Astrophysics Data System (ADS)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

Microbial community composition and trophic role along a marked salinity gradient in Laguna Puilar, Salar de Atacama, Chile.

PubMed

Dorador, Cristina; Fink, Patrick; Hengst, Martha; Icaza, Gonzalo; Villalobos, Alvaro S; Vejar, Drina; Meneses, Daniela; Zadjelovic, Vinko; Burmann, Lisa; Moelzner, Jana; Harrod, Chris

2018-05-09

The geological, hydrological and microbiological features of the Salar de Atacama, the most extensive evaporitic sedimentary basin in the Atacama Desert of northern Chile, have been extensively studied. In contrast, relatively little attention has been paid to the composition and roles of microbial communities in hypersaline lakes which are a unique feature in the Salar. In the present study biochemical, chemical and molecular biological tools were used to determine the composition and roles of microbial communities in water, microbial mats and sediments along a marked salinity gradient in Laguna Puilar which is located in the "Los Flamencos" National Reserve. The bacterial communities at the sampling sites were dominated by members of the phyla Bacteroidetes, Chloroflexi, Cyanobacteria and Proteobacteria. Stable isotope and fatty acid analyses revealed marked variability in the composition of microbial mats at different sampling sites both horizontally (at different sites) and vertically (in the different layers). The Laguna Puilar was shown to be a microbially dominated ecosystem in which more than 60% of the fatty acids at particular sites are of bacterial origin. Our pioneering studies also suggest that the energy budgets of avian consumers (three flamingo species) and dominant invertebrates (amphipods and gastropods) use minerals as a source of energy and nutrients. Overall, the results of this study support the view that the Salar de Atacama is a heterogeneous and fragile ecosystem where small changes in environmental conditions may alter the balance of microbial communities with possible consequences at different trophic levels.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

NASA Astrophysics Data System (ADS)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Estimating the effective number of breeders from single parr samples for conservation monitoring of wild populations of Atlantic salmon Salmo salar.

PubMed

Bacles, C F E; Bouchard, C; Lange, F; Manicki, A; Tentelier, C; Lepais, O

2018-03-01

This study assesses whether the effective number of breeders (N b ) can be estimated using a time and cost-effective protocol using genetic sibship reconstruction from a single sample of young-of-the-year (YOY) for the purposes of Atlantic salmon Salmo salar population monitoring. N b was estimated for 10 consecutive reproductive seasons for S. salar in the River Nivelle, a small population located at the rear-edge of the species distribution area in France, chronically under its conservation limit and subjected to anthropogenic and environmental changes. Subsampling of real and simulated data showed that accurate estimates of N b can be obtained from YOY genotypes, collected at moderate random sampling intensity, achievable using routine juvenile electrofishing protocols. Spatial bias and time elapsed since spawning were found to affect estimates, which must be accounted for in sampling designs. N b estimated in autumn for S. salar in the River Nivelle was low and variable across years from 23 (95% C.I. 14-41) to 75 (53-101) and was not statistically correlated with the estimated number of returning adults, but it was positively correlated with the estimated number of YOY at age 9 months. N b was found to be lower for intermediate levels of redd aggregation, suggesting that the strength of the competition between males to access females affects reproductive success variance depending on redd spatial configuration. Thus, environmental factors such as habitat availability and quality for spawning and YOY development predominate over demographic ones (number of returning adults) in driving long-term population viability for S. salar in the River Nivelle. This study showcases N b as an integrated parameter, encompassing demographic and ecological information about a reproductive event, relevant to the assessment of both short-term effects of management practices and long-term population conservation status. © 2018 The Fisheries Society of the British Isles.

Pop-up satellite archival tag effects on the diving behaviour, growth and survival of adult Atlantic salmon Salmo salar at sea.

PubMed

Hedger, R D; Rikardsen, A H; Thorstad, E B

2017-01-01

The effects of large, externally attached pop-up satellite archival tags (PSATs) were compared with those of small implanted data storage tags (DSTs) on adult Atlantic salmon Salmo salar during their ocean migration in regards to depth utilization, diving depth, diving rate, diving speed and temperatures experienced. Additionally the return rate and growth of individuals tagged with PSATs was compared with those of small acoustic tags and DSTs. Overall, the depth distribution of individuals tagged with PSATs was similar to that of those tagged with DSTs, reflecting the pelagic nature of S. salar at sea. Individuals tagged with PSATs, however, dived less frequently and to shallower depths, and dived and surfaced at slower velocities. Sea surface temperatures experienced by individuals tagged with PSATs were similar to those experienced by those tagged with DSTs for the same time of year, suggesting that there were no large differences in the ocean migration. Return rates did not depend on whether individuals were tagged with PSATs or not, indicating that survival at sea was not impacted by PSATs in comparison to small internal tags. Individuals tagged with PSATs, however, had a smaller increase in body mass than those tagged with acoustic tags or DSTs. It was concluded that PSATs are suitable for use in researching large-scale migratory behaviour of adult S. salar at sea, but that some effects on their behaviour from tagging must be expected. Effects of PSATs may be largest in the short term when S. salar are swimming in bursts at high speeds. Even though individuals tagged with PSATs performed deep and frequent dives, the results of this study suggest that untagged individuals would perform even deeper and more frequent dives than tagged individuals. © 2016 The Fisheries Society of the British Isles.

Consistent melanophore spot patterns allow long-term individual recognition of Atlantic salmon Salmo salar.

PubMed

Stien, L H; Nilsson, J; Bui, S; Fosseidengen, J E; Kristiansen, T S; Øverli, Ø; Folkedal, O

2017-12-01

The present study shows that permanent melanophore spot patterns in Atlantic salmon Salmo salar make it possible to use images of the operculum to keep track of individual fish over extended periods of their life history. Post-smolt S. salar (n = 246) were initially photographed at an average mass of 98 g and again 10 months later after rearing in a sea cage, at an average mass of 3088 g. Spots that were present initially remained and were the most overt (largest) 10 months later, while new and less overt spots had developed. Visual recognition of spot size and position showed that fish with at least four initial spots were relatively easy to identify, while identifying fish with less than four spots could be challenging. An automatic image analysis method was developed and shows potential for fast match processing of large numbers of fish. The current findings promote visual recognition of opercular spots as a welfare-friendly alternative to tagging in experiments involving salmonid fishes. © The Authors. Journal of Fish Biology published by John Wiley & Sons Ltd on behalf of The Fisheries Society of the British Isles.

Fast detection of Piscirickettsia salmonis in Salmo salar serum through MALDI-TOF-MS profiling.

PubMed

Olate, Verónica R; Nachtigall, Fabiane M; Santos, Leonardo S; Soto, Alex; Araya, Macarena; Oyanedel, Sandra; Díaz, Verónica; Marchant, Vanessa; Rios-Momberg, Mauricio

2016-03-01

Piscirickettsia salmonis is a pathogenic bacteria known as the aetiological agent of the salmonid rickettsial syndrome and causes a high mortality in farmed salmonid fishes. Detection of P. salmonis in farmed fishes is based mainly on molecular biology and immunohistochemistry techniques. These techniques are in most of the cases expensive and time consuming. In the search of new alternatives to detect the presence of P. salmonis in salmonid fishes, this work proposed the use of MALDI-TOF-MS to compare serum protein profiles from Salmo salar fish, including experimentally infected and non-infected fishes using principal component analysis (PCA). Samples were obtained from a controlled bioassay where S. salar was challenged with P. salmonis in a cohabitation model and classified according to the presence or absence of the bacteria by real time PCR analysis. MALDI spectra of the fish serum samples showed differences in its serum protein composition. These differences were corroborated with PCA analysis. The results demonstrated that the use of both MALDI-TOF-MS and PCA represents a useful tool to discriminate the fish status through the analysis of salmonid serum samples. Copyright © 2016 John Wiley & Sons, Ltd.

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

Growth hormone transgenesis does not influence territorial dominance or growth and survival of first-feeding Atlantic salmon Salmo salar in food-limited stream microcosms.

PubMed

Moreau, D T R; Fleming, I A; Fletcher, G L; Brown, J A

2011-03-01

This study explored the relative competitive ability and performance of first-feeding growth hormone (GH) transgenic and non-transgenic Atlantic salmon Salmo salar fry under low food conditions. Pair-wise dominance trials indicated a strong competitive advantage for residents of a contested foraging territory. Transgenic and non-transgenic individuals, however, were equally likely to be dominant. Similarly, in stream environments with limited food, the transgene did not influence the growth in mass or survival at high or low fry densities. Fry in low-density treatments, however, performed better than fry in high-density treatments. These results indicate that, under the environment examined, the growth performance of GH-transgenic and non-transgenic S. salar may be similar during first feeding, an intense period of selection in their life history. Similarities in competitive ability and growth performance with wild-type fish suggest that the capacity of transgenic S. salar to establish in natural streams may not be inhibited during early life history. © 2011 The Authors. Journal of Fish Biology © 2011 The Fisheries Society of the British Isles.

Bayesian Multi-Trait Analysis Reveals a Useful Tool to Increase Oil Concentration and to Decrease Toxicity in Jatropha curcas L.

PubMed Central

Silva Junqueira, Vinícius; de Azevedo Peixoto, Leonardo; Galvêas Laviola, Bruno; Lopes Bhering, Leonardo; Mendonça, Simone; Agostini Costa, Tania da Silveira; Antoniassi, Rosemar

2016-01-01

The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models. PMID:27281340

Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves

NASA Technical Reports Server (NTRS)

Mengshoel, Ole Jakob

2009-01-01

Bayesian networks (BNs) are used to represent and efficiently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagation in a clique tree compiled from a Bayesian network. There is a lack of understanding of how clique tree computation time, and BN computation time in more general, depends on variations in BN size and structure. On the one hand, complexity results tell us that many interesting BN queries are NP-hard or worse to answer, and it is not hard to find application BNs where the clique tree approach in practice cannot be used. On the other hand, it is well-known that tree-structured BNs can be used to answer probabilistic queries in polynomial time. In this article, we develop an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, or (ii) the expected number of moral edges in their moral graphs. Our approach is based on combining analytical and experimental results. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for each set. For the special case of bipartite BNs, we consequently have two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, we systematically increase the degree of the root nodes in bipartite Bayesian networks, and find that root clique growth is well-approximated by Gompertz growth curves. It is believed that this research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms, and presents an aid for analytical trade-off studies of clique tree clustering using

Developing a new Bayesian Risk Index for risk evaluation of soil contamination.

PubMed

Albuquerque, M T D; Gerassis, S; Sierra, C; Taboada, J; Martín, J E; Antunes, I M H R; Gallego, J R

2017-12-15

Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Systemic granuloma observed in Atlantic salmon Salmo salar raised to market size in a freshwater recirculation aquaculture system

USDA-ARS?s Scientific Manuscript database

Systemic granuloma was observed in sampled adult Atlantic salmon Salmo salar raised to harvest size in a freshwater recirculation aquaculture system. The prevalence of this condition was estimated at 10-20% of the population, with affected individuals grossly demonstrating pathology in varying degre...

Hierarchical structure of the Sicilian goats revealed by Bayesian analyses of microsatellite information.

PubMed

Siwek, M; Finocchiaro, R; Curik, I; Portolano, B

2011-02-01

Genetic structure and relationship amongst the main goat populations in Sicily (Girgentana, Derivata di Siria, Maltese and Messinese) were analysed using information from 19 microsatellite markers genotyped on 173 individuals. A posterior Bayesian approach implemented in the program STRUCTURE revealed a hierarchical structure with two clusters at the first level (Girgentana vs. Messinese, Derivata di Siria and Maltese), explaining 4.8% of variation (amovaФ(ST) estimate). Seven clusters nested within these first two clusters (further differentiations of Girgentana, Derivata di Siria and Maltese), explaining 8.5% of variation (amovaФ(SC) estimate). The analyses and methods applied in this study indicate their power to detect subtle population structure. © 2010 The Authors, Animal Genetics © 2010 Stichting International Foundation for Animal Genetics.

Bayesian analysis of volcanic eruptions

NASA Astrophysics Data System (ADS)

Ho, Chih-Hsiang

1990-10-01

The simple Poisson model generally gives a good fit to many volcanoes for volcanic eruption forecasting. Nonetheless, empirical evidence suggests that volcanic activity in successive equal time-periods tends to be more variable than a simple Poisson with constant eruptive rate. An alternative model is therefore examined in which eruptive rate(λ) for a given volcano or cluster(s) of volcanoes is described by a gamma distribution (prior) rather than treated as a constant value as in the assumptions of a simple Poisson model. Bayesian analysis is performed to link two distributions together to give the aggregate behavior of the volcanic activity. When the Poisson process is expanded to accomodate a gamma mixing distribution on λ, a consequence of this mixed (or compound) Poisson model is that the frequency distribution of eruptions in any given time-period of equal length follows the negative binomial distribution (NBD). Applications of the proposed model and comparisons between the generalized model and simple Poisson model are discussed based on the historical eruptive count data of volcanoes Mauna Loa (Hawaii) and Etna (Italy). Several relevant facts lead to the conclusion that the generalized model is preferable for practical use both in space and time.

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

2017-12-01

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

Model Diagnostics for Bayesian Networks

ERIC Educational Resources Information Center

Sinharay, Sandip

2006-01-01

Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

Hepatic Proteome Analysis of Atlantic Salmon (Salmo salar) After Exposure to Environmental Concentrations of Human Pharmaceuticals*

PubMed Central

Hampel, Miriam; Alonso, Esteban; Aparicio, Irene; Santos, Juan Luis; Leaver, Michael

2015-01-01

Pharmaceuticals are pseudopersistent aquatic pollutants with unknown effects at environmentally relevant concentrations. Atlantic salmon (Salmo salar) were exposed to Acetaminophen: 54.77 ± 34.67; Atenolol: 11.08 ± 7.98, and Carbamazepine: 7.85 ± 0.13 μg·L−1 for 5 days. After Acetaminophen treatment, 19 proteins were differently expressed, of which 11 were significant with respect to the control group (eight up-regulated and three down-regulated). After Atenolol treatment, seven differently expressed proteins were obtained in comparison with the control, of which six could be identified (four up-regulated and two down-regulated). Carbamazepine exposure resulted in 15 differently expressed proteins compared with the control, with 10 of them identified (seven up-regulated and three down-regulated). Out of these, three features were common between Acetaminophen and Carbamazepine and one between Carbamazepine and Atenolol. One feature was common across all treatments. Principal component analysis and heat map clustering showed a clear grouping of the variability caused by the applied treatments. The obtained data suggest (1) that exposure to environmentally relevant concentrations of the pharmaceuticals alters the hepatic protein expression profile of the Atlantic salmon; and (2) the existence of treatment specific processes that may be useful for biomarker development. PMID:25394398

Bayesian Nonparametric Ordination for the Analysis of Microbial Communities.

PubMed

Ren, Boyu; Bacallado, Sergio; Favaro, Stefano; Holmes, Susan; Trippa, Lorenzo

2017-01-01

Human microbiome studies use sequencing technologies to measure the abundance of bacterial species or Operational Taxonomic Units (OTUs) in samples of biological material. Typically the data are organized in contingency tables with OTU counts across heterogeneous biological samples. In the microbial ecology community, ordination methods are frequently used to investigate latent factors or clusters that capture and describe variations of OTU counts across biological samples. It remains important to evaluate how uncertainty in estimates of each biological sample's microbial distribution propagates to ordination analyses, including visualization of clusters and projections of biological samples on low dimensional spaces. We propose a Bayesian analysis for dependent distributions to endow frequently used ordinations with estimates of uncertainty. A Bayesian nonparametric prior for dependent normalized random measures is constructed, which is marginally equivalent to the normalized generalized Gamma process, a well-known prior for nonparametric analyses. In our prior, the dependence and similarity between microbial distributions is represented by latent factors that concentrate in a low dimensional space. We use a shrinkage prior to tune the dimensionality of the latent factors. The resulting posterior samples of model parameters can be used to evaluate uncertainty in analyses routinely applied in microbiome studies. Specifically, by combining them with multivariate data analysis techniques we can visualize credible regions in ecological ordination plots. The characteristics of the proposed model are illustrated through a simulation study and applications in two microbiome datasets.

Photoperiod control of downstream movements of Atlantic salmon Salmo salar smolts

USGS Publications Warehouse

Zydlewski, Gayle B.; Stich, Daniel S.; McCormick, Stephen D.

2014-01-01

This study provides the first direct observations that photoperiod controls the initiation of downstream movement in Atlantic salmon Salmo salar smolts. Under simulated natural day length (LDN) conditions and seasonal increases in temperature, smolts increased their downstream movements five-fold for a period of 1 month in late spring. Under the same conditions, parr did not show changes in downstream movement behaviour. When given a shortened day length (10L:14D) beginning in late winter, smolts did not increase the number of downstream movements. An early increase in day length (16L:8D) in late winter resulted in earlier initiation and termination of downstream movements compared to the LDN group. Physiological status and behaviour were related but not completely coincident: gill Na+/K+-ATPase activity increased in all treatments and thyroid hormone was elevated prior to movement in 16L:8D treatment. The most parsimonious model describing downstream movement of smolts included synergistic effects of photoperiod treatment and temperature, indicating that peak movements occurred at colder temperatures in the 16L:8D treatment than in LDN, and temperature did not influence movement of smolts in the 10L:14D treatment. The complicated interactions of photoperiod and temperature are not surprising since many organisms have evolved to rely on correlations among environmental cues and windows of opportunity to time behaviours associated with life-history transitions. These complicated interactions, however, have serious implications for phenological adjustments and persistence ofS. salar populations in response to climate change.

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

Morphological differences in parr of Atlantic salmon Salmo salar from three regions in Norway.

PubMed

Solem, O; Berg, O K

2011-05-01

Morphological characters were compared in parr (total length 33-166 mm) of Atlantic salmon Salmo salar sampled from eight wild populations in three regions, three in northern, two in the middle and three in southern Norway, covering a distance of 1700 km (from 70° N to 58° N). On the basis of morphological characters 94·6% of the individuals were correctly classified into the three regions. Discrimination between populations within these three regions also had a high degree of correct classification (89·0-95·8%). Principle component analysis identified largest differences to be in head characters, notably eye diameter and jawbone, with the smallest diameter and head size among the northernmost populations. Fish from the southern rivers had a deeper body form whereas fish from the middle region had larger heads and pectoral fins. This illustrates that S. salar already in the early parr stage has morphological traits, which can be used in discrimination between regions and populations and that these differences are discernible in spite of the volume of escaped farmed fish spawning in Norwegian rivers during the past 30 years. © 2011 The Authors. Journal of Fish Biology © 2011 The Fisheries Society of the British Isles.

Bayesian correlated clustering to integrate multiple datasets

PubMed Central

Kirk, Paul; Griffin, Jim E.; Savage, Richard S.; Ghahramani, Zoubin; Wild, David L.

2012-01-01

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/. Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID

A quantitative history of precipitation and hydrologic variability for the last 45 ka: Lake Titicaca, Salar de Coipasa and Salar de Uyuni, Peru and Bolivia

NASA Astrophysics Data System (ADS)

Nunnery, A.; Baker, P. A.; Coe, M. T.; Fritz, S. C.; Rigsby, C. A.

2011-12-01

Precipitation on the Bolivian/Peruvian Altiplano is dominantly controlled by the South American summer Monsoon (SASM). Over long timescales moisture transport to the Altiplano by the SASM fluctuates in intensity due to precessional insolation forcing as well as teleconnections to millennial scale abrupt temperature shifts in the North Atlantic. These long-term changes in moisture transport have been observed in multiple paleoclimate and paleo-lake level records as advances and retreats of large lakes in the terminal basin (the Salar de Uyuni). Several previous studies using energy/water balance models have been applied to paleoclimate records in attempts to provide quantitative constraints on past precipitation and temperature (P and T). For example, Blodgett et al. concluded that high paleolake stands, first dated at ca. 16,000 cal. yr BP, required P 20% higher and T 5°C colder than modern. We expand on this work conducting two experiments. The first uses a latitudinal paleohydrologic profile to reconstruct hydrological history. The second uses a terrestrial hydrology model (THMB) to "predict" lake level given changes in P and T. The profile is constructed using records from Lake Titicaca (LT), Salar de Coipasa (SC) and Salar de Uyuni (SU). LT carbonate and diatom records indicate a deep, overflowing lake for much of the last 100 ka with a distinct dry, closed-basin phase in the early to mid Holocene. A continuous sediment core from SC indicates lake level fluctuations between deep and shallow phases for the last 45 ka. A natural gamma radiation log from SU, where large paleolakes alternated with shallow salt pans characteristic of drier and/or warmer periods, shows alternation between wet and dry phases through time. These three records give evidence to the complex nature of Altiplano hydrology, most notably the ability to sustain lakes in the SC basin while exhibiting dry conditions in SU. For the second experiment, THMB, which estimates water balance and

Genome Sequence of Streptococcus phocae subsp. salmonis Strain C-4T, Isolated from Atlantic Salmon (Salmo salar)

PubMed Central

Suarez, Rudy; Lazo, Eduardo; Bravo, Diego; Llegues, Katerina O.; Romalde, Jesús L.; Godoy, Marcos G.

2014-01-01

Streptococcus phocae subsp. salmonis is a fish pathogen that has an important impact on the Chilean salmon industry. Here, we report the genome sequence of the type strain C-4T isolated from Atlantic salmon (Salmo salar), showing a number of interesting features and genes related to its possible virulence factors. PMID:25502668

Weight loss and fillet quality characteristics of Atlantic salmon (Salmo salar) after purging for 5, 10, 15 or 20 days

USDA-ARS?s Scientific Manuscript database

Atlantic salmon, Salmo salar, are typically cultured in marine net pens. However, technological advancements in recirculating aquaculture systems have increased the feasibility of culturing Atlantic salmon in land-based systems to alleviate environmental and disease issues limiting sustainability. ...

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Within-farm spread of infectious salmon anemia virus (ISAV) in Atlantic salmon Salmo salar farms in Chile.

PubMed

Mardones, F O; Jansen, P A; Valdes-Donoso, P; Jarpa, M; Lyngstad, T M; Jimenez, D; Carpenter, T E; Perez, A M

2013-09-24

Spread of infectious salmon anemia virus (ISAV) at the cage level was quantified using a subset of data from 23 Atlantic salmon Salmo salar farms located in southern Chile. Data collected from official surveillance activities were systematically organized to obtain detailed information on infectious salmon anemia (ISA) outbreaks. Descriptive statistics for outbreak duration, proportion of infected fish, and time to secondary infection were calculated to quantify the magnitude of ISAV incursions. Linear and multiple failure time (MFT) regression models were used to determine factors associated with the cage-level reproduction number (Rc) and hazard rate (HR) for recurrent events, respectively. In addition, the Knox test was used to assess if cage-to-cage transmissions were clustered in space and time. Findings suggest that within farms, ISA outbreaks, on average, lasted 30 wk (median = 26 wk, 95% CI = 24 to 37 wk) and affected 57.3% (95% CI = 47.7 to 67.0%) of susceptible cages. The median time to secondarily diagnosed cages was 23 d. Occurrence of clinical ISAV outbreaks was significantly associated with increased Rc, whereas increased HR was significantly associated with clinical outbreaks and with a large number of fish. Spatio-temporal analysis failed to identify clustering of cage cases, suggesting that within-farm ISAV spread is independent of the spatial location of the cages. Results presented here will help to better understand ISAV transmission, to improve the design of surveillance programs in Chile and other regions in which salmon are intensively farmed, and to examine the economic impact of ISAV and related management strategies on various cost and demand shifting factors.

Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

NASA Astrophysics Data System (ADS)

Fox, Neil I.; Micheas, Athanasios C.; Peng, Yuqiang

2016-07-01

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System - Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme.

Effects of storage time on the motility, mortality and calcium levels of Atlantic salmon Salmo salar spermatozoa.

PubMed

Parodi, J; Guerra, G; Cuevas, M; Ramírez-Reveco, A; Romero, F

2017-04-01

This study estimates spermatozoa mortality, morphology, motility and intracellular calcium levels in Atlantic salmon Salmo salar milt after prolonged storage. Milt samples were preserved at 4° C for 25 days and then evaluated for mortality. Motility remained high for the first 3 days and the mortality was low during the first 5 days of storage. A decrease of >50% in calcium content was observed after 5 days of storage. When spermatozoa were activated, calcium levels increased >200% in relative fluorescence units (RFU); this rate of increase was lost when the samples were stored for extended periods of time and was only partially manifested in a zero calcium solution. The results suggest that in vitro storage of S. salar spermatozoa at 4° C for a period of 3 days preserves motility and limits mortality to levels similar to those of fresh spermatozoa. This method also maintains intracellular calcium storage critical for spermatozoa performance. © 2017 The Fisheries Society of the British Isles.

A review of factors influencing maturation of atlantic salmon salmo salar with focus on water recirculation aquaculture system environments

USDA-ARS?s Scientific Manuscript database

Maturation of Atlantic salmon Salmo salar is an extremely complex process, particularly in aquaculture systems, with many variables (known or otherwise) having the capacity to influence the timing and prevalence of maturation, and acting as promoters and/or inhibitors of sexual development. The vast...

Small area clustering of under-five children's mortality and associated factors using geo-additive Bayesian discrete-time survival model in Kersa HDSS, Ethiopia.

PubMed

Dedefo, Melkamu; Oljira, Lemessa; Assefa, Nega

2016-02-01

Child mortality reflects a country's level of socio-economic development and quality of life. In Ethiopia, limited studies were conducted on under-five mortality and almost none of them tried to identify the spatial effect on mortality. Thus, this study explored the small area clustering of under-five mortality and associated factors in Kersa HDSS, Eastern Ethiopia. The study population included all children under the age of five years during the time September, 2008-august 31, 2012 which are registered in Kersa Health and Demographic Surveillance System (Kersa HDSS). A flexible Bayesian geo-additive discrete-time survival mixed model was used. Some of the factors that are significantly associated with under-five mortality, with posterior odds ratio and 95% credible intervals, are maternal educational status 1.31(1.13,-1.49), place of delivery 1.016(1.013-1.12), no of live birth at a delivery 0.35(0.23,1.83), low household wealth index 1.26(1.10 1.43) middle level household wealth index 0.95 (0.84 1.07) pre-term duration of pregnancy 1.95(1.27,2.91), post-term duration of pregnancy 0.74(0.60,0.93) and antenatal visit 1.19(1.06, 1.35). Variation was noted in the risk of under-five mortality by the selected small administrative regions (kebeles). This study reveals geographic patterns in rates of under-five mortality in those selected small administrative regions and shows some important determinants of under-five mortality. More importantly, we observed clustering of under-five mortality, which indicates the importance of spatial effects and presentation of this clustering through maps that facilitates visuality and highlights differentials across geographical areas that would, otherwise, be overlooked in traditional data-analytic methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bayesian Mediation Analysis

ERIC Educational Resources Information Center

Yuan, Ying; MacKinnon, David P.

2009-01-01

In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

Spatial clustering of average risks and risk trends in Bayesian disease mapping.

PubMed

Anderson, Craig; Lee, Duncan; Dean, Nema

2017-01-01

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Practical Bayesian tomography

NASA Astrophysics Data System (ADS)

Granade, Christopher; Combes, Joshua; Cory, D. G.

2016-03-01

In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we address all three problems. First, we use modern statistical methods, as pioneered by Huszár and Houlsby (2012 Phys. Rev. A 85 052120) and by Ferrie (2014 New J. Phys. 16 093035), to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first priors on quantum states and channels that allow for including useful experimental insight. Finally, we develop a method that allows tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.

Assessing groundwater recharge in an Andean closed basin using isotopic characterization and a rainfall-runoff model: Salar del Huasco basin, Chile

NASA Astrophysics Data System (ADS)

Uribe, Javier; Muñoz, José F.; Gironás, Jorge; Oyarzún, Ricardo; Aguirre, Evelyn; Aravena, Ramón

2015-11-01

Closed basins are catchments whose drainage networks converge to lakes, salt flats or alluvial plains. Salt flats in the closed basins in arid northern Chile are extremely important ecological niches. The Salar del Huasco, one of these salt flats located in the high plateau (Altiplano), is a Ramsar site located in a national park and is composed of a wetland ecosystem rich in biodiversity. The proper management of the groundwater, which is essential for the wetland function, requires accurate estimates of recharge in the Salar del Huasco basin. This study quantifies the spatio-temporal distribution of the recharge, through combined use of isotopic characterization of the different components of the water cycle and a rainfall-runoff model. The use of both methodologies aids the understanding of hydrological behavior of the basin and enabled estimation of a long-term average recharge of 22 mm/yr (i.e., 15 % of the annual rainfall). Recharge has a high spatial variability, controlled by the geological and hydrometeorological characteristics of the basin, and a high interannual variability, with values ranging from 18 to 26 mm/yr. The isotopic approach allowed not only the definition of the conceptual model used in the hydrological model, but also eliminated the possibility of a hydrogeological connection between the aquifer of the Salar del Huasco basin and the aquifer that feeds the springs of the nearby town of Pica. This potential connection has been an issue of great interest to agriculture and tourism activities in the region.

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effects of ozonation on select waterborne steroid hormones in recirculation aquaculture systems containing sexually mature Atlantic salmon Salmo salar

USDA-ARS?s Scientific Manuscript database

A controlled 3-month study was conducted in 6 replicated water recirculation aquaculture systems (RAS) containing a mixture of sexually mature and immature Atlantic salmon Salmo salar to determine whether water ozonation is associated with a reduction in waterborne hormones. Post-smolt Atlantic salm...

Production of market-size North American strain Atlantic salmon Salmo salar in a land-based recirculation aquaculture system using freshwater

USDA-ARS?s Scientific Manuscript database

There is interest in culturing Atlantic salmon Salmo salar to market-size in land-based, closed containment systems that use recirculation aquaculture systems (RAS), as this technology often enables facilities to locate near major markets, obtain permits, exclude obligate pathogens, and/or reduce en...

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Genome Sequence of Streptococcus phocae subsp. salmonis Strain C-4T, Isolated from Atlantic Salmon (Salmo salar).

PubMed

Avendaño-Herrera, Ruben; Suarez, Rudy; Lazo, Eduardo; Bravo, Diego; Llegues, Katerina O; Romalde, Jesús L; Godoy, Marcos G

2014-12-11

Streptococcus phocae subsp. salmonis is a fish pathogen that has an important impact on the Chilean salmon industry. Here, we report the genome sequence of the type strain C-4(T) isolated from Atlantic salmon (Salmo salar), showing a number of interesting features and genes related to its possible virulence factors. Copyright © 2014 Avendaño-Herrera et al.

The Bayesian reader: explaining word recognition as an optimal Bayesian decision process.

PubMed

Norris, Dennis

2006-04-01

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model and the way the model predicts different patterns of results in different tasks follow entirely from the assumption that human readers approximate optimal Bayesian decision makers. ((c) 2006 APA, all rights reserved).

Bayesian Mass Estimates of the Milky Way: Including Measurement Uncertainties with Hierarchical Bayes

NASA Astrophysics Data System (ADS)

Eadie, Gwendolyn M.; Springford, Aaron; Harris, William E.

2017-02-01

We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie et al. and Eadie and Harris and builds upon the preliminary reports by Eadie et al. The method uses a distribution function f({ E },L) to model the Galaxy and kinematic data from satellite objects, such as globular clusters (GCs), to trace the Galaxy’s gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie and Harris and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions. This profile implies a mass within 125 kpc of 4.8× {10}11{M}⊙ with a 95% Bayesian credible region of (4.0{--}5.8)× {10}11{M}⊙ . Our results also provide estimates of the true specific energies of all the GCs. By comparing these estimated energies to the measured energies of GCs with complete velocity measurements, we observe that (the few) remote tracers with complete measurements may play a large role in determining a total mass estimate of the Galaxy. Thus, our study stresses the need for more remote tracers with complete velocity measurements.

Phosphorus flux due to Atlantic salmon (Salmo salar) in an oligotrophic upland stream: effects of management and demography

Treesearch

Keith H. Nislow; John D. Armstrong; Simon McKelvey

2004-01-01

Little is known concerning the role of Atlantic salmon (Salmo salar) in the transport of nutrients to and from river systems. We used demographic data from the River Bran, an oligotrophic river in Scotland, UK, to construct a budget for the transport of phosphorus (P) and applied it to investigate the effects of management strategies and demographic...

Bayesian demography 250 years after Bayes

PubMed Central

Bijak, Jakub; Bryant, John

2016-01-01

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889

The Psychology of Bayesian Reasoning

DTIC Science & Technology

2014-10-21

The psychology of Bayesian reasoning David R. Mandel* Socio-Cognitive Systems Section, Defence Research and Development Canada and Department...belief revision, subjective probability, human judgment, psychological methods. Most psychological research on Bayesian reasoning since the 1970s has...attention to some important problems with the conventional approach to studying Bayesian reasoning in psychology that has been dominant since the

Bayesian Ensemble Trees (BET) for Clustering and Prediction in Heterogeneous Data

PubMed Central

Duan, Leo L.; Clancy, John P.; Szczesniak, Rhonda D.

2016-01-01

We propose a novel “tree-averaging” model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian Ensemble Trees (BET) and model them as a Dirichlet process. We show that BET determines the optimal number of trees by adapting to the data heterogeneity. Compared with the other ensemble methods, BET requires much fewer trees and shows equivalent prediction accuracy using weighted averaging. Moreover, each tree in BET provides variable selection criterion and interpretation for each subset. We developed an efficient estimating procedure with improved estimation strategies in both CART and mixture models. We demonstrate these advantages of BET with simulations and illustrate the approach with a real-world data example involving regression of lung function measurements obtained from patients with cystic fibrosis. Supplemental materials are available online. PMID:27524872

Basics of Bayesian methods.

PubMed

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

Late Quaternary Paleoclimatic History of Tropical South America From Drilling Lake Titicaca and the Salar de Uyuni

NASA Astrophysics Data System (ADS)

Baker, P. A.; Fritz, S. C.; Seltzer, G. O.; Rigsby, C. A.; Lowenstein, T. K.; Ku, R.

2003-12-01

Seven drill cores were recovered from Lake Titicaca during the NSF/ICDP/DOSECC drilling expedition of 2001. Sub-lake floor drilling depths ranged from 53 to 139 m; water depths ranged from 40 to 232 m; recoveries ranged from 75 to 112 percent. Our most detailed multi-proxy analyses to date have been done on Core 2B raised from the central basin of the lake from 232 m water depth, drilled to 139.26 m sub-lakefloor with 140.61 m of total sediment recovered (101 percent). A basal age of 200 Ka is estimated by linear extrapolation from radiocarbon measurements in the upper 25 m of core; Ar-Ar dating of interbedded ashes and U/Th dating of abiogenic aragonites are underway. The volume and lake level of Lake Titicaca have undergone large changes several times during the late Quaternary. Proxies for these water level changes (each of different fidelity) include the ratio of planktonic-to-benthic diatoms, sedimentary carbonate content, and stable isotopic content of organic carbon. The most recent of these changes, has been described previously from earlier piston cores. In the early and middle Holocene the lake fell below its outlet to 85 m below modern level, lake salinity increased several-fold, and the Salar de Uyuni, which receives overflow from Titicaca, dessicated. In contrast, Lake Titicaca was deep, fresh, and overflowing (southward to the Salar de Uyuni) throughout the last glacial maximum from prior to 25,000 BP to at least 15,000 BP. According to extrapolated ages, the penultimate major lowstand of Lake Titicaca occurred prior to 60,000 BP, when seismic evidence indicates that lake level was about 200 m lower than present. Near the end of this lowstand, the lake also became quite saline. There are at least three, and possibly more, older lowstands, each separated temporally by periods in which the lake freshened dramatically and overflowed. These results will be compared with results from previous drilling in the Salar de Uyuni.

Interactions between riparian shading and food supply: a seasonal comparison of effects on time budgets, space use and growth in Atlantic salmon Salmo salar.

PubMed

Orpwood, J E; Armstrong, J D; Griffiths, S W

2010-11-01

This study examines seasonal (winter v. summer) differences in space-time budgets, food intake and growth of Atlantic salmon Salmo salar parr in a controlled, large-scale stream environment, to examine the direction and magnitude of shifts in behaviour patterns as influenced by the availability of overhead cover and food supply. Salmo salar parr tested in the presence of overhead cover were significantly more nocturnal and occupied more peripheral positions than those tested in the absence of overhead cover. This increase in nocturnal activity was driven primarily by increased activity at night, accompanied by a reduction in daytime activity during winter. The presence of overhead cover had no effect on rates of food intake or growth for a given food supply in a given season. Growth rates were significantly higher for fish subjected to a high food supply than those subjected to a low food supply. Food supply did not affect the extent to which S. salar parr were nocturnal. These results were consistent between winter and summer. The use of riparian shading as a management technique to mitigate the effects of warming allows the adoption of more risk-averse foraging behaviour and may be particularly beneficial in circumstances where it serves also to increase the availability of food. © 2010 Crown Copyright Marine Scotland. Journal of Fish Biology © 2010 The Fisheries Society of the British Isles.

Long noncoding RNAs (lncRNAs) dynamics evidence immunomodulation during ISAV-Infected Atlantic salmon (Salmo salar)

PubMed Central

Boltaña, Sebastian; Valenzuela-Miranda, Diego; Aguilar, Andrea; Mackenzie, Simon; Gallardo-Escárate, Cristian

2016-01-01

Despite evidence for participation in the host response to infection, the roles of many long non-coding RNAs (lncRNAs) remain unknown. Therefore, the aims of this study were to identify lncRNAs in Atlantic salmon (Salmo salar) and evaluate their transcriptomic regulation during ISA virus (ISAV) infection, an Orthomyxoviridae virus associated with high mortalities in salmonid aquaculture. Using next-generation sequencing, whole-transcriptome analysis of the Salmo salar response to ISAV infection was performed, identifying 5,636 putative lncRNAs with a mean length of 695 base pairs. The transcriptional modulation evidenced a similar number of differentially expressed lncRNAs in the gills (3,294), head-kidney (3,275), and liver (3,325) over the course of the infection. Moreover, analysis of a subset of these lncRNAs showed the following: (i) Most were similarly regulated in response to ISA virus infection; (ii) The transcript subsets were uniquely modulated in each tissue (gills, liver, and head-kidney); and (iii) A subset of lncRNAs were upregulated for each tissue and time analysed, indicating potential markers for ISAV infection. These findings represent the first discovery of widespread differential expression of lncRNAs in response to virus infection in non-model species, suggesting that lncRNAs could be involved in regulating the host response during ISAV infection. PMID:26939752

Long noncoding RNAs (lncRNAs) dynamics evidence immunomodulation during ISAV-Infected Atlantic salmon (Salmo salar).

PubMed

Boltaña, Sebastian; Valenzuela-Miranda, Diego; Aguilar, Andrea; Mackenzie, Simon; Gallardo-Escárate, Cristian

2016-03-04

Despite evidence for participation in the host response to infection, the roles of many long non-coding RNAs (lncRNAs) remain unknown. Therefore, the aims of this study were to identify lncRNAs in Atlantic salmon (Salmo salar) and evaluate their transcriptomic regulation during ISA virus (ISAV) infection, an Orthomyxoviridae virus associated with high mortalities in salmonid aquaculture. Using next-generation sequencing, whole-transcriptome analysis of the Salmo salar response to ISAV infection was performed, identifying 5,636 putative lncRNAs with a mean length of 695 base pairs. The transcriptional modulation evidenced a similar number of differentially expressed lncRNAs in the gills (3,294), head-kidney (3,275), and liver (3,325) over the course of the infection. Moreover, analysis of a subset of these lncRNAs showed the following: (i) Most were similarly regulated in response to ISA virus infection; (ii) The transcript subsets were uniquely modulated in each tissue (gills, liver, and head-kidney); and (iii) A subset of lncRNAs were upregulated for each tissue and time analysed, indicating potential markers for ISAV infection. These findings represent the first discovery of widespread differential expression of lncRNAs in response to virus infection in non-model species, suggesting that lncRNAs could be involved in regulating the host response during ISAV infection.

Piscine myocarditis virus (PMCV) in wild Atlantic salmon Salmo salar.

PubMed

Garseth, Ase Helen; Biering, Eirik; Tengs, Torstein

2012-12-27

Cardiomyopathy syndrome (CMS) is a severe cardiac disease of sea-farmed Atlantic salmon Salmo salar L., but CMS-like lesions have also been found in wild Atlantic salmon. In 2010 a double-stranded RNA virus of the Totiviridae family, provisionally named piscine myocarditis virus (PMCV), was described as the causative agent of CMS. In the present paper we report the first detection of PMCV in wild Atlantic salmon. The study is based on screening of 797 wild Atlantic salmon by real-time RT-PCR. The samples were collected from 35 different rivers along the coast of Norway, and all individuals included in the study were classified as wild, based on visual appearance and scale reading. Two samples tested positive during PCR analysis, and the results were confirmed by sequencing.

Limitations of cytochrome oxidase I for the barcoding of Neritidae (Mollusca: Gastropoda) as revealed by Bayesian analysis.

PubMed

Chee, S Y

2015-05-25

The mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) gene has been universally and successfully utilized as a barcoding gene, mainly because it can be amplified easily, applied across a wide range of taxa, and results can be obtained cheaply and quickly. However, in rare cases, the gene can fail to distinguish between species, particularly when exposed to highly sensitive methods of data analysis, such as the Bayesian method, or when taxa have undergone introgressive hybridization, over-splitting, or incomplete lineage sorting. Such cases require the use of alternative markers, and nuclear DNA markers are commonly used. In this study, a dendrogram produced by Bayesian analysis of an mtDNA COI dataset was compared with that of a nuclear DNA ATPS-α dataset, in order to evaluate the efficiency of COI in barcoding Malaysian nerites (Neritidae). In the COI dendrogram, most of the species were in individual clusters, except for two species: Nerita chamaeleon and N. histrio. These two species were placed in the same subcluster, whereas in the ATPS-α dendrogram they were in their own subclusters. Analysis of the ATPS-α gene also placed the two genera of nerites (Nerita and Neritina) in separate clusters, whereas COI gene analysis placed both genera in the same cluster. Therefore, in the case of the Neritidae, the ATPS-α gene is a better barcoding gene than the COI gene.

From the viral perspective: infectious salmon anemia virus (ISAV) transcriptome during the infective process in Atlantic salmon (Salmo salar).

PubMed

Valenzuela-Miranda, Diego; Cabrejos, María Eugenia; Yañez, José Manuel; Gallardo-Escárate, Cristian

2015-04-01

The infectious salmon anemia virus (ISAV) is a severe disease that mainly affects the Atlantic salmon (Salmo salar) aquaculture industry. Although several transcriptional studies have aimed to understand Salmon-ISAV interaction through the evaluation of host-gene transcription, none of them has focused their attention upon the viral transcriptional dynamics. For this purpose, RNA-Seq and RT-qPCR analyses were conducted in gills, liver and head-kidney of S. salar challenged by cohabitation with ISAV. Results evidence the time and tissue transcript patterns involved in the viral expression and how the transcription levels of ISAV segments are directly linked with the protein abundance found in other virus of the Orthomyxoviridae family. In addition, RT-qPCR result evidenced that quantification of ISAV through amplification of segment 3 would result in a more sensitive approach for detection and quantification of ISAV. This study offers a more comprehensive approach regarding the ISAV infective process and gives novel knowledge for its molecular detection. Copyright © 2014 Elsevier B.V. All rights reserved.

Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar).

PubMed

Houston, Ross D; Taggart, John B; Cézard, Timothé; Bekaert, Michaël; Lowe, Natalie R; Downing, Alison; Talbot, Richard; Bishop, Stephen C; Archibald, Alan L; Bron, James E; Penman, David J; Davassi, Alessandro; Brew, Fiona; Tinch, Alan E; Gharbi, Karim; Hamilton, Alastair

2014-02-06

Dense single nucleotide polymorphism (SNP) genotyping arrays provide extensive information on polymorphic variation across the genome of species of interest. Such information can be used in studies of the genetic architecture of quantitative traits and to improve the accuracy of selection in breeding programs. In Atlantic salmon (Salmo salar), these goals are currently hampered by the lack of a high-density SNP genotyping platform. Therefore, the aim of the study was to develop and test a dense Atlantic salmon SNP array. SNP discovery was performed using extensive deep sequencing of Reduced Representation (RR-Seq), Restriction site-Associated DNA (RAD-Seq) and mRNA (RNA-Seq) libraries derived from farmed and wild Atlantic salmon samples (n = 283) resulting in the discovery of > 400 K putative SNPs. An Affymetrix Axiom® myDesign Custom Array was created and tested on samples of animals of wild and farmed origin (n = 96) revealing a total of 132,033 polymorphic SNPs with high call rate, good cluster separation on the array and stable Mendelian inheritance in our sample. At least 38% of these SNPs are from transcribed genomic regions and therefore more likely to include functional variants. Linkage analysis utilising the lack of male recombination in salmonids allowed the mapping of 40,214 SNPs distributed across all 29 pairs of chromosomes, highlighting the extensive genome-wide coverage of the SNPs. An identity-by-state clustering analysis revealed that the array can clearly distinguish between fish of different origins, within and between farmed and wild populations. Finally, Y-chromosome-specific probes included on the array provide an accurate molecular genetic test for sex. This manuscript describes the first high-density SNP genotyping array for Atlantic salmon. This array will be publicly available and is likely to be used as a platform for high-resolution genetics research into traits of evolutionary and economic importance in salmonids and in aquaculture

Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar)

PubMed Central

2014-01-01

Background Dense single nucleotide polymorphism (SNP) genotyping arrays provide extensive information on polymorphic variation across the genome of species of interest. Such information can be used in studies of the genetic architecture of quantitative traits and to improve the accuracy of selection in breeding programs. In Atlantic salmon (Salmo salar), these goals are currently hampered by the lack of a high-density SNP genotyping platform. Therefore, the aim of the study was to develop and test a dense Atlantic salmon SNP array. Results SNP discovery was performed using extensive deep sequencing of Reduced Representation (RR-Seq), Restriction site-Associated DNA (RAD-Seq) and mRNA (RNA-Seq) libraries derived from farmed and wild Atlantic salmon samples (n = 283) resulting in the discovery of > 400 K putative SNPs. An Affymetrix Axiom® myDesign Custom Array was created and tested on samples of animals of wild and farmed origin (n = 96) revealing a total of 132,033 polymorphic SNPs with high call rate, good cluster separation on the array and stable Mendelian inheritance in our sample. At least 38% of these SNPs are from transcribed genomic regions and therefore more likely to include functional variants. Linkage analysis utilising the lack of male recombination in salmonids allowed the mapping of 40,214 SNPs distributed across all 29 pairs of chromosomes, highlighting the extensive genome-wide coverage of the SNPs. An identity-by-state clustering analysis revealed that the array can clearly distinguish between fish of different origins, within and between farmed and wild populations. Finally, Y-chromosome-specific probes included on the array provide an accurate molecular genetic test for sex. Conclusions This manuscript describes the first high-density SNP genotyping array for Atlantic salmon. This array will be publicly available and is likely to be used as a platform for high-resolution genetics research into traits of evolutionary and economic importance in

Bayesian Modeling of Temporal Coherence in Videos for Entity Discovery and Summarization.

PubMed

Mitra, Adway; Biswas, Soma; Bhattacharyya, Chiranjib

2017-03-01

A video is understood by users in terms of entities present in it. Entity Discovery is the task of building appearance model for each entity (e.g., a person), and finding all its occurrences in the video. We represent a video as a sequence of tracklets, each spanning 10-20 frames, and associated with one entity. We pose Entity Discovery as tracklet clustering, and approach it by leveraging Temporal Coherence (TC): the property that temporally neighboring tracklets are likely to be associated with the same entity. Our major contributions are the first Bayesian nonparametric models for TC at tracklet-level. We extend Chinese Restaurant Process (CRP) to TC-CRP, and further to Temporally Coherent Chinese Restaurant Franchise (TC-CRF) to jointly model entities and temporal segments using mixture components and sparse distributions. For discovering persons in TV serial videos without meta-data like scripts, these methods show considerable improvement over state-of-the-art approaches to tracklet clustering in terms of clustering accuracy, cluster purity and entity coverage. The proposed methods can perform online tracklet clustering on streaming videos unlike existing approaches, and can automatically reject false tracklets. Finally we discuss entity-driven video summarization- where temporal segments of the video are selected based on the discovered entities, to create a semantically meaningful summary.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

Why aren't there more Atlantic salmon (Salmo salar)?

USGS Publications Warehouse

Parrish, D.L.; Behnke, R.J.; Gephard, S.R.; McCormick, S.D.; Reeves, G.H.

1998-01-01

Numbers of wild anadromous Atlantic salmon (Salmo salar) have declined demonstrably throughout their native range. The current status of runs on rivers historically supporting salmon indicate widespread declines and extirpations in Europe and North America primarily in southern portions of the range. Many of these declines or extirpations can be attributed to the construction of mainstem dams, pollution (including acid rain), and total dewatering of streams. Purported effects on declines during the 1960s through the 1990s include overfishing, and more recently, changing ocean conditions, and intensive aquaculture. Most factors affecting salmon numbers do not act singly, but rather in concert, which masks the relative contribution of each factor. Salmon researchers and managers should not look for a single culprit in declining numbers of salmon, but rather, seek solutions through rigorous data gathering and testing of multiple effects integrated across space and time.

A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

2017-06-01

A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

Investigating the influence of nitrate nitrogen on post-smolt Atlantic salmon Salmo salar reproductive physiology in freshwater recirculation aquaculture systems

USDA-ARS?s Scientific Manuscript database

An 8-month trial was carried out to assess the effects of NO3-N on a variety of performance and physiological outcomes in post-smolt Atlantic salmon Salmo salar (initial weight 102 plus or minus 1 g) reared in six replicated laboratory-scale water recirculation aquaculture systems (RAS). Three RAS r...

Quaternary shortening in the central Puna Plateau of NW Argentina: Preliminary results from the Salar de Pocitos, Salta province (24.5° S, 67° W)

NASA Astrophysics Data System (ADS)

Freymark, Jessica; Strecker, Manfred R.; Bookhagen, Bodo; Bekeschus, Benjamin; Eckelmann, Felix; Alonso, Ricardo

2013-04-01

Active tectonism in Cenozoic orogenic plateaus is often characterized by a combination of active extensional and strike-slip faulting subsequent to protracted phases of shortening and the build-up of high topography. In the Puna Plateau of NW Argentina, the southern part of the world's second largest orogenic plateau, the changeover from shortening to extensional tectonics is thought to have occured between 7 and 5 Ma along the southeastern plateau margin, while the central and northern plateau areas apparently changed into an extensional regime between 9 and 6 Ma (Cladouhos et al., 1994). Despite these observations of extensional structures we report on new data from the Salar de Pocitos that show sustained shortening in the south-central part of the plateau. The south-central Puna Plateau is characterized by an average elevation of about 3700 m with low relief and internally drained basins, which are bordered by reverse-fault bounded ranges. The N-S oriented Salar de Pocitos is an integral part of these contractional structures and covers an area of ~435 km². The western border of the basin constitutes the eastern flank of an anticline involving Tertiary and Quaternary sediments, while the eastern border is delimited by a N-S striking reverse fault, bounding the range front of the Sierra Qda. Honda. In the north of the Salar de Pocitos the three Miocene volcanoes Tultul, Delmedio and Pocitos form a barrier with the Salar del Rincón, and the south of the basin is bordered by fault blocks involving Ordovician lithologies that have left only a narrow valley that may have provided an outlet of the basin in the past. Multiple terraces generated during Late Pleistocene and Holocene lake highstands straddle the Pocitos Basin and serve as excellent strain markers to assess neotectonic deformation. We surveyed the terraces along N-S and E-W transects using a differential GPS. The E-W surveys are perpendicular to the structures that bound the basin and record

Growth evaluation of Atlantic Salmon (Salmo salar) raised in seawater or freshwater and fed either fishmeal based or marine-free diets

USDA-ARS?s Scientific Manuscript database

A forty week feeding study was conducted with Atlantic salmon (Salmo salar) smolts in two recirculating aquaculture systems. Two identical systems were used and contained either freshwater (0 ppt) or seawater (about 30 ppt). Fish were fed one of two diets, a control diet containing fishmeal and fi...

Growth evaluation of atlantic salmon (Salmo salar) raised in seawater or freshwater and fed either fishmeal based on marine-free diets

USDA-ARS?s Scientific Manuscript database

A forty week feeding study was conducted with Atlantic salmon (Salmo salar) smolts in two recirculating aquaculture systems. Twelve salmon (average initial weight 117 g; initial density 9.4 kg/m3) were stocked per tank. Two identical systems were used and contained either freshwater (0 ppt) or sea...

Immunoglobulin isotypes in Atlantic salmon, Salmo salar.

PubMed

Hordvik, Ivar

2015-02-27

There are three major immunoglobulin (Ig) isotypes in salmonid fish: IgM, IgD and IgT, defined by the heavy chains μ, δ and τ, respectively. As a result of whole genome duplication in the ancestor of the salmonid fish family, Atlantic salmon (Salmo salar) possess two highly similar Ig heavy chain gene complexes (A and B), comprising two μ genes, two δ genes, three intact τ genes and five τ pseudogenes. The μA and μB genes correspond to two distinct sub-populations of serum IgM. The IgM-B sub-variant has a characteristic extra cysteine near the C-terminal part of the heavy chain and exhibits a higher degree of polymer disulfide cross-linking compared to IgM-A. The IgM-B:IgM-A ratio in serum is typically 60:40, but skewed ratios are also observed. The IgT isotype appears to be specialized to mucosal immune responses in salmonid fish. The concentration of IgT in serum is 100 to 1000 times lower than IgM. Secreted forms of IgD have been detected in rainbow trout, but not yet in Atlantic salmon.

Potential use of the invasive European green crab (Carcinus maenas) as an ingredient in Atlantic Salmon (Salmo salar) diets; a preliminary analysis

USDA-ARS?s Scientific Manuscript database

Atlantic salmon (Salmo salar) is an important cultured carnivorous species with wide comsumer acceptance. With the finite supply of available fishmeal and fish oil available for aquafeeds, research on and utilization of alternative protein and lipid sources is expandingWe examined the nutritional p...

Trans-generational maternal effect: temperature influences egg size of the offspring in Atlantic salmon Salmo salar.

PubMed

Jonsson, B; Jonsson, N

2016-08-01

Effect of increased temperature during egg maturation on the mass of single eggs produced by the offspring was investigated experimentally in Atlantic salmon Salmo salar. Mass of eggs produced by next-generation females was larger when their mothers experienced warmer water during the last two months of egg maturation, relative to those that experienced unheated river water. There was no similar trans-generational paternal effect on offspring egg mass. © 2016 The Fisheries Society of the British Isles.

Cloning of the prepro C-RFa gene and brain localization of the active peptide in Salmo salar.

PubMed

Montefusco-Siegmund, R A; Romero, A; Kausel, G; Muller, M; Fujimoto, M; Figueroa, J

2006-08-01

In all vertebrates, the synthesis and release of prolactin (Prl) from pituitary lactotroph cells is tightly controlled by hypothalamic factors. We have cloned and characterized a hypothalamic cDNA from Atlantic salmon (Salmo salar) encoding C-RFa, a peptide structurally related to mammalian Prl-releasing peptide (PrRP). The deduced preprohormone precursor is composed of 155 amino acid residues presenting a 87.1% similarity to chum salmon C-RFa and a 100% similarity to all fish C-RFa in the bioactive precursor motifs. C-RFa-immunoreactive perikarya and fibres were located in the brain of S. salar, especially in the hypothalamus, olfactory tract, optic tectum and cerebellum. In contrast, immunolabelled fibres were not observed in the pituitary stalk or in the hypophysis. However, interestingly, we detected immunolabelled cells in the rostral pars distalis of the pituitary in the basolateral region in which Prl is synthesized. These results were confirmed by obtaining a strong signal by using reverse transcription/polymerase chain reaction (RT-PCR) on mRNA from both hypothalamus and pituitary. These data show, for the first time, by immunohistochemistry and RT-PCR, that C-RFa is produced in pituitary cells. Finally, based on these results, a possible function for C-RFa as a locally produced PrRP in this teleost is discussed.

Hepatitis disease detection using Bayesian theory

NASA Astrophysics Data System (ADS)

Maseleno, Andino; Hidayati, Rohmah Zahroh

2017-02-01

This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

PubMed

Jones, Matt; Love, Bradley C

2011-08-01

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls

A local approach for focussed Bayesian fusion

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael; Goussev, Igor; Beyerer, Jürgen

2009-04-01

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

PubMed Central

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model.

PubMed

Jääskinen, Väinö; Parkkinen, Ville; Cheng, Lu; Corander, Jukka

2014-02-01

In many biological applications it is necessary to cluster DNA sequences into groups that represent underlying organismal units, such as named species or genera. In metagenomics this grouping needs typically to be achieved on the basis of relatively short sequences which contain different types of errors, making the use of a statistical modeling approach desirable. Here we introduce a novel method for this purpose by developing a stochastic partition model that clusters Markov chains of a given order. The model is based on a Dirichlet process prior and we use conjugate priors for the Markov chain parameters which enables an analytical expression for comparing the marginal likelihoods of any two partitions. To find a good candidate for the posterior mode in the partition space, we use a hybrid computational approach which combines the EM-algorithm with a greedy search. This is demonstrated to be faster and yield highly accurate results compared to earlier suggested clustering methods for the metagenomics application. Our model is fairly generic and could also be used for clustering of other types of sequence data for which Markov chains provide a reasonable way to compress information, as illustrated by experiments on shotgun sequence type data from an Escherichia coli strain.

Genomic adaptation of the ISA virus to Salmo salar codon usage.

PubMed

Tello, Mario; Vergara, Francisco; Spencer, Eugenio

2013-07-05

The ISA virus (ISAV) is an Orthomyxovirus whose genome encodes for at least 10 proteins. Low protein identity and lack of genetic tools have hampered the study of the molecular mechanism behind its virulence. It has been shown that viral codon usage controls several processes such as translational efficiency, folding, tuning of protein expression, antigenicity and virulence. Despite this, the possible role that adaptation to host codon usage plays in virulence and viral evolution has not been studied in ISAV. Intergenomic adaptation between viral and host genomes was calculated using the codon adaptation index score with EMBOSS software and the Kazusa database. Classification of host genes according to GeneOnthology was performed using Blast2go. A non parametric test was applied to determine the presence of significant correlations among CAI, mortality and time. Using the codon adaptation index (CAI) score, we found that the encoding genes for nucleoprotein, matrix protein M1 and antagonist of Interferon I signaling (NS1) are the ISAV genes that are more adapted to host codon usage, in agreement with their requirement for production of viral particles and inactivation of antiviral responses. Comparison to host genes showed that ISAV shares CAI values with less than 0.45% of Salmo salar genes. GeneOntology classification of host genes showed that ISAV genes share CAI values with genes from less than 3% of the host biological process, far from the 14% shown by Influenza A viruses and closer to the 5% shown by Influenza B and C. As well, we identified a positive correlation (p<0.05) between CAI values of a virus and the duration of the outbreak disease in given salmon farms, as well as a weak relationship between codon adaptation values of PB1 and the mortality rates of a set of ISA viruses. Our analysis shows that ISAV is the least adapted viral Salmo salar pathogen and Orthomyxovirus family member less adapted to host codon usage, avoiding the general behavior of

The Gaia-ESO Survey: dynamical models of flattened, rotating globular clusters

NASA Astrophysics Data System (ADS)

Jeffreson, S. M. R.; Sanders, J. L.; Evans, N. W.; Williams, A. A.; Gilmore, G. F.; Bayo, A.; Bragaglia, A.; Casey, A. R.; Flaccomio, E.; Franciosini, E.; Hourihane, A.; Jackson, R. J.; Jeffries, R. D.; Jofré, P.; Koposov, S.; Lardo, C.; Lewis, J.; Magrini, L.; Morbidelli, L.; Pancino, E.; Randich, S.; Sacco, G. G.; Worley, C. C.; Zaggia, S.

2017-08-01

We present a family of self-consistent axisymmetric rotating globular cluster models which are fitted to spectroscopic data for NGC 362, NGC 1851, NGC 2808, NGC 4372, NGC 5927 and NGC 6752 to provide constraints on their physical and kinematic properties, including their rotation signals. They are constructed by flattening Modified Plummer profiles, which have the same asymptotic behaviour as classical Plummer models, but can provide better fits to young clusters due to a slower turnover in the density profile. The models are in dynamical equilibrium as they depend solely on the action variables. We employ a fully Bayesian scheme to investigate the uncertainty in our model parameters (including mass-to-light ratios and inclination angles) and evaluate the Bayesian evidence ratio for rotating to non-rotating models. We find convincing levels of rotation only in NGC 2808. In the other clusters, there is just a hint of rotation (in particular, NGC 4372 and NGC 5927), as the data quality does not allow us to draw strong conclusions. Where rotation is present, we find that it is confined to the central regions, within radii of R ≤ 2rh. As part of this work, we have developed a novel q-Gaussian basis expansion of the line-of-sight velocity distributions, from which general models can be constructed via interpolation on the basis coefficients.

Calibrating the Planck cluster mass scale with CLASH

NASA Astrophysics Data System (ADS)

Penna-Lima, M.; Bartlett, J. G.; Rozo, E.; Melin, J.-B.; Merten, J.; Evrard, A. E.; Postman, M.; Rykoff, E.

2017-08-01

We determine the mass scale of Planck galaxy clusters using gravitational lensing mass measurements from the Cluster Lensing And Supernova survey with Hubble (CLASH). We have compared the lensing masses to the Planck Sunyaev-Zeldovich (SZ) mass proxy for 21 clusters in common, employing a Bayesian analysis to simultaneously fit an idealized CLASH selection function and the distribution between the measured observables and true cluster mass. We used a tiered analysis strategy to explicitly demonstrate the importance of priors on weak lensing mass accuracy. In the case of an assumed constant bias, bSZ, between true cluster mass, M500, and the Planck mass proxy, MPL, our analysis constrains 1-bSZ = 0.73 ± 0.10 when moderate priors on weak lensing accuracy are used, including a zero-mean Gaussian with standard deviation of 8% to account for possible bias in lensing mass estimations. Our analysis explicitly accounts for possible selection bias effects in this calibration sourced by the CLASH selection function. Our constraint on the cluster mass scale is consistent with recent results from the Weighing the Giants program and the Canadian Cluster Comparison Project. It is also consistent, at 1.34σ, with the value needed to reconcile the Planck SZ cluster counts with Planck's base ΛCDM model fit to the primary cosmic microwave background anisotropies.

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

UNIFORMLY MOST POWERFUL BAYESIAN TESTS

PubMed Central

Johnson, Valen E.

2014-01-01

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternative hypothesis, exceeds a specified threshold. Like their classical counterpart, uniformly most powerful Bayesian tests are most easily defined in one-parameter exponential family models, although extensions outside of this class are possible. The connection between uniformly most powerful tests and uniformly most powerful Bayesian tests can be used to provide an approximate calibration between p-values and Bayes factors. Finally, issues regarding the strong dependence of resulting Bayes factors and p-values on sample size are discussed. PMID:24659829

Reconstructing paleo-precipitation amounts using a terrestrial hydrologic model: Lake Titicaca and the Salar de Uyuni, Peru and Bolivia

NASA Astrophysics Data System (ADS)

Nunnery, J. A.; Baker, P. A.; Coe, M. T.; Fritz, S. C.

2010-12-01

The Peruvian/Bolivian Altiplano has provided many information-rich records bearing on the history of the South American summer monsoon (SASM), a large-scale circulation system that is responsible for much of the precipitation over the Amazon basin and the southern tropics and subtropics. Examples of these paleoclimate time series include long, drill core records from Lake Titicaca (extending back to ca. 400 Ka, Fritz et al., 2007), the long drill core record from Salar de Uyuni (> 250 Ka, Baker et al., 2001; Fritz et al., 2004), paleo-lake level records from the Salar de Uyuni (e.g. Bills et al., 2004; Placzek et al, 2006); drill core records from the Rio Desaguadero valley (Rigsby et al., 2003), and ice core records from Quelccaya, Illimani, and Sajama (Thompson et al., 2000; Ramirez et al., 2003). Several previous studies using energy and water balance models have been applied to these records in attempts to provide quantitative constraints on paleo-temperature and paleo-precipitation (e.g. Kessler, 1984; Hastenrath and Kutzbach, 1985; Cross et al, 2001; Rowe and Dunbar, 2004; Arnold, 2002; Blodgett et al., 1997). For example, Blodgett et al. concluded that high paleolake stands in the Bolivian Altiplano, dated at ca. 16,000 cal. Yr BP (Bills et al., 1994) required precipitation 20% higher than modern at temperatures 5°C colder than modern. However, their model did not take into account the major overflow from Lake Titicaca. Using the THMB hydrologic model, we show that overflow from Lake Titicaca is necessary to produce and sustain large lakes in the Salar de Uyuni basin. This hydrological connection (via the Rio Desaguadero) between the northern and southern Altiplano likely was only established about 60,000 years ago. Prior to that, there were no sustained, large and deep paleolakes on the southern Altiplano. Rather, drill core evidence indicates a very long sequence of shallow, hypersaline lakes and playas.

Bayesian methods in reliability

NASA Astrophysics Data System (ADS)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

Is probabilistic bias analysis approximately Bayesian?

PubMed Central

MacLehose, Richard F.; Gustafson, Paul

2011-01-01

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. While the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well. PMID:22157311

Associations between piscine reovirus infection and life history traits in wild-caught Atlantic salmon Salmo salar L. in Norway.

PubMed

Garseth, Ase Helen; Biering, Eirik; Aunsmo, Arnfinn

2013-10-01

Piscine Reovirus (PRV), the putative causative agent of heart and skeletal muscle inflammation (HSMI), is widely distributed in both farmed and wild Atlantic salmon (Salmo salar L.) in Norway. While HSMI is a common and commercially important disease in farmed Atlantic salmon, the presence of PRV has so far not been associated with HSMI related lesions in wild salmon. Factors associated with PRV-infection were investigated in returning Atlantic salmon captured in Norwegian rivers. A multilevel mixed-effect logistic regression model confirmed clustering within rivers and demonstrated that PRV-infection is associated with life-history, sex, catch-year and body length as a proxy for sea-age. Escaped farmed salmon (odds ratio/OR: 7.32, p<0.001) and hatchery-reared salmon (OR: 1.69 p=0.073) have higher odds of being PRV-infected than wild Atlantic salmon. Male salmon have double odds of being PRV infected compared to female salmon (OR: 2.11, p<0.001). Odds of being PRV-infected increased with body-length measured as decimetres (OR: 1.20, p=0.004). Since body length and sea-age are correlated (r=0.85 p<0.001), body length serves as a proxy for sea-age, meaning that spending more years in sea increases the odds of being PRV-infected. Copyright © 2013 The Author. Published by Elsevier B.V. All rights reserved.

Philosophy and the practice of Bayesian statistics

PubMed Central

Gelman, Andrew; Shalizi, Cosma Rohilla

2015-01-01

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. PMID:22364575

Phylogenetic relationships of the dwarf boas and a comparison of Bayesian and bootstrap measures of phylogenetic support.

PubMed

Wilcox, Thomas P; Zwickl, Derrick J; Heath, Tracy A; Hillis, David M

2002-11-01

Four New World genera of dwarf boas (Exiliboa, Trachyboa, Tropidophis, and Ungaliophis) have been placed by many systematists in a single group (traditionally called Tropidophiidae). However, the monophyly of this group has been questioned in several studies. Moreover, the overall relationships among basal snake lineages, including the placement of the dwarf boas, are poorly understood. We obtained mtDNA sequence data for 12S, 16S, and intervening tRNA-val genes from 23 species of snakes representing most major snake lineages, including all four genera of New World dwarf boas. We then examined the phylogenetic position of these species by estimating the phylogeny of the basal snakes. Our phylogenetic analysis suggests that New World dwarf boas are not monophyletic. Instead, we find Exiliboa and Ungaliophis to be most closely related to sand boas (Erycinae), boas (Boinae), and advanced snakes (Caenophidea), whereas Tropidophis and Trachyboa form an independent clade that separated relatively early in snake radiation. Our estimate of snake phylogeny differs significantly in other ways from some previous estimates of snake phylogeny. For instance, pythons do not cluster with boas and sand boas, but instead show a strong relationship with Loxocemus and Xenopeltis. Additionally, uropeltids cluster strongly with Cylindrophis, and together are embedded in what has previously been considered the macrostomatan radiation. These relationships are supported by both bootstrapping (parametric and nonparametric approaches) and Bayesian analysis, although Bayesian support values are consistently higher than those obtained from nonparametric bootstrapping. Simulations show that Bayesian support values represent much better estimates of phylogenetic accuracy than do nonparametric bootstrap support values, at least under the conditions of our study. Copyright 2002 Elsevier Science (USA)

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Bayesian Latent Class Analysis Tutorial.

PubMed

Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

2018-01-01

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

Effects of ocean acidification on salinity tolerance and seawater growth of Atlantic salmon Salmo salar smolts.

PubMed

Mccormick, S D; Regish, A M

2018-06-23

Human activity has resulted in increasing atmospheric carbon dioxide (CO 2 ), which will result in reduced pH and higher levels of CO 2 in the ocean, a process known as ocean acidification. Understanding the effects of ocean acidification (OA) on fishes will be important to predicting and mitigating its consequences. Anadromous species such as salmonids may be especially at risk because of their rapid movements between fresh water and seawater, which could minimize their ability to acclimate. In the present study, we examine the effect of future OA on the salinity tolerance and early seawater growth of Atlantic salmon Salmo salar smolts. Exposure to 61.81 Pa and 102.34 Pa CO 2 did not alter salinity tolerance but did result in slightly lower plasma chloride levels in smolts exposed to seawater compared with controls (39.59 Pa). Gill Na + -K + -ATPase activity, plasma cortisol, glucose and haematocrit after seawater exposure were not altered by elevated CO 2 . Growth rate in the first 2 weeks of seawater exposure was greater at 102.34 Pa CO 2 than under control conditions. This study of the effects of OA on S. salar during the transition from fresh water to seawater indicates that elevated CO 2 is not likely to affect osmoregulation negatively and may improve early growth in seawater. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Hierarchical Bayesian Model for Calibrating Estimates of Species Divergence Times

PubMed Central

Heath, Tracy A.

2012-01-01

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories. Both simulated and biological data are analyzed to evaluate the performance of the Dirichlet process hyperprior. Compared with fixed exponential prior densities, the hierarchical Bayesian approach results in more accurate and precise estimates of internal node ages. When this hyperprior is applied using Markov chain Monte Carlo methods, the ages of calibrated nodes are sampled from mixtures of exponential distributions and uncertainty in the values of calibration density parameters is taken into account. PMID:22334343

Conditional clustering of temporal expression profiles

PubMed Central

Wang, Ling; Montano, Monty; Rarick, Matt; Sebastiani, Paola

2008-01-01

Background Many microarray experiments produce temporal profiles in different biological conditions but common cluster techniques are not able to analyze the data conditional on the biological conditions. Results This article presents a novel technique to cluster data from time course microarray experiments performed across several experimental conditions. Our algorithm uses polynomial models to describe the gene expression patterns over time, a full Bayesian approach with proper conjugate priors to make the algorithm invariant to linear transformations, and an iterative procedure to identify genes that have a common temporal expression profile across two or more experimental conditions, and genes that have a unique temporal profile in a specific condition. Conclusion We use simulated data to evaluate the effectiveness of this new algorithm in finding the correct number of clusters and in identifying genes with common and unique profiles. We also use the algorithm to characterize the response of human T cells to stimulations of antigen-receptor signaling gene expression temporal profiles measured in six different biological conditions and we identify common and unique genes. These studies suggest that the methodology proposed here is useful in identifying and distinguishing uniquely stimulated genes from commonly stimulated genes in response to variable stimuli. Software for using this clustering method is available from the project home page. PMID:18334028

A Bayesian Nonparametric Approach to Test Equating

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2009-01-01

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…

Bayesian Model Averaging for Propensity Score Analysis

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2013-01-01

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

Bayesian models: A statistical primer for ecologists

USGS Publications Warehouse

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

PubMed

Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George

2014-01-01

Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.

Whole body-element composition of Atlantic salmon Salmo salar influenced by migration direction and life stage in three distinct populations.

PubMed

Ebel, J D; Leroux, S J; Robertson, M J; Dempson, J B

2016-11-01

Body-element content was measured for three life stages of wild Atlantic salmon Salmo salar from three distinct Newfoundland populations as individuals crossed between freshwater and marine ecosystems. Life stage explained most of the variation in observed body-element concentration whereas river of capture explained very little variation. Element composition of downstream migrating post-spawn adults (i.e. kelts) and juvenile smolts were similar and the composition of these two life stages strongly differed from adults migrating upstream to spawn. Low variation within life stages and across populations suggests that S. salar may exert rheostatic control of their body-element composition. Additionally, observed differences in trace element concentration between adults and other life stages were probably driven by the high carbon concentration in adults because abundant elements, such as carbon, can strongly influence the observed concentrations of less abundant elements. Thus, understanding variation among individuals in trace elements composition requires the measurement of more abundant elements. Changes in element concentration with ontogeny have important consequences the role of fishes in ecosystem nutrient cycling and should receive further attention. © 2016 The Fisheries Society of the British Isles.

Inferring the Growth of Massive Galaxies Using Bayesian Spectral Synthesis Modeling

NASA Astrophysics Data System (ADS)

Stillman, Coley Michael; Poremba, Megan R.; Moustakas, John

2018-01-01

The most massive galaxies in the universe are typically found at the centers of massive galaxy clusters. Studying these galaxies can provide valuable insight into the hierarchical growth of massive dark matter halos. One of the key challenges of measuring the stellar mass growth of massive galaxies is converting the measured light profiles into stellar mass. We use Prospector, a state-of-the-art Bayesian spectral synthesis modeling code, to infer the total stellar masses of a pilot sample of massive central galaxies selected from the Sloan Digital Sky Survey. We compare our stellar mass estimates to previous measurements, and present some of the quantitative diagnostics provided by Prospector.

Species-richness of the Anopheles annulipes Complex (Diptera: Culicidae) Revealed by Tree and Model-Based Allozyme Clustering Analyses

DTIC Science & Technology

2007-01-01

including tree- based methods such as the unweighted pair group method of analysis ( UPGMA ) and Neighbour-joining (NJ) (Saitou & Nei, 1987). By...based Bayesian approach and the tree-based UPGMA and NJ cluster- ing methods. The results obtained suggest that far more species occur in the An...unlikely that groups that differ by more than these levels are conspecific. Genetic distances were clustered using the UPGMA and NJ algorithms in MEGA

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Predation on stocked Atlantic salmon (Salmo salar) fry

USGS Publications Warehouse

Henderson, J.N.; Letcher, B.H.

2003-01-01

We studied predator-prey interactions between juvenile Atlantic salmon (Salmo salar) and trout in three Massachusetts, U.S.A., streams and in artificial streams. We sampled stomach contents of age-1+ and older salmon and trout (Salvelinus fontinalis, Salmo trutta) following salmon fry stocking in the spring of 1997 and 1998. Between 4.3 and 48.6% of the stocked fry were consumed within the first 2 days after stocking, and total fry mortality from predation varied from 4.3 to 60.7%. No significant differences were found between stomach weights of predators (without fry weight) that consumed fry and those that did not. Artificial stream experiments testing effects of habitat complexity and predator species on predator consumption rates revealed that consumption rates were not different between brook (S. fontinalis) and brown (S. trutta) trout (p = 0.59). Predation rate tended to decrease as the percentage of riffle habitat increased but the decrease was not significant (p = 0.22). Our results indicate that predation on stocked Atlantic salmon fry can be substantial (up to 60%), appears to be short lived (2 days), and is not related in a simple way to abiotic and biotic factors.

Migration and survival of Atlantic salmon Salmo salar smolts in a large natural lake.

PubMed

Kennedy, R J; Rosell, R; Millane, M; Doherty, D; Allen, M

2018-06-08

An investigation with acoustic telemetry of the passage of Salmo salar smolts through a large natural lake found heavy mortality occurred at the river-to-lake confluences (mean 31 . 2 % km -1 ), but was lower in the main body of the lake (mean 2 . 4 % km -1 ). Predation was a significant pressure on emigrating smolts as tagged pike Esox lucius aggregated at river-to-lake confluences during the peak of the smolt run. Tagged smolts mainly emmigrated into the lake in the late evening after dusk, possibly as a predator-avoidance behaviour. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Fillet quality and processing attributes of postsmolt Atlantic salmon, Salmo salar, fed a fishmeal-free diet and a fishmeal-based diet in recirculation aquaculture systems

USDA-ARS?s Scientific Manuscript database

Many studies have evaluated the adequacy of alternate ingredient diets for Atlantic salmon, Salmo salar, mainly with focus on fish performance and health; however, comprehensive analysis of fillet quality is lacking, particularly for salmon fed these diets in recirculation aquaculture systems (RAS)....

Philosophy and the practice of Bayesian statistics.

PubMed

Gelman, Andrew; Shalizi, Cosma Rohilla

2013-02-01

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.

Bayesian modeling of flexible cognitive control

PubMed Central

Jiang, Jiefeng; Heller, Katherine; Egner, Tobias

2014-01-01

“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. PMID:24929218

On the blind use of statistical tools in the analysis of globular cluster stars

NASA Astrophysics Data System (ADS)

D'Antona, Francesca; Caloi, Vittoria; Tailo, Marco

2018-04-01

As with most data analysis methods, the Bayesian method must be handled with care. We show that its application to determine stellar evolution parameters within globular clusters can lead to paradoxical results if used without the necessary precautions. This is a cautionary tale on the use of statistical tools for big data analysis.

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

NASA Astrophysics Data System (ADS)

Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.

2015-03-01

We present Π4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

Bayesian learning

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Searching Algorithm Using Bayesian Updates

ERIC Educational Resources Information Center

Caudle, Kyle

2010-01-01

In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…

Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation

PubMed Central

Froyen, Vicky; Feldman, Jacob; Singh, Manish

2015-01-01

We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian Hierarchical Grouping (BHG). In BHG we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are “owned” by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz. PMID:26322548

COMplementary Primer ASymmetric PCR (COMPAS-PCR) Applied to the Identification of Salmo salar, Salmo trutta and Their Hybrids

PubMed Central

2016-01-01

Avoiding complementarity between primers when designing a PCR assay constitutes a central rule strongly anchored in the mind of the molecular scientist. 3’-complementarity will extend the primers during PCR elongation using one another as template, consequently disabling further possible involvement in traditional target amplification. However, a 5’-complementarity will leave the primers unchanged during PCR cycles, albeit sequestered to one another, therefore also suppressing target amplification. We show that 5’-complementarity between primers may be exploited in a new PCR method called COMplementary-Primer-Asymmetric (COMPAS)-PCR, using asymmetric primer concentrations to achieve target PCR amplification. Moreover, such a design may paradoxically reduce spurious non-target amplification by actively sequestering the limiting primer. The general principles were demonstrated using 5S rDNA direct repeats as target sequences to design a species-specific assay for identifying Salmo salar and Salmo trutta using almost fully complementary primers overlapping the same target sequence. Specificity was enhanced by using 3’-penultimate point mutations and the assay was further developed to enable identification of S. salar x S. trutta hybrids by High Resolution Melt analysis in a 35 min one-tube assay. This small paradigm shift, using highly complementary primers for PCR, should help develop robust assays that previously would not be considered. PMID:27783658

Genomic adaptation of the ISA virus to Salmo salar codon usage

PubMed Central

2013-01-01

Background The ISA virus (ISAV) is an Orthomyxovirus whose genome encodes for at least 10 proteins. Low protein identity and lack of genetic tools have hampered the study of the molecular mechanism behind its virulence. It has been shown that viral codon usage controls several processes such as translational efficiency, folding, tuning of protein expression, antigenicity and virulence. Despite this, the possible role that adaptation to host codon usage plays in virulence and viral evolution has not been studied in ISAV. Methods Intergenomic adaptation between viral and host genomes was calculated using the codon adaptation index score with EMBOSS software and the Kazusa database. Classification of host genes according to GeneOnthology was performed using Blast2go. A non parametric test was applied to determine the presence of significant correlations among CAI, mortality and time. Results Using the codon adaptation index (CAI) score, we found that the encoding genes for nucleoprotein, matrix protein M1 and antagonist of Interferon I signaling (NS1) are the ISAV genes that are more adapted to host codon usage, in agreement with their requirement for production of viral particles and inactivation of antiviral responses. Comparison to host genes showed that ISAV shares CAI values with less than 0.45% of Salmo salar genes. GeneOntology classification of host genes showed that ISAV genes share CAI values with genes from less than 3% of the host biological process, far from the 14% shown by Influenza A viruses and closer to the 5% shown by Influenza B and C. As well, we identified a positive correlation (p<0.05) between CAI values of a virus and the duration of the outbreak disease in given salmon farms, as well as a weak relationship between codon adaptation values of PB1 and the mortality rates of a set of ISA viruses. Conclusions Our analysis shows that ISAV is the least adapted viral Salmo salar pathogen and Orthomyxovirus family member less adapted to host codon

Bayesian just-so stories in psychology and neuroscience.

PubMed

Bowers, Jeffrey S; Davis, Colin J

2012-05-01

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian "just so" stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal. 2012 APA, all rights reserved.

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

PubMed

Baldacchino, Tara; Jacobs, William R; Anderson, Sean R; Worden, Keith; Rowson, Jennifer

2018-01-01

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Analysis of Spectral-type A/B Stars in Five Open Clusters

NASA Astrophysics Data System (ADS)

Wilhelm, Ronald J.; Rafuil Islam, M.

2014-01-01

We have obtained low resolution (R = 1000) spectroscopy of N=68, spectral-type A/B stars in five nearby open star clusters using the McDonald Observatory, 2.1m telescope. The sample of blue stars in various clusters were selected to test our new technique for determining interstellar reddening and distances in areas where interstellar reddening is high. We use a Bayesian approach to find the posterior distribution for Teff, Logg and [Fe/H] from a combination of reddened, photometric colors and spectroscopic line strengths. We will present calibration results for this technique using open cluster star data with known reddening and distances. Preliminary results suggest our technique can produce both reddening and distance determinations to within 10% of cluster values. Our technique opens the possibility of determining distances for blue stars at low Galactic latitudes where extinction can be large and differential. We will also compare our stellar parameter determinations to previously reported MK spectral classifications and discuss the probability that some of our stars are not members of their reported clusters.

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.

PubMed

Kolehmainen, Ville; Vanne, Antti; Siltanen, Samuli; Järvenpää, Seppo; Kaipio, Jari P; Lassas, Matti; Kalke, Martti

2006-02-01

Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.

Bayesian structural equation modeling in sport and exercise psychology.

PubMed

Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus

2015-08-01

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

Advances in Significance Testing for Cluster Detection

NASA Astrophysics Data System (ADS)

Coleman, Deidra Andrea

surveillance data while controlling the Bayesian False Discovery Rate (BFDR). The procedure entails choosing an appropriate Bayesian model that captures the spatial dependency inherent in epidemiological data and considers all days of interest, selecting a test statistic based on a chosen measure that provides the magnitude of the maximumal spatial cluster for each day, and identifying a cutoff value that controls the BFDR for rejecting the collective null hypothesis of no outbreak over a collection of days for a specified region.We use our procedure to analyze botulism-like syndrome data collected by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

A default Bayesian hypothesis test for mediation.

PubMed

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

Free will in Bayesian and inverse Bayesian inference-driven endo-consciousness.

PubMed

Gunji, Yukio-Pegio; Minoura, Mai; Kojima, Kei; Horry, Yoichi

2017-12-01

How can we link challenging issues related to consciousness and/or qualia with natural science? The introduction of endo-perspective, instead of exo-perspective, as proposed by Matsuno, Rössler, and Gunji, is considered one of the most promising candidate approaches. Here, we distinguish the endo-from the exo-perspective in terms of whether the external is or is not directly operated. In the endo-perspective, the external can be neither perceived nor recognized directly; rather, one can only indirectly summon something outside of the perspective, which can be illustrated by a causation-reversal pair. On one hand, causation logically proceeds from the cause to the effect. On the other hand, a reversal from the effect to the cause is non-logical and is equipped with a metaphorical structure. We argue that the differences in exo- and endo-perspectives result not from the difference between Western and Eastern cultures, but from differences between modernism and animism. Here, a causation-reversal pair described using a pair of upward (from premise to consequence) and downward (from consequence to premise) causation and a pair of Bayesian and inverse Bayesian inference (BIB inference). Accordingly, the notion of endo-consciousness is proposed as an agent equipped with BIB inference. We also argue that BIB inference can yield both highly efficient computations through Bayesian interference and robust computations through inverse Bayesian inference. By adapting a logical model of the free will theorem to the BIB inference, we show that endo-consciousness can explain free will as a regression of the controllability of voluntary action. Copyright © 2017. Published by Elsevier Ltd.

Application of Bayesian Approach in Cancer Clinical Trial

PubMed Central

Bhattacharjee, Atanu

2014-01-01

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques. PMID:29147387

Bayesian least squares deconvolution

NASA Astrophysics Data System (ADS)

Asensio Ramos, A.; Petit, P.

2015-11-01

Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

A guide to Bayesian model selection for ecologists

USGS Publications Warehouse

Hooten, Mevin B.; Hobbs, N.T.

2015-01-01

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

Universal Darwinism As a Process of Bayesian Inference.

PubMed

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

Daniel Goodman’s empirical approach to Bayesian statistics

USGS Publications Warehouse

Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

2016-01-01

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

Bayesian Inference: with ecological applications

USGS Publications Warehouse

Link, William A.; Barker, Richard J.

2010-01-01

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Bayesian statistics in medicine: a 25 year review.

PubMed

Ashby, Deborah

2006-11-15

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

PubMed

Hensman, James; Lawrence, Neil D; Rattray, Magnus

2013-08-20

Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

Optical characterization limits of nanoparticle aggregates at different wavelengths using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Eriçok, Ozan Burak; Ertürk, Hakan

2018-07-01

Optical characterization of nanoparticle aggregates is a complex inverse problem that can be solved by deterministic or statistical methods. Previous studies showed that there exists a different lower size limit of reliable characterization, corresponding to the wavelength of light source used. In this study, these characterization limits are determined considering a light source wavelength range changing from ultraviolet to near infrared (266-1064 nm) relying on numerical light scattering experiments. Two different measurement ensembles are considered. Collection of well separated aggregates made up of same sized particles and that of having particle size distribution. Filippov's cluster-cluster algorithm is used to generate the aggregates and the light scattering behavior is calculated by discrete dipole approximation. A likelihood-free Approximate Bayesian Computation, relying on Adaptive Population Monte Carlo method, is used for characterization. It is found that when the wavelength range of 266-1064 nm is used, successful characterization limit changes from 21-62 nm effective radius for monodisperse and polydisperse soot aggregates.

Effects of feeding regimes and early maturation on migratory behaviour of landlocked hatchery-reared Atlantic salmon Salmo salar smolts.

PubMed

Norrgård, J R; Bergman, E; Schmitz, M; Greenberg, L A

2014-10-01

The migratory behaviour of hatchery-reared landlocked Atlantic salmon Salmo salar raised under three different feeding regimes was monitored through the lower part of the River Klarälven, Sweden. The smolts were implanted with acoustic transmitters and released into the River Klarälven, 25 km upstream of the outlet in Lake Vänern. Early mature males, which had matured the previous autumn, were also tagged and released. To monitor migration of the fish, acoustic receivers were deployed along the migratory route. The proportion of S. salar that reached Lake Vänern was significantly greater for fish fed fat-reduced feed than for fish given rations with higher fat content, regardless of ration size. Fish from the early mature male group remained in the river to a greater extent than fish from the three feeding regimes. Smolt status (degree of silvering), as visually assessed, did not differ among the feeding regime groups, and moreover, fully-silvered fish, regardless of feeding regime, migrated faster and had a greater migration success than fish with less developed smolt characteristics. Also, successful migrants had a lower condition factor than unsuccessful ones. These results indicate that the migration success of hatchery-reared S. smolts released to the wild can be enhanced by relatively simple changes in feeding regimes and by matching stocking time with smolt development. © 2014 The Fisheries Society of the British Isles.

Stress response of Salmo salar (Linnaeus 1758) facing low abundance infestation of Caligus rogercresseyi (Boxshall & Bravo 2000), an object in the tank, and handling.

PubMed

González Gómez, M P; Marín Arribas, S L; Vargas-Chacoff, L

2016-07-01

This study looks at how low infestation loads of adult Caligus rogercresseyi and other stressors affect the physiology of Salmo salar. Experimental fish groups were with (infested) or without (control) exposure to the parasite. The parasite cohort was followed for 78 days post-infestation (dpi), and only adult lice were observed. Additional stressors were applied at 60 and 75 dpi. The analysis included measurements of fish physiology and weight. Low-level infestations by adult C. rogercresseyi for more than 50 dpi induced moderate stress in S. salar as well as a high energy demand and increased small skin mucous cells. Threshold lice loads were identified, and above those loads, a high stress response was observed. Additional stressors altered fish physiology, inducing downregulation of the cortisol response after the first stressor and upregulation after the second stressor, but infested fish responded more strongly. Parasitism by C. rogercresseyi is energetically demanding, affecting the primary and secondary responses (e.g. cortisol and glucose levels), as well as the tertiary response (fish weight). © 2015 John Wiley & Sons Ltd.

ISA virus regulates the generation of reactive oxygen species and p47phox expression in a p38 MAPK-dependent manner in Salmo salar.

PubMed

Olavarría, Víctor H; Valdivia, Sharin; Salas, Boris; Villalba, Melina; Sandoval, Rodrigo; Oliva, Harold; Valdebenito, Samuel; Yañez, Alejandro

2015-02-01

Several viruses, including Orthomyxovirus, utilize cellular reactive oxygen species (ROS) for viral genomic replication and survival within host cells. However, the role of ROS in early events of viral entry and signal induction has not been elucidated. Here, we show that ISA virus (ISAV) induces ROS production very early during infection of CHSE-214 and SHK-1Ycells, and that production is sustained over the observed 24h post-infection. The mitogen-activated protein kinase (MAPK) family is responsible for important signaling pathways. In this study, we report that ISAV activates ERK and p38 in Salmo salar. In salmonid macrophages, while ERK was required for SOD, GLURED, p47phox expression, p38 regulated the ROS production by the NADPH oxidase complex activation. These results, together with the presence of several consensus target motifs for p38 MAPK in the promoter of the S. salar p47phox gene, suggest that p38 MAPK regulates p47phox gene expression in fish through the activation of this key transcription factor. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bayesian Just-So Stories in Psychology and Neuroscience

ERIC Educational Resources Information Center

Bowers, Jeffrey S.; Davis, Colin J.

2012-01-01

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…

Universal Darwinism As a Process of Bayesian Inference

PubMed Central

Campbell, John O.

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

The Bayesian Revolution Approaches Psychological Development

ERIC Educational Resources Information Center

Shultz, Thomas R.

2007-01-01

This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…

[Intracellular Protein Degradation in Growth of Atlantic Salmon, Salmo salar L].

PubMed

Lysenko, L A; Kantserova, N P; Krupnova, M Yu; Veselov, A E; Nemova, N N

2015-01-01

A brief review on the common characteristics and specific features of proteolytic machinery in fish skeletal muscles (based on Atlantic salmon, Salmo salar L., Salmonidae) has been given. Among a variety of proteases in the muscle tissue, those determining protein degradation level in developing and intensively growing muscles in salmon young and by this way regulating protein retention intensity and growth at all namely lysosomal cathepsins B and D and calcium-dependent proteases (calpains) were comprehensively studied. Revealed age-related differences in intracellular protease activity in salmon skeletal muscles indicate the role of proteolysis regulation in growth in general and a specific role of the individual proteolytic enzymes in particular. The data on negative correlation of cathepsin D and calpain activity levels in muscles and the rate of weight increase in juvenile salmon were obtained. A revealed positive correlation of cathepsin B activity and morphometric parameters in fish young presumably indicates its primary contribution to non-myofibrillar protein turnover.

New immunomodulatory role of neuropeptide Y (NPY) in Salmo salar leucocytes.

PubMed

González-Stegmaier, Roxana; Villarroel-Espíndola, Franz; Manríquez, René; López, Mauricio; Monrás, Mónica; Figueroa, Jaime; Enríquez, Ricardo; Romero, Alex

2017-11-01

Neuropeptide Y (NPY) plays different roles in mammals such as: regulate food intake, memory retention, cardiovascular functions, and anxiety. It has also been shown in the modulation of chemotaxis, T lymphocyte differentiation, and leukocyte migration. In fish, NPY expression and functions have been studied but its immunomodulatory role remains undescribed. This study confirmed the expression and synthesis of NPY in S. salar under inflammation, and validated a commercial antibody for NPY detection in teleost. Additionally, immunomodulatory effects of NPY were assayed in vitro and in vivo. Phagocytosis and superoxide anion production in leukocytes and SHK cells were induced under stimulation with a synthetic peptide. IL-8 mRNA was selectively and strongly induced in the spleen, head kidney, and isolated cells, after in vivo challenge with NPY. All together suggest that NPY is expressed in immune tissues and modulates the immune response in teleost fish. Copyright © 2017 Elsevier Ltd. All rights reserved.

Weighted community detection and data clustering using message passing

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Yanchen; Zhang, Pan

2018-03-01

Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

PubMed

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

ERIC Educational Resources Information Center

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A. G.

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are…

Oxygen consumption, ammonia excretion and protein use in response to thermal changes in juvenile Atlantic salmon Salmo salar.

PubMed

Kieffer, J D; Wakefield, A M

2009-02-01

Experiments were designed to examine the effects of various temperature challenges on oxygen consumption and ammonia excretion rates and protein utilization in juvenile Atlantic salmon Salmo salar. Fish acclimated to 15 degrees C were acutely and abruptly exposed to either 20 or 25 degrees C for a period of 3 h. To simulate a more environmentally relevant temperature challenge, a third group of fish was exposed to a gradual increase in temperature from 15 to 20 degrees C over a period of 3 h (c. 1.7 degrees C h(-1)). Oxygen consumption and ammonia excretion rates were monitored before, during and after the temperature shift. From the ammonia excretion and oxygen consumption rates, protein utilization rates were calculated. Acute temperature changes (15-20 degrees C or 15-25 degrees C) caused large and immediate increases in the oxygen consumption rates. When the temperature was gradually changed (i.e. 1.7 degrees C h(-1)), however, the rates of oxygen consumption and ammonia excretion were only marginally altered. When fish were exposed to warmer temperatures (i.e. 15-20 degrees C or 15-25 degrees C) protein use generally remained at pre-exposure (15 degrees C) levels. A rapid transfer back to 15 degrees C (20-15 degrees C or 25-15 degrees C) generally increased protein use in S. salar. These results indicate that both the magnitude and the rate of temperature change are important in describing the physiological response in juvenile salmonids.

Bayesian ensemble refinement by replica simulations and reweighting.

PubMed

Hummer, Gerhard; Köfinger, Jürgen

2015-12-28

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Bayesian ensemble refinement by replica simulations and reweighting

NASA Astrophysics Data System (ADS)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Modeling Diagnostic Assessments with Bayesian Networks

ERIC Educational Resources Information Center

Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

2007-01-01

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

Quantum-Like Representation of Non-Bayesian Inference

NASA Astrophysics Data System (ADS)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

Bayesian analysis of rare events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

THE DYNAMICS OF MERGING CLUSTERS: A MONTE CARLO SOLUTION APPLIED TO THE BULLET AND MUSKET BALL CLUSTERS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dawson, William A., E-mail: wadawson@ucdavis.edu

2013-08-01

Merging galaxy clusters have become one of the most important probes of dark matter, providing evidence for dark matter over modified gravity and even constraints on the dark matter self-interaction cross-section. To properly constrain the dark matter cross-section it is necessary to understand the dynamics of the merger, as the inferred cross-section is a function of both the velocity of the collision and the observed time since collision. While the best understanding of merging system dynamics comes from N-body simulations, these are computationally intensive and often explore only a limited volume of the merger phase space allowed by observed parametermore » uncertainty. Simple analytic models exist but the assumptions of these methods invalidate their results near the collision time, plus error propagation of the highly correlated merger parameters is unfeasible. To address these weaknesses I develop a Monte Carlo method to discern the properties of dissociative mergers and propagate the uncertainty of the measured cluster parameters in an accurate and Bayesian manner. I introduce this method, verify it against an existing hydrodynamic N-body simulation, and apply it to two known dissociative mergers: 1ES 0657-558 (Bullet Cluster) and DLSCL J0916.2+2951 (Musket Ball Cluster). I find that this method surpasses existing analytic models-providing accurate (10% level) dynamic parameter and uncertainty estimates throughout the merger history. This, coupled with minimal required a priori information (subcluster mass, redshift, and projected separation) and relatively fast computation ({approx}6 CPU hours), makes this method ideal for large samples of dissociative merging clusters.« less

The effects of long-term 20 mg/L carbon dioxide exposure on the health and performance of Atlantic salmon Salmo salar post-smolts in water recirculation aquaculture systems

USDA-ARS?s Scientific Manuscript database

Previous research and experience has linked elevated dissolved carbon dioxide (CO2) to reduced growth performance, poor feed conversion, and a variety of health issues in farm-raised fish, including Atlantic salmon Salmo salar. Supplemental control measures in water recirculation aquaculture systems...

A program for the Bayesian Neural Network in the ROOT framework

NASA Astrophysics Data System (ADS)

Zhong, Jiahang; Huang, Run-Sheng; Lee, Shih-Chang

2011-12-01

We present a Bayesian Neural Network algorithm implemented in the TMVA package (Hoecker et al., 2007 [1]), within the ROOT framework (Brun and Rademakers, 1997 [2]). Comparing to the conventional utilization of Neural Network as discriminator, this new implementation has more advantages as a non-parametric regression tool, particularly for fitting probabilities. It provides functionalities including cost function selection, complexity control and uncertainty estimation. An example of such application in High Energy Physics is shown. The algorithm is available with ROOT release later than 5.29. Program summaryProgram title: TMVA-BNN Catalogue identifier: AEJX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: BSD license No. of lines in distributed program, including test data, etc.: 5094 No. of bytes in distributed program, including test data, etc.: 1,320,987 Distribution format: tar.gz Programming language: C++ Computer: Any computer system or cluster with C++ compiler and UNIX-like operating system Operating system: Most UNIX/Linux systems. The application programs were thoroughly tested under Fedora and Scientific Linux CERN. Classification: 11.9 External routines: ROOT package version 5.29 or higher ( http://root.cern.ch) Nature of problem: Non-parametric fitting of multivariate distributions Solution method: An implementation of Neural Network following the Bayesian statistical interpretation. Uses Laplace approximation for the Bayesian marginalizations. Provides the functionalities of automatic complexity control and uncertainty estimation. Running time: Time consumption for the training depends substantially on the size of input sample, the NN topology, the number of training iterations, etc. For the example in this manuscript, about 7 min was used on a PC/Linux with 2.0 GHz processors.

WebMOTIFS: automated discovery, filtering and scoring of DNA sequence motifs using multiple programs and Bayesian approaches

PubMed Central

Romer, Katherine A.; Kayombya, Guy-Richard; Fraenkel, Ernest

2007-01-01

WebMOTIFS provides a web interface that facilitates the discovery and analysis of DNA-sequence motifs. Several studies have shown that the accuracy of motif discovery can be significantly improved by using multiple de novo motif discovery programs and using randomized control calculations to identify the most significant motifs or by using Bayesian approaches. WebMOTIFS makes it easy to apply these strategies. Using a single submission form, users can run several motif discovery programs and score, cluster and visualize the results. In addition, the Bayesian motif discovery program THEME can be used to determine the class of transcription factors that is most likely to regulate a set of sequences. Input can be provided as a list of gene or probe identifiers. Used with the default settings, WebMOTIFS accurately identifies biologically relevant motifs from diverse data in several species. WebMOTIFS is freely available at http://fraenkel.mit.edu/webmotifs. PMID:17584794

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Non-Bayesian Optical Inference Machines

NASA Astrophysics Data System (ADS)

Kadar, Ivan; Eichmann, George

1987-01-01

In a recent paper, Eichmann and Caulfield) presented a preliminary exposition of optical learning machines suited for use in expert systems. In this paper, we extend the previous ideas by introducing learning as a means of reinforcement by information gathering and reasoning with uncertainty in a non-Bayesian framework2. More specifically, the non-Bayesian approach allows the representation of total ignorance (not knowing) as opposed to assuming equally likely prior distributions.

Resolution analysis of marine seismic full waveform data by Bayesian inversion

NASA Astrophysics Data System (ADS)

Ray, A.; Sekar, A.; Hoversten, G. M.; Albertin, U.

2015-12-01

The Bayesian posterior density function (PDF) of earth models that fit full waveform seismic data convey information on the uncertainty with which the elastic model parameters are resolved. In this work, we apply the trans-dimensional reversible jump Markov Chain Monte Carlo method (RJ-MCMC) for the 1D inversion of noisy synthetic full-waveform seismic data in the frequency-wavenumber domain. While seismic full waveform inversion (FWI) is a powerful method for characterizing subsurface elastic parameters, the uncertainty in the inverted models has remained poorly known, if at all and is highly initial model dependent. The Bayesian method we use is trans-dimensional in that the number of model layers is not fixed, and flexible such that the layer boundaries are free to move around. The resulting parameterization does not require regularization to stabilize the inversion. Depth resolution is traded off with the number of layers, providing an estimate of uncertainty in elastic parameters (compressional and shear velocities Vp and Vs as well as density) with depth. We find that in the absence of additional constraints, Bayesian inversion can result in a wide range of posterior PDFs on Vp, Vs and density. These PDFs range from being clustered around the true model, to those that contain little resolution of any particular features other than those in the near surface, depending on the particular data and target geometry. We present results for a suite of different frequencies and offset ranges, examining the differences in the posterior model densities thus derived. Though these results are for a 1D earth, they are applicable to areas with simple, layered geology and provide valuable insight into the resolving capabilities of FWI, as well as highlight the challenges in solving a highly non-linear problem. The RJ-MCMC method also presents a tantalizing possibility for extension to 2D and 3D Bayesian inversion of full waveform seismic data in the future, as it objectively

Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations.

PubMed

Jolani, Shahab

2018-03-01

In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain valid inferences in the presence of missing data. However, MI of clustered data such as multicenter studies and individual participant data meta-analysis requires advanced imputation routines that preserve the hierarchical structure of data. In clustered data, a specific challenge is the presence of systematically missing data, when a variable is completely missing in some clusters, and sporadically missing data, when it is partly missing in some clusters. Unfortunately, little is known about how to perform MI when both types of missing data occur simultaneously. We develop a new class of hierarchical imputation approach based on chained equations methodology that simultaneously imputes systematically and sporadically missing data while allowing for arbitrary patterns of missingness among them. Here, we use a random effect imputation model and adopt a simplification over fully Bayesian techniques such as Gibbs sampler to directly obtain draws of parameters within each step of the chained equations. We justify through theoretical arguments and extensive simulation studies that the proposed imputation methodology has good statistical properties in terms of bias and coverage rates of parameter estimates. An illustration is given in a case study with eight individual participant datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iliadis, C.; Anderson, K. S.; Coc, A.

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We presentmore » astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.« less

Bayesian Exploratory Factor Analysis

PubMed Central

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

2014-01-01

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

Prior approval: the growth of Bayesian methods in psychology.

PubMed

Andrews, Mark; Baguley, Thom

2013-02-01

Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

PubMed

Li, Yan; Dong, Zigang

2016-06-27

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

A Primer on Bayesian Analysis for Experimental Psychopathologists

PubMed Central

Krypotos, Angelos-Miltiadis; Blanken, Tessa F.; Arnaudova, Inna; Matzke, Dora; Beckers, Tom

2016-01-01

The principal goals of experimental psychopathology (EPP) research are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of null-hypothesis significance testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research. PMID:28748068

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

Enhancements to the Bayesian Infrasound Source Location Method

DTIC Science & Technology

2012-09-01

ENHANCEMENTS TO THE BAYESIAN INFRASOUND SOURCE LOCATION METHOD Omar E. Marcillo, Stephen J. Arrowsmith, Rod W. Whitaker, and Dale N. Anderson Los...ABSTRACT We report on R&D that is enabling enhancements to the Bayesian Infrasound Source Location (BISL) method for infrasound event location...the Bayesian Infrasound Source Location Method 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Fundamentals and Recent Developments in Approximate Bayesian Computation

PubMed Central

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka

2017-01-01

Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.] PMID:28175922

Molecular responses to toxicological stressors: profiling microRNAs in wild Atlantic salmon (Salmo salar) exposed to acidic aluminum-rich water.

PubMed

Kure, Elin H; Sæbø, Mona; Stangeland, Astrid M; Hamfjord, Julian; Hytterød, Sigurd; Heggenes, Jan; Lydersen, Espen

2013-08-15

Atlantic salmon (Salmo salar) is among the most sensitive organisms toward acidic, aluminum exposure. Main documented responses to this type of stress are a combination of hypoxia and loss of blood plasma ions. Physiological responses to stress in fish are often grouped into primary, secondary and tertiary responses, where the above mentioned effects are secondary responses, while primary responses include endocrine changes as measurable levels of catecholamines and corticosteroids. In this study we have exposed young (14 months) Atlantic salmon to acid/Al water (pH ≈ 5.6, Al(i) ≈ 80 μg L⁻¹) for 3 days, and obtained clear and consistent decrease of Na⁺ and Cl⁻ ions, and increases of glucose in blood plasma, hematocrit and P(CO₂) in blood. We did not measure plasma cortisol (primary response compound), but analyzed effects on microRNA level (miRNA) in muscle tissue, as this may represent initial markers of primary stress responses. miRNAs regulate diverse biological processes, many are evolutionarily conserved, and hundreds have been identified in various animals, although only in a few fish species. We used a novel high-throughput sequencing (RNA-Seq) method to identify miRNAs in Atlantic salmon and specific miRNAs as potential early markers for stress. A total of 18 miRNAs were significantly differentially expressed (FDR<0.1) in exposed compared to control fish, four down-regulated and 14 up-regulated. An unsupervised hierarchical clustering of significant miRNAs revealed two clusters representing exposed and non-exposed individuals. Utilizing the genome of the zebrafish and bioinformatic tools, we identified 224 unique miRNAs in the Atlantic salmon samples sequenced. Additional laboratory studies focusing on function, stress dose-responses and temporal expression of the identified miRNAs will facilitate their use as initial markers for stress responses. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Bayesian coronal seismology

NASA Astrophysics Data System (ADS)

Arregui, Iñigo

2018-01-01

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

Draft Genome Sequence of the Fish Pathogen Yersinia ruckeri Strain 37551, Serotype O1b, Isolated from Diseased, Vaccinated Atlantic Salmon (Salmo salar) in Chile

PubMed Central

Navas, Esteban; Bohle, Harry; Henríquez, Patricio; Grothusen, Horst; Bustamante, Fernando; Bustos, Patricio

2014-01-01

We sequenced the genome of a motile O1b Yersinia ruckeri field isolate from Chile, which is causing enteric redmouth disease (ERM) in vaccinated Atlantic salmon (Salmo salar). The draft genome has 3,775,486 bp, a G+C content of 47.1%, and is predicted to contain 3,406 coding sequences. PMID:25169862

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

NASA Astrophysics Data System (ADS)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

The Bayesian boom: good thing or bad?

PubMed Central

Hahn, Ulrike

2014-01-01

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition. PMID:25152738

MODEL-FREE MULTI-PROBE LENSING RECONSTRUCTION OF CLUSTER MASS PROFILES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Umetsu, Keiichi

2013-05-20

Lens magnification by galaxy clusters induces characteristic spatial variations in the number counts of background sources, amplifying their observed fluxes and expanding the area of sky, the net effect of which, known as magnification bias, depends on the intrinsic faint-end slope of the source luminosity function. The bias is strongly negative for red galaxies, dominated by the geometric area distortion, whereas it is mildly positive for blue galaxies, enhancing the blue counts toward the cluster center. We generalize the Bayesian approach of Umetsu et al. for reconstructing projected cluster mass profiles, by incorporating multiple populations of background sources for magnification-biasmore » measurements and combining them with complementary lens-distortion measurements, effectively breaking the mass-sheet degeneracy and improving the statistical precision of cluster mass measurements. The approach can be further extended to include strong-lensing projected mass estimates, thus allowing for non-parametric absolute mass determinations in both the weak and strong regimes. We apply this method to our recent CLASH lensing measurements of MACS J1206.2-0847, and demonstrate how combining multi-probe lensing constraints can improve the reconstruction of cluster mass profiles. This method will also be useful for a stacked lensing analysis, combining all lensing-related effects in the cluster regime, for a definitive determination of the averaged mass profile.« less

A SAS Interface for Bayesian Analysis with WinBUGS

ERIC Educational Resources Information Center

Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki

2008-01-01

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…

The Development of Bayesian Theory and Its Applications in Business and Bioinformatics

NASA Astrophysics Data System (ADS)

Zhang, Yifei

2018-03-01

Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.

Advances in Bayesian Modeling in Educational Research

ERIC Educational Resources Information Center

Levy, Roy

2016-01-01

In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

Bayesian Statistics for Biological Data: Pedigree Analysis

ERIC Educational Resources Information Center

Stanfield, William D.; Carlton, Matthew A.

2004-01-01

The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT.

PubMed

Carlis, John; Bruso, Kelsey

2012-03-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n(2)) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing.

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT

PubMed Central

Bruso, Kelsey

2012-01-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n2) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing. PMID:22773923

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Classifying emotion in Twitter using Bayesian network

NASA Astrophysics Data System (ADS)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Development of intestinal ion-transporting mechanisms during smoltification and seawater acclimation in Atlantic salmon Salmo salar

USGS Publications Warehouse

Sundh, Henrik; Nilsen, Tom O.; Lindström, Jenny; Hasselberg-Frank, Linda; Stefansson, Sigurd O.; McCormick, Stephen D.; Sundell, K.

2014-01-01

This study investigated the expression of ion transporters involved in intestinal fluid absorption and presents evidence for developmental changes in abundance and tissue distribution of these transporters during smoltification and seawater (SW) acclimation of Atlantic salmonSalmo salar. Emphasis was placed on Na+, K+-ATPase (NKA) and Na+, K+, Cl− co-transporter (NKCC) isoforms, at both transcriptional and protein levels, together with transcription of chloride channel genes. The nka α1c was the dominant isoform at the transcript level in both proximal and distal intestines; also, it was the most abundant isoform expressed in the basolateral membrane of enterocytes in the proximal intestine. This isoform was also abundantly expressed in the distal intestine in the lower part of the mucosal folds. The protein expression of intestinal Nkaα1c increased during smoltification. Immunostaining was localized to the basal membrane of the enterocytes in freshwater (FW) fish, and re-distributed to a lateral position after SW entry. Two other Nka isoforms, α1a and α1b, were expressed in the intestine but were not regulated to the same extent during smoltification and subsequent SW transfer. Their localization in the intestinal wall indicates a house-keeping function in excitatory tissues. The absorptive form of the NKCC-like isoform (sub-apically located NKCC2 and/or Na+, Cl−co-transporter) increased during smoltification and further after SW transfer. The cellular distribution changed from a diffuse expression in the sub-apical regions during smoltification to clustering of the transporters closer to the apical membrane after entry to SW. Furthermore, transcript abundance indicates that the mechanisms necessary for exit of chloride ions across the basolateral membrane and into the lateral intercellular space are present in the form of one or more of three different chloride channels: cystic fibrosis transmembrane conductance regulator I and II and chloride channel

Hierarchical Bayesian modeling of heterogeneous variances in average daily weight gain of commercial feedlot cattle.

PubMed

Cernicchiaro, N; Renter, D G; Xiang, S; White, B J; Bello, N M

2013-06-01

Variability in ADG of feedlot cattle can affect profits, thus making overall returns more unstable. Hence, knowledge of the factors that contribute to heterogeneity of variances in animal performance can help feedlot managers evaluate risks and minimize profit volatility when making managerial and economic decisions in commercial feedlots. The objectives of the present study were to evaluate heteroskedasticity, defined as heterogeneity of variances, in ADG of cohorts of commercial feedlot cattle, and to identify cattle demographic factors at feedlot arrival as potential sources of variance heterogeneity, accounting for cohort- and feedlot-level information in the data structure. An operational dataset compiled from 24,050 cohorts from 25 U. S. commercial feedlots in 2005 and 2006 was used for this study. Inference was based on a hierarchical Bayesian model implemented with Markov chain Monte Carlo, whereby cohorts were modeled at the residual level and feedlot-year clusters were modeled as random effects. Forward model selection based on deviance information criteria was used to screen potentially important explanatory variables for heteroskedasticity at cohort- and feedlot-year levels. The Bayesian modeling framework was preferred as it naturally accommodates the inherently hierarchical structure of feedlot data whereby cohorts are nested within feedlot-year clusters. Evidence for heterogeneity of variance components of ADG was substantial and primarily concentrated at the cohort level. Feedlot-year specific effects were, by far, the greatest contributors to ADG heteroskedasticity among cohorts, with an estimated ∼12-fold change in dispersion between most and least extreme feedlot-year clusters. In addition, identifiable demographic factors associated with greater heterogeneity of cohort-level variance included smaller cohort sizes, fewer days on feed, and greater arrival BW, as well as feedlot arrival during summer months. These results support that

Teaching Bayesian Statistics in a Health Research Methodology Program

ERIC Educational Resources Information Center

Pullenayegum, Eleanor M.; Thabane, Lehana

2009-01-01

Despite the appeal of Bayesian methods in health research, they are not widely used. This is partly due to a lack of courses in Bayesian methods at an appropriate level for non-statisticians in health research. Teaching such a course can be challenging because most statisticians have been taught Bayesian methods using a mathematical approach, and…

Accurate Biomass Estimation via Bayesian Adaptive Sampling

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay

2005-01-01

The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.

An Intuitive Dashboard for Bayesian Network Inference

NASA Astrophysics Data System (ADS)

Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

2014-03-01

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

Movement, migration, and smolting of Atlantic salmon (Salmo salar)

USGS Publications Warehouse

McCormick, S.D.; Hansen, Lonnie P.; Quinn, T.P.; Saunders, R.L.

1998-01-01

A variety of movements characterize the behavioral plasticity of Atlantic salmon (Salmo salar) in fresh water, including movements of fry from redds, establishment of feeding territories, spawning movements of sexually mature male parr, movement to and from winter habitat, and smolt migration in spring. Smolting is an adaptive specialization for downstream migration, seawater entry, and marine residence. While still in fresh water, smolts become silvery and streamlined, lose their positive rheotaxis and territoriality, and begin schooling. Physiological changes include increased salinity tolerance, olfactory sensitivity, metabolic rate, scope for growth, and altered hemoglobin and visual pigments. Through their impact on the neuroendocrine system, photoperiod and temperature regulate physiological changes, whereas temperature and water flow may initiate migration. Smolt survival is affected by a limited period of readiness (a physiological 'smolt window') and the timing of seawater entry with environmental conditions such as temperature, food, and predators (an ecological 'smolt window'). Smolt development is adversely affected by acidity, pollutants, and improper rearing conditions, and is often more sensitive than other life stages. Unfortunately, the migration corridor of smolts (mainstems of rivers and estuaries) are the most heavily impacted by pollution, dams, and other anthropogenic activities that may be directly lethal or increase mortality by delaying or inhibiting smolt migration.

A multimembership catalogue for 1876 open clusters using UCAC4 data

NASA Astrophysics Data System (ADS)

Sampedro, L.; Dias, W. S.; Alfaro, E. J.; Monteiro, H.; Molino, A.

2017-10-01

The main objective of this work is to determine the cluster members of 1876 open clusters, using positions and proper motions of the astrometric fourth United States Naval Observatory (USNO) CCD Astrograph Catalog (UCAC4). For this purpose, we apply three different methods, all based on a Bayesian approach, but with different formulations: a purely parametric method, another completely non-parametric algorithm and a third, recently developed by Sampedro & Alfaro, using both formulations at different steps of the whole process. The first and second statistical moments of the members' phase-space subspace, obtained after applying the three methods, are compared for every cluster. Although, on average, the three methods yield similar results, there are also specific differences between them, as well as for some particular clusters. The comparison with other published catalogues shows good agreement. We have also estimated, for the first time, the mean proper motion for a sample of 18 clusters. The results are organized in a single catalogue formed by two main files, one with the most relevant information for each cluster, partially including that in UCAC4, and the other showing the individual membership probabilities for each star in the cluster area. The final catalogue, with an interface design that enables an easy interaction with the user, is available in electronic format at the Stellar Systems Group (SSG-IAA) web site (http://ssg.iaa.es/en/content/sampedro-cluster-catalog).

Power in Bayesian Mediation Analysis for Small Sample Research.

PubMed

Miočević, Milica; MacKinnon, David P; Levy, Roy

2017-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

Bayesian B-spline mapping for dynamic quantitative traits.

PubMed

Xing, Jun; Li, Jiahan; Yang, Runqing; Zhou, Xiaojing; Xu, Shizhong

2012-04-01

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

Using Bayesian Networks to Improve Knowledge Assessment

ERIC Educational Resources Information Center

Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

2013-01-01

In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

Particle identification in ALICE: a Bayesian approach

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Pereira Da Costa, H.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Souza, R. D. de; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yang, P.; Yano, S.; Yasin, Z.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-05-01

We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss ( d E/d x) and time of flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high-purity samples of identified particles in the decay channels K0S → π-π+, φ→ K-K+, and Λ→ p π- in p-Pb collisions at √{s_{NN}}=5.02 TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected pT spectra of pions, kaons, protons, and D0 mesons in pp collisions at √{s}=7 TeV. In all cases, the results using Bayesian PID were found to be consistent with previous measurements performed by ALICE using a standard PID approach. For the measurement of D0 → K-π+, it was found that a Bayesian PID approach gave a higher signal-to-background ratio and a similar or larger statistical significance when compared with standard PID selections, despite a reduced identification efficiency. Finally, we present an exploratory study of the measurement of Λc+ → p K-π+ in pp collisions at √{s}=7 TeV, using the Bayesian approach for the identification of its decay products.

Immunomodulatory effect of prolactin on Atlantic salmon (Salmo salar) macrophage function.

PubMed

Paredes, Marco; Gonzalez, Katerina; Figueroa, Jaime; Montiel-Eulefi, Enrique

2013-10-01

The in vitro and in vivo effect of prolactin (PRL) on kidney macrophages from Atlantic salmon (Salmo salar) was investigated under the assumption that PRL stimulates immune innate response in mammals. Kidney macrophages were treated two ways: first, cultured in RPMI 1640 medium containing 10, 25, 50 and 100 ng/mL of PRL and second, isolated from a fish with a PRL-injected dose of 100 ng/Kg. Reduced nitro blue tetrazolium (formazan) was used to produce intracellular superoxide anion. Phagocytic activity of PRL was determined in treated cells by optical microscopy observation of phagocytized Congo red-stained yeast. Kidney lysozyme activity was measured in PRL-injected fish. In vitro and in vivo macrophages treated with PRL presented an enhanced superoxide anion production, elevated phagocytic index and increased phagocytic activity. Treated fish showed higher levels of lysozyme activity in the head kidney compared to the control. These results indicate that PRL-stimulated innate immune response in Atlantic salmon and future studies will allow us to assess the possibility of using PRL as an immunostimulant in the Chilean salmon industry.

Draft Genome Sequence of the Fish Pathogen Yersinia ruckeri Strain 37551, Serotype O1b, Isolated from Diseased, Vaccinated Atlantic Salmon (Salmo salar) in Chile.

PubMed

Navas, Esteban; Bohle, Harry; Henríquez, Patricio; Grothusen, Horst; Bustamante, Fernando; Bustos, Patricio; Mancilla, Marcos

2014-08-28

We sequenced the genome of a motile O1b Yersinia ruckeri field isolate from Chile, which is causing enteric redmouth disease (ERM) in vaccinated Atlantic salmon (Salmo salar). The draft genome has 3,775,486 bp, a G+C content of 47.1%, and is predicted to contain 3,406 coding sequences. Copyright © 2014 Navas et al.

Genome-wide transcription analysis of histidine-related cataract in Atlantic salmon (Salmo salar L)

PubMed Central

Waagbø, Rune; Breck, Olav; Stavrum, Anne-Kristin; Petersen, Kjell; Olsvik, Pål A.

2009-01-01

Purpose Elevated levels of dietary histidine have previously been shown to prevent or mitigate cataract formation in farmed Atlantic salmon (Salmo salar L). The aim of this study was to shed light on the mechanisms by which histidine acts. Applying microarray analysis to the lens transcriptome, we screened for differentially expressed genes in search for a model explaining cataract development in Atlantic salmon and possible markers for early cataract diagnosis. Methods Adult Atlantic salmon (1.7 kg) were fed three standard commercial salmon diets only differing in the histidine content (9, 13, and 17 g histidine/kg diet) for four months. Individual cataract scores for both eyes were assessed by slit-lamp biomicroscopy. Lens N-acetyl histidine contents were measured by high performance liquid chromatography (HPLC). Total RNA extracted from whole lenses was analyzed using the GRASP 16K salmonid microarray. The microarray data were analyzed using J-Express Pro 2.7 and validated by quantitative real-time polymerase chain reaction (qRT–PCR). Results Fish developed cataracts with different severity in response to dietary histidine levels. Lens N-acetyl histidine contents reflected the dietary histidine levels and were negatively correlated to cataract scores. Significance analysis of microarrays (SAM) revealed 248 significantly up-regulated transcripts and 266 significantly down-regulated transcripts in fish that were fed a low level of histidine compared to fish fed a higher histidine level. Among the differentially expressed transcripts were metallothionein A and B as well as transcripts involved in lipid metabolism, carbohydrate metabolism, regulation of ion homeostasis, and protein degradation. Hierarchical clustering and correspondence analysis plot confirmed differences in gene expression between the feeding groups. The differentially expressed genes could be categorized as “early” and “late” responsive according to their expression pattern relative to

Weighing the Giants - I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

NASA Astrophysics Data System (ADS)

von der Linden, Anja; Allen, Mark T.; Applegate, Douglas E.; Kelly, Patrick L.; Allen, Steven W.; Ebeling, Harald; Burchat, Patricia R.; Burke, David L.; Donovan, David; Morris, R. Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam

2014-03-01

uncertainty for our weak-lensing mass measurements. In accompanying papers, we discuss the key aspects of our photometric calibration and photometric redshift measurements (Kelly et al.), and measure cluster masses using two methods, including a novel Bayesian weak-lensing approach that makes full use of the photometric redshift probability distributions for individual background galaxies (Applegate et al.). In subsequent papers, we will incorporate these weak-lensing mass measurements into a self-consistent framework to simultaneously determine cluster scaling relations and cosmological parameters.

Bayesian analyses of time-interval data for environmental radiation monitoring.

PubMed

Luo, Peng; Sharp, Julia L; DeVol, Timothy A

2013-01-01

Time-interval (time difference between two consecutive pulses) analysis based on the principles of Bayesian inference was investigated for online radiation monitoring. Using experimental and simulated data, Bayesian analysis of time-interval data [Bayesian (ti)] was compared with Bayesian and a conventional frequentist analysis of counts in a fixed count time [Bayesian (cnt) and single interval test (SIT), respectively]. The performances of the three methods were compared in terms of average run length (ARL) and detection probability for several simulated detection scenarios. Experimental data were acquired with a DGF-4C system in list mode. Simulated data were obtained using Monte Carlo techniques to obtain a random sampling of the Poisson distribution. All statistical algorithms were developed using the R Project for statistical computing. Bayesian analysis of time-interval information provided a similar detection probability as Bayesian analysis of count information, but the authors were able to make a decision with fewer pulses at relatively higher radiation levels. In addition, for the cases with very short presence of the source (< count time), time-interval information is more sensitive to detect a change than count information since the source data is averaged by the background data over the entire count time. The relationships of the source time, change points, and modifications to the Bayesian approach for increasing detection probability are presented.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Power in Bayesian Mediation Analysis for Small Sample Research

PubMed Central

Miočević, Milica; MacKinnon, David P.; Levy, Roy

2018-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results. PMID:29662296

Bayesian inference for psychology. Part II: Example applications with JASP.

PubMed

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for

An introduction to using Bayesian linear regression with clinical data.

PubMed

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using Bayesian belief networks in adaptive management.

Treesearch

J.B. Nyberg; B.G. Marcot; R. Sulyma

2006-01-01

Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

Topography of the Flattest Surface on Earth: using ICESAT, GPS, and MISR to Measure Salt Surface Topography on Salar de Uyuni, Bolivia

NASA Technical Reports Server (NTRS)

Comstock, Robert L.; Bills, Bruce G.

2004-01-01

Salt flats are aptly named: they are composed largely of salt, and are maintained as nearly equipotential surfaces via frequent flooding. The salar de Uyuni, on the Altiplano in southwestern Bolivia, is the largest salt flat on Earth, with an area of 9,800 sq km. Except for a few bedrock islands, it has less than 40 cm of relief. The upper-most salt unit averages 5 m thick and contains 50 cu km of nearly pure halite. It includes most of the salt that was in solution in paleolake Minchin, which attained a maximum area of 60,000 sq km and a maximum depth of 150 m, roughly 15 kyr ago. Despite approx. 10 m of differential isostatic rebound since deposition, the salar surface has been actively maintained as an extraordinarily flat and smooth surface by annual flooding during the rainy season. We have used the strong optical absorption properties of water in the visible band to map spatial variations in water depth during a time when the salar was flooded. As water depth increases, the initially pure white surface appears both darker and bluer. We utilized MISR images taken during the interval from April to November 2001. The red and infra-red bands (672 and 867 nm wavelength) were most useful since the water depth is small and the absorption at those wavelengths is quite strong. Nadir pointed MISR images have 275 m spatial resolution. To aid in our evaluation of water depth variations over the saiar surface, we utilized two sources of direct topographic measurements: several ICESAT altimetry tracks cross the area, and a 40x50 km GPS grid was surveyed to calibrate ICESAT. A difficulty in using these data types is that both give salt surface elevations relative to the ellipsoid, whereas the water surface will, in the absence of wind or tidal disturbances, follow an equipotential surface. Geoid height is not known to the required accuracy of a few cm in the central Andes. As a result, before comparing optical absorption from MISR to salt surface topography from GPS or

Bayesian calibration for forensic age estimation.

PubMed

Ferrante, Luigi; Skrami, Edlira; Gesuita, Rosaria; Cameriere, Roberto

2015-05-10

Forensic medicine is increasingly called upon to assess the age of individuals. Forensic age estimation is mostly required in relation to illegal immigration and identification of bodies or skeletal remains. A variety of age estimation methods are based on dental samples and use of regression models, where the age of an individual is predicted by morphological tooth changes that take place over time. From the medico-legal point of view, regression models, with age as the dependent random variable entail that age tends to be overestimated in the young and underestimated in the old. To overcome this bias, we describe a new full Bayesian calibration method (asymmetric Laplace Bayesian calibration) for forensic age estimation that uses asymmetric Laplace distribution as the probability model. The method was compared with three existing approaches (two Bayesian and a classical method) using simulated data. Although its accuracy was comparable with that of the other methods, the asymmetric Laplace Bayesian calibration appears to be significantly more reliable and robust in case of misspecification of the probability model. The proposed method was also applied to a real dataset of values of the pulp chamber of the right lower premolar measured on x-ray scans of individuals of known age. Copyright © 2015 John Wiley & Sons, Ltd.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Salmo salar and Esox lucius full-length cDNA sequences reveal changes in evolutionary pressures on a post-tetraploidization genome

PubMed Central

2010-01-01

Background Salmonids are one of the most intensely studied fish, in part due to their economic and environmental importance, and in part due to a recent whole genome duplication in the common ancestor of salmonids. This duplication greatly impacts species diversification, functional specialization, and adaptation. Extensive new genomic resources have recently become available for Atlantic salmon (Salmo salar), but documentation of allelic versus duplicate reference genes remains a major uncertainty in the complete characterization of its genome and its evolution. Results From existing expressed sequence tag (EST) resources and three new full-length cDNA libraries, 9,057 reference quality full-length gene insert clones were identified for Atlantic salmon. A further 1,365 reference full-length clones were annotated from 29,221 northern pike (Esox lucius) ESTs. Pairwise dN/dS comparisons within each of 408 sets of duplicated salmon genes using northern pike as a diploid out-group show asymmetric relaxation of selection on salmon duplicates. Conclusions 9,057 full-length reference genes were characterized in S. salar and can be used to identify alleles and gene family members. Comparisons of duplicated genes show that while purifying selection is the predominant force acting on both duplicates, consistent with retention of functionality in both copies, some relaxation of pressure on gene duplicates can be identified. In addition, there is evidence that evolution has acted asymmetrically on paralogs, allowing one of the pair to diverge at a faster rate. PMID:20433749

BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

NASA Technical Reports Server (NTRS)

Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.

1994-01-01

We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

Properties of the Bayesian Knowledge Tracing Model

ERIC Educational Resources Information Center

van de Sande, Brett

2013-01-01

Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model…

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2014-11-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2015-04-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Additive Genetic Variability and the Bayesian Alphabet

PubMed Central

Gianola, Daniel; de los Campos, Gustavo; Hill, William G.; Manfredi, Eduardo; Fernando, Rohan

2009-01-01

The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called “Bayes A”) with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly. PMID:19620397

Exploring the IMF of star clusters: a joint SLUG and LEGUS effort

NASA Astrophysics Data System (ADS)

Ashworth, G.; Fumagalli, M.; Krumholz, M. R.; Adamo, A.; Calzetti, D.; Chandar, R.; Cignoni, M.; Dale, D.; Elmegreen, B. G.; Gallagher, J. S., III; Gouliermis, D. A.; Grasha, K.; Grebel, E. K.; Johnson, K. E.; Lee, J.; Tosi, M.; Wofford, A.

2017-08-01

We present the implementation of a Bayesian formalism within the Stochastically Lighting Up Galaxies (slug) stellar population synthesis code, which is designed to investigate variations in the initial mass function (IMF) of star clusters. By comparing observed cluster photometry to large libraries of clusters simulated with a continuously varying IMF, our formalism yields the posterior probability distribution function (PDF) of the cluster mass, age and extinction, jointly with the parameters describing the IMF. We apply this formalism to a sample of star clusters from the nearby galaxy NGC 628, for which broad-band photometry in five filters is available as part of the Legacy ExtraGalactic UV Survey (LEGUS). After allowing the upper-end slope of the IMF (α3) to vary, we recover PDFs for the mass, age and extinction that are broadly consistent with what is found when assuming an invariant Kroupa IMF. However, the posterior PDF for α3 is very broad due to a strong degeneracy with the cluster mass, and it is found to be sensitive to the choice of priors, particularly on the cluster mass. We find only a modest improvement in the constraining power of α3 when adding Hα photometry from the companion Hα-LEGUS survey. Conversely, Hα photometry significantly improves the age determination, reducing the frequency of multi-modal PDFs. With the aid of mock clusters, we quantify the degeneracy between physical parameters, showing how constraints on the cluster mass that are independent of photometry can be used to pin down the IMF properties of star clusters.

Three sympatric clusters of the malaria vector Anopheles culicifacies E (Diptera: Culicidae) detected in Sri Lanka.

PubMed

Harischandra, Iresha Nilmini; Dassanayake, Ranil Samantha; De Silva, Bambaranda Gammacharige Don Nissanka Kolitha

2016-01-04

The disease re-emergence threat from the major malaria vector in Sri Lanka, Anopheles culicifacies, is currently increasing. To predict malaria vector dynamics, knowledge of population genetics and gene flow is required, but this information is unavailable for Sri Lanka. This study was carried out to determine the population structure of An. culicifacies E in Sri Lanka. Eight microsatellite markers were used to examine An. culicifacies E collected from six sites in Sri Lanka during 2010-2012. Standard population genetic tests and analyses, genetic differentiation, Hardy-Weinberg equilibrium, linkage disequilibrium, Bayesian cluster analysis, AMOVA, SAMOVA and isolation-by-distance were conducted using five polymorphic loci. Five microsatellite loci were highly polymorphic with high allelic richness. Hardy-Weinberg Equilibrium (HWE) was significantly rejected for four loci with positive F(IS) values in the pooled population (p < 0.0100). Three loci showed high deviations in all sites except Kataragama, which was in agreement with HWE for all loci except one locus (p < 0.0016). Observed heterozygosity was less than the expected values for all sites except Kataragama, where reported negative F(IS) values indicated a heterozygosity excess. Genetic differentiation was observed for all sampling site pairs and was not supported by the isolation by distance model. Bayesian clustering analysis identified the presence of three sympatric clusters (gene pools) in the studied population. Significant genetic differentiation was detected in cluster pairs with low gene flow and isolation by distance was not detected between clusters. Furthermore, the results suggested the presence of a barrier to gene flow that divided the populations into two parts with the central hill region of Sri Lanka as the dividing line. Three sympatric clusters were detected among An. culicifacies E specimens isolated in Sri Lanka. There was no effect of geographic distance on genetic

Computational Neuropsychology and Bayesian Inference.

PubMed

Parr, Thomas; Rees, Geraint; Friston, Karl J

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Computational Neuropsychology and Bayesian Inference

PubMed Central

Parr, Thomas; Rees, Geraint; Friston, Karl J.

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology. PMID:29527157

BMDS: A Collection of R Functions for Bayesian Multidimensional Scaling

ERIC Educational Resources Information Center

Okada, Kensuke; Shigemasu, Kazuo

2009-01-01

Bayesian multidimensional scaling (MDS) has attracted a great deal of attention because: (1) it provides a better fit than do classical MDS and ALSCAL; (2) it provides estimation errors of the distances; and (3) the Bayesian dimension selection criterion, MDSIC, provides a direct indication of optimal dimensionality. However, Bayesian MDS is not…

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Physiological preparedness and performance of Atlantic salmon Salmo salar smolts in relation to behavioural salinity preferences and thresholds.

PubMed

Stich, D S; Zydlewski, G B; Zydlewski, J D

2016-02-01

This study investigated the relationships between behavioural responses of Atlantic salmon Salmo salar smolts to saltwater (SW) exposure and physiological characteristics of smolts in laboratory experiments. It concurrently described the behaviour of acoustically tagged smolts with respect to SW and tidal cycles during estuary migration. Salmo salar smolts increased their use of SW relative to fresh water (FW) from April to June in laboratory experiments. Mean preference for SW never exceeded 50% of time in any group. Preference for SW increased throughout the course of smolt development. Maximum continuous time spent in SW was positively related to gill Na(+), K(+)-ATPase (NKA) activity and osmoregulatory performance in full-strength SW (measured as change in gill NKA activity and plasma osmolality). Smolts decreased depth upon reaching areas of the Penobscot Estuary where SW was present, and all fish became more surface oriented during passage from head of tide to the ocean. Acoustically tagged, migrating smolts with low gill NKA activity moved faster in FW reaches of the estuary than those with higher gill NKA activity. There was no difference in movement rate through SW reaches of the estuary based on gill NKA activity. Migrating fish moved with tidal flow during the passage of the lower estuary based on the observed patterns in both vertical and horizontal movements. The results indicate that smolts select low-salinity water during estuary migration and use tidal currents to minimize energetic investment in seaward migration. Seasonal changes in osmoregulatory ability highlight the importance of the timing of stocking and estuary arrival. © 2015 The Fisheries Society of the British Isles.

Embedding the results of focussed Bayesian fusion into a global context

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

Multilocus microsatellite typing shows three different genetic clusters of Leishmania major in Iran.

PubMed

Mahnaz, Tashakori; Al-Jawabreh, Amer; Kuhls, Katrin; Schönian, Gabriele

2011-10-01

Ten polymorphic microsatellite markers were used to analyse 25 strains of Leishmania major collected from cutaneous leishmaniasis cases in different endemic areas in Iran. Nine of the markers were polymorphic, revealing 21 different genotypes. The data displayed significant microsatellite polymorphism with rare allelic heterozygosity. Bayesian statistic and distance based analyses identified three genetic clusters among the 25 strains analysed. Cluster I represented mainly strains isolated in the west and south-west of Iran, with the exception of four strains originating from central Iran. Cluster II comprised strains from the central part of Iran, and cluster III included only strains from north Iran. The geographical distribution of L. major in Iran was supported by comparing the microsatellite profiles of the 25 Iranian strains to those of 105 strains collected in 19 Asian and African countries. The Iranian clusters I and II were separated from three previously described populations comprising strains from Africa, the Middle East and Central Asia whereas cluster III grouped together with the Central Asian population. The considerable genetic variability of L. major might be related to the existence of different populations of Phlebotomus papatasi and/or to differences in reservoir host abundance in different parts of Iran. Copyright © 2011 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Dynamic Bayesian network modeling for longitudinal brain morphometry

PubMed Central

Chen, Rong; Resnick, Susan M; Davatzikos, Christos; Herskovits, Edward H

2011-01-01

Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment — the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group. PMID:21963916

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

Bayesian model reduction and empirical Bayes for group (DCM) studies

PubMed Central

Friston, Karl J.; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E.; van Wijk, Bernadette C.M.; Ziegler, Gabriel; Zeidman, Peter

2016-01-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level – e.g., dynamic causal models – and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. PMID:26569570

Bayesian model reduction and empirical Bayes for group (DCM) studies.

PubMed

Friston, Karl J; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E; van Wijk, Bernadette C M; Ziegler, Gabriel; Zeidman, Peter

2016-03-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Bayesian seismic tomography by parallel interacting Markov chains

NASA Astrophysics Data System (ADS)

Gesret, Alexandrine; Bottero, Alexis; Romary, Thomas; Noble, Mark; Desassis, Nicolas

2014-05-01

The velocity field estimated by first arrival traveltime tomography is commonly used as a starting point for further seismological, mineralogical, tectonic or similar analysis. In order to interpret quantitatively the results, the tomography uncertainty values as well as their spatial distribution are required. The estimated velocity model is obtained through inverse modeling by minimizing an objective function that compares observed and computed traveltimes. This step is often performed by gradient-based optimization algorithms. The major drawback of such local optimization schemes, beyond the possibility of being trapped in a local minimum, is that they do not account for the multiple possible solutions of the inverse problem. They are therefore unable to assess the uncertainties linked to the solution. Within a Bayesian (probabilistic) framework, solving the tomography inverse problem aims at estimating the posterior probability density function of velocity model using a global sampling algorithm. Markov chains Monte-Carlo (MCMC) methods are known to produce samples of virtually any distribution. In such a Bayesian inversion, the total number of simulations we can afford is highly related to the computational cost of the forward model. Although fast algorithms have been recently developed for computing first arrival traveltimes of seismic waves, the complete browsing of the posterior distribution of velocity model is hardly performed, especially when it is high dimensional and/or multimodal. In the latter case, the chain may even stay stuck in one of the modes. In order to improve the mixing properties of classical single MCMC, we propose to make interact several Markov chains at different temperatures. This method can make efficient use of large CPU clusters, without increasing the global computational cost with respect to classical MCMC and is therefore particularly suited for Bayesian inversion. The exchanges between the chains allow a precise sampling of the

Bayesian markets to elicit private information.

PubMed

Baillon, Aurélien

2017-07-25

Financial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., "Are you satisfied with your life?") or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their "type") as a signal. Hence, beliefs about others' types are correlated with one's own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others' signals.

Historical record of Yersinia ruckeri and Aeromonas salmonicida among sea-run Atlantic salmon (Salmo salar) in the Penobscot River

USGS Publications Warehouse

Cipriano, R.C.; Coll, J.

2005-01-01

Despite restoration efforts, only about 2,000 Atlantic salmon (Salmo salar) salmon have annually returned to New England Rivers and more than 71% of these fish migrate to the Penobscot River alone. This report provides a historical compilation on the prevalence's of both Yersinia ruckeri, cause of enteric redmouth disease, and Aeromonas salmonicida, cause of furunculosis, among mature sea-run Atlantic salmon that returned to the Penobscot River from 1976 to 2003. Aeromonas salmonicida was detected in 28.6% and Yersinia ruckeri was detected among 50% of the yearly returns. Consequently, Atlantic salmon that return to the river are potential reservoirs of infection.

A Bayesian, generalized frailty model for comet assays.

PubMed

Ghebretinsae, Aklilu Habteab; Faes, Christel; Molenberghs, Geert; De Boeck, Marlies; Geys, Helena

2013-05-01

This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals

Approximate Bayesian evaluations of measurement uncertainty

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Bodnar, Olha

2018-04-01

The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.

PubMed

Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

2017-01-01

Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and

A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects.

PubMed

Henschel, Volkmar; Engel, Jutta; Hölzel, Dieter; Mansmann, Ulrich

2009-02-10

Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty. MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework. Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN. The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.

Bayesian estimation of the discrete coefficient of determination.

PubMed

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Linkage maps of the Atlantic salmon (Salmo salar) genome derived from RAD sequencing

PubMed Central

2014-01-01

Background Genetic linkage maps are useful tools for mapping quantitative trait loci (QTL) influencing variation in traits of interest in a population. Genotyping-by-sequencing approaches such as Restriction-site Associated DNA sequencing (RAD-Seq) now enable the rapid discovery and genotyping of genome-wide SNP markers suitable for the development of dense SNP linkage maps, including in non-model organisms such as Atlantic salmon (Salmo salar). This paper describes the development and characterisation of a high density SNP linkage map based on SbfI RAD-Seq SNP markers from two Atlantic salmon reference families. Results Approximately 6,000 SNPs were assigned to 29 linkage groups, utilising markers from known genomic locations as anchors. Linkage maps were then constructed for the four mapping parents separately. Overall map lengths were comparable between male and female parents, but the distribution of the SNPs showed sex-specific patterns with a greater degree of clustering of sire-segregating SNPs to single chromosome regions. The maps were integrated with the Atlantic salmon draft reference genome contigs, allowing the unique assignment of ~4,000 contigs to a linkage group. 112 genome contigs mapped to two or more linkage groups, highlighting regions of putative homeology within the salmon genome. A comparative genomics analysis with the stickleback reference genome identified putative genes closely linked to approximately half of the ordered SNPs and demonstrated blocks of orthology between the Atlantic salmon and stickleback genomes. A subset of 47 RAD-Seq SNPs were successfully validated using a high-throughput genotyping assay, with a correspondence of 97% between the two assays. Conclusions This Atlantic salmon RAD-Seq linkage map is a resource for salmonid genomics research as genotyping-by-sequencing becomes increasingly common. This is aided by the integration of the SbfI RAD-Seq SNPs with existing reference maps and the draft reference genome, as well

Bayesian markets to elicit private information

PubMed Central

2017-01-01

Financial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., “Are you satisfied with your life?”) or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their “type”) as a signal. Hence, beliefs about others’ types are correlated with one’s own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others’ signals. PMID:28696293

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

Computational statistics using the Bayesian Inference Engine

NASA Astrophysics Data System (ADS)

Weinberg, Martin D.

2013-09-01

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

Bayesian Correlation Analysis for Sequence Count Data

PubMed Central

Lau, Nelson; Perkins, Theodore J.

2016-01-01

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities’ measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low—especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities’ signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset. PMID:27701449

A Bayesian approach to meta-analysis of plant pathology studies.

PubMed

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

What Is the Probability You Are a Bayesian?

ERIC Educational Resources Information Center

Wulff, Shaun S.; Robinson, Timothy J.

2014-01-01

Bayesian methodology continues to be widely used in statistical applications. As a result, it is increasingly important to introduce students to Bayesian thinking at early stages in their mathematics and statistics education. While many students in upper level probability courses can recite the differences in the Frequentist and Bayesian…

Hierarchical Bayesian Spatio–Temporal Analysis of Climatic and Socio–Economic Determinants of Rocky Mountain Spotted Fever

PubMed Central

Raghavan, Ram K.; Goodin, Douglas G.; Neises, Daniel; Anderson, Gary A.; Ganta, Roman R.

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio–economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio–temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio–economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main–effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate–change impacts on tick–borne diseases are discussed. PMID:26942604

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

PubMed

Raghavan, Ram K; Goodin, Douglas G; Neises, Daniel; Anderson, Gary A; Ganta, Roman R

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Bayesian networks for maritime traffic accident prevention: benefits and challenges.

PubMed

Hänninen, Maria

2014-12-01

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making. Copyright © 2014 Elsevier Ltd. All rights reserved.

The image recognition based on neural network and Bayesian decision

NASA Astrophysics Data System (ADS)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Editorial: Bayesian benefits for child psychology and psychiatry researchers.

PubMed

Oldehinkel, Albertine J

2016-09-01

For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

Apoptosis inhibition of Atlantic salmon (Salmo salar) peritoneal macrophages by Piscirickettsia salmonis.

PubMed

Díaz, S; Rojas, M E; Galleguillos, M; Maturana, C; Smith, P I; Cifuentes, F; Contreras, I; Smith, P A

2017-12-01

To improve the understanding of the piscirickettsiosis pathogenesis, the in vivo apoptosis modulation of peritoneal macrophages and lymphocytes was studied in juvenile Salmo salar intraperitoneally injected with Piscirickettsia salmonis. Five fish were sampled at post-exposure days 1, 5, 8 (preclinical), 20 (clinical) and 40 (post-clinical period of the disease), and the leucocytes of their coelomic washings were analysed by flow cytometry (using the JC-1 cationic dye), TUNEL and cytology to detect apoptotic cells. A selective and temporal pattern of apoptosis modulation by P. salmonis infection was observed. Apoptosis in lymphocytes was not affected, whereas it was inhibited in macrophages but only during the preclinical stage of the induced piscirickettsiosis. Hence, it is postulated that P. salmonis inhibits macrophage apoptosis at the beginning of the disease development to survive, multiply and probably be transported inside these phagocytes; once this process is complete, macrophage apoptosis is no longer inhibited, thus facilitating the exit of the bacteria from the infected cells for continuing their life cycle. © 2017 John Wiley & Sons Ltd.

A Bayesian-frequentist two-stage single-arm phase II clinical trial design.

PubMed

Dong, Gaohong; Shih, Weichung Joe; Moore, Dirk; Quan, Hui; Marcella, Stephen

2012-08-30

It is well-known that both frequentist and Bayesian clinical trial designs have their own advantages and disadvantages. To have better properties inherited from these two types of designs, we developed a Bayesian-frequentist two-stage single-arm phase II clinical trial design. This design allows both early acceptance and rejection of the null hypothesis ( H(0) ). The measures (for example probability of trial early termination, expected sample size, etc.) of the design properties under both frequentist and Bayesian settings are derived. Moreover, under the Bayesian setting, the upper and lower boundaries are determined with predictive probability of trial success outcome. Given a beta prior and a sample size for stage I, based on the marginal distribution of the responses at stage I, we derived Bayesian Type I and Type II error rates. By controlling both frequentist and Bayesian error rates, the Bayesian-frequentist two-stage design has special features compared with other two-stage designs. Copyright © 2012 John Wiley & Sons, Ltd.

Protective oral vaccination against infectious salmon anaemia virus in Salmo salar.

PubMed

Caruffo, Mario; Maturana, Carlos; Kambalapally, Swetha; Larenas, Julio; Tobar, Jaime A

2016-07-01

Infectious salmon anemia (ISA) is a systemic disease caused by an orthomyxovirus, which has a significant economic impact on the production of Atlantic salmon (Salmo salar). Currently, there are several commercial ISA vaccines available, however, those products are applied through injection, causing stress in the fish and leaving them susceptible to infectious diseases due to the injection process and associated handling. In this study, we evaluated an oral vaccine against ISA containing a recombinant viral hemagglutinin-esterase and a fusion protein as antigens. Our findings indicated that oral vaccination is able to protect Atlantic salmon against challenge with a high-virulence Chilean isolate. The oral vaccination was also correlated with the induction of IgM-specific antibodies. On the other hand, the vaccine was unable to modulate expression of the antiviral related gene Mx, showing the importance of the humoral response to the disease survival. This study provides new insights into fish protection and immune response induced by an oral vaccine against ISA, but also promises future development of preventive solutions or validation of the current existing therapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comment on priors for Bayesian occupancy models

PubMed Central

Gerber, Brian D.

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are “uninformative” or “vague”, such priors can easily be unintentionally highly informative. Here we report on how the specification of a “vague” normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts. PMID:29481554

A comment on priors for Bayesian occupancy models.

PubMed

Northrup, Joseph M; Gerber, Brian D

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are "uninformative" or "vague", such priors can easily be unintentionally highly informative. Here we report on how the specification of a "vague" normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts.

Bayesian networks in overlay recipe optimization

NASA Astrophysics Data System (ADS)

Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew

2005-05-01

Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a

Recent Transmission Clustering of HIV-1 C and CRF17_BF Strains Characterized by NNRTI-Related Mutations among Newly Diagnosed Men in Central Italy

PubMed Central

Orchi, Nicoletta; Gori, Caterina; Bertoli, Ada; Forbici, Federica; Montella, Francesco; Pennica, Alfredo; De Carli, Gabriella; Giuliani, Massimo; Continenza, Fabio; Pinnetti, Carmela; Nicastri, Emanuele; Ceccherini-Silberstein, Francesca; Mastroianni, Claudio Maria; Girardi, Enrico; Andreoni, Massimo; Antinori, Andrea; Santoro, Maria Mercedes; Perno, Carlo Federico

2015-01-01

Background Increased evidence of relevant HIV-1 epidemic transmission in European countries is being reported, with an increased circulation of non-B-subtypes. Here, we present two recent HIV-1 non-B transmission clusters characterized by NNRTI-related amino-acidic mutations among newly diagnosed HIV-1 infected men, living in Rome (Central-Italy). Methods Pol and V3 sequences were available at the time of diagnosis for all individuals. Maximum-Likelihood and Bayesian phylogenetic-trees with bootstrap and Bayesian-probability supports defined transmission-clusters. HIV-1 drug-resistance and V3-tropism were also evaluated. Results Among 534 new HIV-1 non-B cases, diagnosed from 2011 to 2014, in Central-Italy, 35 carried virus gathering in two distinct clusters, including 27 HIV-1 C and 8 CRF17_BF subtypes, respectively. Both clusters were centralized in Rome, and their origin was estimated to have been after 2007. All individuals within both clusters were males and 37.1% of them had been recently-infected. While C-cluster was entirely composed by Italian men-who-have-sex-with-men, with a median-age of 34 years (IQR:30–39), individuals in CRF17_BF-cluster were older, with a median-age of 51 years (IQR:48–59) and almost all reported sexual-contacts with men and women. All carried R5-tropic viruses, with evidence of atypical or resistance amino-acidic mutations related to NNRTI-drugs (K103Q in C-cluster, and K101E+E138K in CRF17_BF-cluster). Conclusions These two epidemiological clusters provided evidence of a strong and recent circulation of C and CRF17_BF strains in central Italy, characterized by NNRTI-related mutations among men engaging in high-risk behaviours. These findings underline the role of molecular epidemiology in identifying groups at increased risk of HIV-1 transmission, and in enhancing additional prevention efforts. PMID:26270824

Bayesian Learning and the Psychology of Rule Induction

ERIC Educational Resources Information Center

Endress, Ansgar D.

2013-01-01

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum's (2011) Bayesian model of rule-learning as a case study to…

Teaching Bayesian Statistics to Undergraduate Students through Debates

ERIC Educational Resources Information Center

Stewart, Sepideh; Stewart, Wayne

2014-01-01

This paper describes a lecturer's approach to teaching Bayesian statistics to students who were only exposed to the classical paradigm. The study shows how the lecturer extended himself by making use of ventriloquist dolls to grab hold of students' attention and embed important ideas in revealing the differences between the Bayesian and classical…

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

Estimating Tree Height-Diameter Models with the Bayesian Method

PubMed Central

Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2. PMID:24711733

Estimating tree height-diameter models with the Bayesian method.

PubMed

Zhang, Xiongqing; Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the "best" model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.

Pseudo-membranes on internal organs associated with Rhodococcus qingshengii infection in Atlantic salmon (Salmo salar).

PubMed

Avendaño-Herrera, Rubén; Balboa, Sabela; Doce, Alejandra; Ilardi, Pedro; Lovera, Pablo; Toranzo, Alicia E; Romalde, Jesús L

2011-01-10

This paper describes a pathological condition in intensive reared Atlantic salmon (Salmo salar), restricted to the appearance of pseudo-membranes covering internal organs (i.e. spleen, liver, heart and others) associated with the presence of large numbers of a Gram-positive bacteria. Isolate 79043-3, obtained as pure culture from affected fish, was subjected to a polyphasic taxonomic study in order to determine its exact taxonomic position, as well as to experimental challenges leading to determine its pathogenic potential for cultured fish. Based on this characterization, we report the first isolation of Rhodococcus qingshengii, from a farmed population of Atlantic salmon in Chile. Virulence studies demonstrated that the isolate fulfilled the Koch's postulates, suggesting that this bacterial species could be considered as an opportunistic pathogen for Atlantic salmon. Copyright © 2010 Elsevier B.V. All rights reserved.

The Gaia-ESO Survey: open clusters in Gaia-DR1 . A way forward to stellar age calibration

NASA Astrophysics Data System (ADS)

Randich, S.; Tognelli, E.; Jackson, R.; Jeffries, R. D.; Degl'Innocenti, S.; Pancino, E.; Re Fiorentin, P.; Spagna, A.; Sacco, G.; Bragaglia, A.; Magrini, L.; Prada Moroni, P. G.; Alfaro, E.; Franciosini, E.; Morbidelli, L.; Roccatagliata, V.; Bouy, H.; Bravi, L.; Jiménez-Esteban, F. M.; Jordi, C.; Zari, E.; Tautvaišiene, G.; Drazdauskas, A.; Mikolaitis, S.; Gilmore, G.; Feltzing, S.; Vallenari, A.; Bensby, T.; Koposov, S.; Korn, A.; Lanzafame, A.; Smiljanic, R.; Bayo, A.; Carraro, G.; Costado, M. T.; Heiter, U.; Hourihane, A.; Jofré, P.; Lewis, J.; Monaco, L.; Prisinzano, L.; Sbordone, L.; Sousa, S. G.; Worley, C. C.; Zaggia, S.

2018-05-01

Context. Determination and calibration of the ages of stars, which heavily rely on stellar evolutionary models, are very challenging, while representing a crucial aspect in many astrophysical areas. Aims: We describe the methodologies that, taking advantage of Gaia-DR1 and the Gaia-ESO Survey data, enable the comparison of observed open star cluster sequences with stellar evolutionary models. The final, long-term goal is the exploitation of open clusters as age calibrators. Methods: We perform a homogeneous analysis of eight open clusters using the Gaia-DR1 TGAS catalogue for bright members and information from the Gaia-ESO Survey for fainter stars. Cluster membership probabilities for the Gaia-ESO Survey targets are derived based on several spectroscopic tracers. The Gaia-ESO Survey also provides the cluster chemical composition. We obtain cluster parallaxes using two methods. The first one relies on the astrometric selection of a sample of bona fide members, while the other one fits the parallax distribution of a larger sample of TGAS sources. Ages and reddening values are recovered through a Bayesian analysis using the 2MASS magnitudes and three sets of standard models. Lithium depletion boundary (LDB) ages are also determined using literature observations and the same models employed for the Bayesian analysis. Results: For all but one cluster, parallaxes derived by us agree with those presented in Gaia Collaboration (2017, A&A, 601, A19), while a discrepancy is found for NGC 2516; we provide evidence supporting our own determination. Inferred cluster ages are robust against models and are generally consistent with literature values. Conclusions: The systematic parallax errors inherent in the Gaia DR1 data presently limit the precision of our results. Nevertheless, we have been able to place these eight clusters onto the same age scale for the first time, with good agreement between isochronal and LDB ages where there is overlap. Our approach appears promising

Do X-ray dark or underluminous galaxy clusters exist?

NASA Astrophysics Data System (ADS)

Andreon, S.; Moretti, A.

2011-12-01

We study the X-ray properties of a color-selected sample of clusters at 0.1 < z < 0.3, to quantify the real aboundance of the population of X-ray dark or underluminous clusters and at the same time the spurious detection contamination level of color-selected cluster catalogs. Starting from a local sample of color-selected clusters, we restrict our attention to those with sufficiently deep X-ray observations to probe their X-ray luminosity down to very faint values and without introducing any X-ray bias. This allowed us to have an X-ray- unbiased sample of 33 clusters to measure the LX-richness relation. Swift 1.4 Ms X-ray observations show that at least 89% of the color-detected clusters are real objects with a potential well deep enough to heat and retain an intracluster medium. The percentage rises to 94% when one includes the single spectroscopically confirmed color-selected cluster whose X-ray emission is not secured. Looking at our results from the opposite perspective, the percentage of X-ray dark clusters among color-selected clusters is very low: at most about 11 per cent (at 90% confidence). Supplementing our data with those from literature, we conclude that X-ray- and color- cluster surveys sample the same population and consequently that in this regard we can safely use clusters selected with any of the two methods for cosmological purposes. This is an essential and promising piece of information for upcoming surveys in both the optical/IR (DES, EUCLID) and X-ray (eRosita). Richness correlates with X-ray luminosity with a large scatter, 0.51 ± 0.08 (0.44 ± 0.07) dex in lgLX at a given richness, when Lx is measured in a 500 (1070) kpc aperture. We release data and software to estimate the X-ray flux, or its upper limit, of a source with over-Poisson background fluctuations (found in this work to be ~20% on cluster angular scales) and to fit X-ray luminosity vs richness if there is an intrinsic scatter. These Bayesian applications rigorously account for

Microbial characterization of microbial ecosystems associated to evaporites domes of gypsum in Salar de Llamara in Atacama desert.

PubMed

Rasuk, Maria Cecilia; Kurth, Daniel; Flores, Maria Regina; Contreras, Manuel; Novoa, Fernando; Poire, Daniel; Farias, Maria Eugenia

2014-10-01

The Central Andes in northern Chile contains a large number of closed basins whose central depression is occupied by saline lakes and salt crusts (salars). One of these basins is Salar de Llamara (850 m a.s.l.), where large domed structures of seemingly evaporitic origin forming domes can be found. In this work, we performed a detailed microbial characterization of these domes. Mineralogical studies revealed gypsum (CaSO(4)) as a major component. Microbial communities associated to these structures were analysed by 454 16S rDNA amplicon sequencing and compared between winter and summer seasons. Bacteroidetes Proteobacteria and Planctomycetes remained as the main phylogenetic groups, an increased diversity was found in winter. Comparison of the upper air-exposed part and the lower water-submerged part of the domes in both seasons showed little variation in the upper zone, showing a predominance of Chromatiales (Gammaproteobacteria), Rhodospirillales (Alphaproteobacteria), and Sphingobacteriales (Bacteroidetes). However, the submerged part showed marked differences between seasons, being dominated by Proteobacteria (Alpha and Gamma) and Verrucomicrobia in summer, but with more diverse phyla found in winter. Even though not abundant by sequence, Cyanobacteria were visually identified by scanning electron microscopy (SEM), which also revealed the presence of diatoms. Photosynthetic pigments were detected by high-performance liquid chromatography, being more diverse on the upper photosynthetic layer. Finally, the system was compared with other endoevaporite, mats microbialite and Stromatolites microbial ecosystems, showing higher similitude with evaporitic ecosystems from Atacama and Guerrero Negro. This environment is of special interest for extremophile studies because microbial life develops associated to minerals in the driest desert all over the world. Nevertheless, it is endangered by mining activity associated to copper and lithium extraction; thus, its

Migratory behaviour and survival rates of wild northern Atlantic salmon Salmo salar post-smolts: Effects of environmental factors

USGS Publications Warehouse

Davidsen, J.G.; Rikardsen, A.H.; Halttunen, E.; Thorstad, E.B.; Okland, F.; Letcher, B.H.; Skarhamar, J.; Naesje, T.F.

2009-01-01

To study smolt behaviour and survival of a northern Atlantic salmon Salmo salar population during river descent, sea entry and fjord migration, 120 wild S. salar were tagged with acoustic tags and registered at four automatic listening station arrays in the mouth of the north Norwegian River Alta and throughout the Alta Fjord. An estimated 75% of the post-smolts survived from the river mouth, through the estuary and the first 17 km of the fjord. Survival rates in the fjord varied with fork length (LF), and ranged from 97??0 to 99??5% km-1. On average, the post-smolts spent 1??5 days (36 h, range 11-365 h) travelling from the river mouth to the last fjord array, 31 km from the river mouth. The migratory speed was slower (1??8 LF s-1) in the first 4 km after sea entry compared with the next 27 km (3??0 LF s-1). Post-smolts entered the fjord more often during the high or ebbing tide (70%). There was no clear diurnal migration pattern within the river and fjord, but most of the post-smolts entered the fjord at night (66%, 2000-0800 hours), despite the 24 h daylight at this latitude. The tidal cycle, wind-induced currents and the smolts' own movements seemed to influence migratory speeds and routes in different parts of the fjord. A large variation in migration patterns, both in the river and fjord, might indicate that individuals in stochastic estuarine and marine environments are exposed to highly variable selection regimes, resulting in different responses to environmental factors on both temporal and spatial scales. Post-smolts in the northern Alta Fjord had similar early marine survival rates to those observed previously in southern fjords; however, fjord residency in the north was shorter. ?? 2009 The Fisheries Society of the British Isles.

Development of schooling behaviour during the downstream migration of Atlantic salmon Salmo salar smolts in a chalk stream.

PubMed

Riley, W D; Ibbotson, A T; Maxwell, D L; Davison, P I; Beaumont, W R C; Ives, M J

2014-10-01

The downstream migratory behaviour of wild Atlantic salmon Salmo salar smolts was monitored using passive integrated transponder (PIT) antennae systems over 10 years in the lower reaches of a small chalk stream in southern England, U.K. The timing of smolt movements and the likely occurrence of schooling were investigated and compared to previous studies. In nine of the 10 consecutive years of study, the observed diel downstream patterns of S. salar smolt migration appeared to be synchronized with the onset of darkness. The distribution of time intervals between successive nocturnal detections of PIT-tagged smolts was as expected if generated randomly from observed hourly rates. There were, however, significantly more short intervals than expected for smolts detected migrating during the day. For each year from 2006 to 2011, the observed 10th percentile of the daytime intervals was <4 s, compared to ≥55 s for the simulated random times, indicating greater incidence of groups of smolts. Groups with the shortest time intervals between successive PIT tag detections originated from numerous parr tagging sites (used as a proxy for relatedness). The results suggest that the ecological drivers influencing daily smolt movements in the lower reaches of chalk stream catchments are similar to those previously reported at the onset of migration for smolts leaving their natal tributaries; that smolts detected migrating during the night are moving independently following initiation by a common environmental factor (presumably darkness), whereas those detected migrating during the day often move in groups, and that such schools may not be site (kin)-structured. The importance of understanding smolt migratory behaviour is considered with reference to stock monitoring programmes and enhancing downstream passage past barriers. © 2014 Crown copyright. Journal of Fish Biology © 2014 The Fisheries Society of the British Isles.

Comprehension and computation in Bayesian problem solving

PubMed Central

Johnson, Eric D.; Tubau, Elisabet

2015-01-01

Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian inferences relative to normalized formats (e.g., probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e., transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct vs. incorrect reasoners depart, and how individual differences might influence this time point. PMID:26283976

HICOSMO - X-ray analysis of a complete sample of galaxy clusters

NASA Astrophysics Data System (ADS)

Schellenberger, G.; Reiprich, T.

2017-10-01

Galaxy clusters are known to be the largest virialized objects in the Universe. Based on the theory of structure formation one can use them as cosmological probes, since they originate from collapsed overdensities in the early Universe and witness its history. The X-ray regime provides the unique possibility to measure in detail the most massive visible component, the intra cluster medium. Using Chandra observations of a local sample of 64 bright clusters (HIFLUGCS) we provide total (hydrostatic) and gas mass estimates of each cluster individually. Making use of the completeness of the sample we quantify two interesting cosmological parameters by a Bayesian cosmological likelihood analysis. We find Ω_{M}=0.3±0.01 and σ_{8}=0.79±0.03 (statistical uncertainties) using our default analysis strategy combining both, a mass function analysis and the gas mass fraction results. The main sources of biases that we discuss and correct here are (1) the influence of galaxy groups (higher incompleteness in parent samples and a differing behavior of the L_{x} - M relation), (2) the hydrostatic mass bias (as determined by recent hydrodynamical simulations), (3) the extrapolation of the total mass (comparing various methods), (4) the theoretical halo mass function and (5) other cosmological (non-negligible neutrino mass), and instrumental (calibration) effects.

Quantum-Bayesian coherence

NASA Astrophysics Data System (ADS)

Fuchs, Christopher A.; Schack, Rüdiger

2013-10-01

In the quantum-Bayesian interpretation of quantum theory (or QBism), the Born rule cannot be interpreted as a rule for setting measurement-outcome probabilities from an objective quantum state. But if not, what is the role of the rule? In this paper, the argument is given that it should be seen as an empirical addition to Bayesian reasoning itself. Particularly, it is shown how to view the Born rule as a normative rule in addition to usual Dutch-book coherence. It is a rule that takes into account how one should assign probabilities to the consequences of various intended measurements on a physical system, but explicitly in terms of prior probabilities for and conditional probabilities consequent upon the imagined outcomes of a special counterfactual reference measurement. This interpretation is exemplified by representing quantum states in terms of probabilities for the outcomes of a fixed, fiducial symmetric informationally complete measurement. The extent to which the general form of the new normative rule implies the full state-space structure of quantum mechanics is explored.

A Bayesian Assessment of Seismic Semi-Periodicity Forecasts

NASA Astrophysics Data System (ADS)

Nava, F.; Quinteros, C.; Glowacka, E.; Frez, J.

2016-01-01

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela.

Calibrating Bayesian Network Representations of Social-Behavioral Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Effect of low sea water temperature on water balance in the Atlantic salmon, (Salmo salar L.).

PubMed

Lega, Y V; Chernitsky, A G; Belkovsky, N M

1992-08-01

The water balance in Atlantic salmon (Salmo salar L.) overwintering in sea water (34 ‰) was investigated. With a decrease of temperature from 5.6 to 1.0°C the drinking rate decreased from 13.9 to 5.7 ml/kg/day, and the absolute amount of water absorbed decreased from 8.9 to 5.0 ml/kg/day. A decrease in temperature led, however, to an increase in the proportion of water absorbed in the intestines from 60 to 96%. Blood serum osmolarity increased from 320 to 440 mosm/1 with decreasing temperature and there was a reduction in tissue water content from 75 to 69% The disturbance of water balance at low temperature may be one of the factors responsible for mortality of salmon overwintering in sea water.

Finding Groups Using Model-based Cluster Analysis: Heterogeneous Emotional Self-regulatory Processes and Heavy Alcohol Use Risk

PubMed Central

Mun, Eun-Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2010-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of non-nested models using the Bayesian Information Criterion (BIC) to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women based on their baseline heart rate (HR) and HR variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled High Alcohol Risk and Normative groups. Compared to the Normative group, individuals in the High Alcohol Risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The High Alcohol Risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the Normative group showed a significant HRV change only to negative cues. Findings suggest that the individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use alcohol for emotional regulation. PMID:18331138

Bayesian Threshold Estimation

ERIC Educational Resources Information Center

Gustafson, S. C.; Costello, C. S.; Like, E. C.; Pierce, S. J.; Shenoy, K. N.

2009-01-01

Bayesian estimation of a threshold time (hereafter simply threshold) for the receipt of impulse signals is accomplished given the following: 1) data, consisting of the number of impulses received in a time interval from zero to one and the time of the largest time impulse; 2) a model, consisting of a uniform probability density of impulse time…

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

A new prior for bayesian anomaly detection: application to biosurveillance.

PubMed

Shen, Y; Cooper, G F

2010-01-01

Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the

Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia.

PubMed

Alemu, Kassahun; Worku, Alemayehu; Berhane, Yemane; Kumie, Abera

2014-06-06

Malaria attacks are not evenly distributed in space and time. In highland areas with low endemicity, malaria transmission is highly variable and malaria acquisition risk for individuals is unevenly distributed even within a neighbourhood. Characterizing the spatiotemporal distribution of malaria cases in high-altitude villages is necessary to prioritize the risk areas and facilitate interventions. Spatial scan statistics using the Bernoulli method were employed to identify spatial and temporal clusters of malaria in high-altitude villages. Daily malaria data were collected, using a passive surveillance system, from patients visiting local health facilities. Georeference data were collected at villages using hand-held global positioning system devices and linked to patient data. Bernoulli model using Bayesian approaches and Marcov Chain Monte Carlo (MCMC) methods were used to identify the effects of factors on spatial clusters of malaria cases. The deviance information criterion (DIC) was used to assess the goodness-of-fit of the different models. The smaller the DIC, the better the model fit. Malaria cases were clustered in both space and time in high-altitude villages. Spatial scan statistics identified a total of 56 spatial clusters of malaria in high-altitude villages. Of these, 39 were the most likely clusters (LLR = 15.62, p < 0.00001) and 17 were secondary clusters (LLR = 7.05, p < 0.03). The significant most likely temporal malaria clusters were detected between August and December (LLR = 17.87, p < 0.001). Travel away home, males and age above 15 years had statistically significant effect on malaria clusters at high-altitude villages. The study identified spatial clusters of malaria cases occurring at high elevation villages within the district. A patient who travelled away from home to a malaria-endemic area might be the most probable source of malaria infection in a high-altitude village. Malaria interventions in high altitude villages should

The WIYN Open Cluster Study: A 15-Year Report

NASA Astrophysics Data System (ADS)

Mathieu, Robert D.; WOCS Collaboration

2013-06-01

The WIYN 3.5m telescope combines large aperture, wide field of view and superb image quality. The WIYN consortium includes investigators in numerous areas of open cluster research. The combination spawned the WIYN Open Cluster Study (WOCS) over a decade ago, with the goals of producing 1) comprehensive photometric, astrometric and spectroscopic data for new fundamental open clusters and 2) addressing key astrophysical problems with these data. The set of core WOCS open clusters spans age and metallicity. Low reddening, solar proximity and richness were also desirable features in selecting core open clusters. More than 50 WIYN Open Cluster Study papers have been published in refereed journals. Highlights include: deep and wide-field photometry of NGC 188, NGC 2168 (M35), and NGC 6819 (WOCS I, II, XI and LII); deep and wide-field proper-motion studies of the old open clusters NGC 188, NGC 2682 (M67) and NGC 6791 (WOCS XVII, XXXIII and XLVI); comprehensive radial-velocity surveys of NGC 188, NGC 2168 and NGC 6819 (WOCS XXXII, XXIV, and XXXVIII); metallicity and lithium abundances in NGC 2168 (WOCS V); comprehensive definition of the hard-binary populations of NGC 188 and NGC 2168 (WOCS XXII and XLVIII); rotation period distributions in NGC 1039 (M34) and NGC 2168 (WOCS XXXV, XLIII, and XLV); study of chromospheric activity in NGC 2682 (WOCS XVIII); photometric variability surveys in NGC 188 and NGC 2682 (IX and XV); new Bayesian techniques for determination of cluster parameters (WOCS XXIII); a new infrared age-diagnostic for open clusters (WOCS XL); theoretical studies of stellar rotation (WOCS XIII and XIV); sophisticated N-body simulations of NGC 188 (WOCS LI); and the discovery of a high binary frequency and white dwarf companions among NGC 188 blue stragglers. While the WIYN 3.5m telescope remains at its heart, today the WIYN Open Cluster Study collaboration extends beyond both the WIYN observatory and consortium, and continues as a vital and productive

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Bayesian Modeling of a Human MMORPG Player

NASA Astrophysics Data System (ADS)

Synnaeve, Gabriel; Bessière, Pierre

2011-03-01

This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.

A two step Bayesian approach for genomic prediction of breeding values.

PubMed

Shariati, Mohammad M; Sørensen, Peter; Janss, Luc

2012-05-21

In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. The simulated data from the 15th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

PubMed

Ojo, Oluwatobi Blessing; Lougue, Siaka; Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors

PubMed Central

Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014. PMID:28257437

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

PubMed

Carriger, John F; Barron, Mace G; Newman, Michael C

2016-12-20

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

Characterization of full-length sequenced cDNA inserts (FLIcs) from Atlantic salmon (Salmo salar)

PubMed Central

Andreassen, Rune; Lunner, Sigbjørn; Høyheim, Bjørn

2009-01-01

Background Sequencing of the Atlantic salmon genome is now being planned by an international research consortium. Full-length sequenced inserts from cDNAs (FLIcs) are an important tool for correct annotation and clustering of the genomic sequence in any species. The large amount of highly similar duplicate sequences caused by the relatively recent genome duplication in the salmonid ancestor represents a particular challenge for the genome project. FLIcs will therefore be an extremely useful resource for the Atlantic salmon sequencing project. In addition to be helpful in order to distinguish between duplicate genome regions and in determining correct gene structures, FLIcs are an important resource for functional genomic studies and for investigation of regulatory elements controlling gene expression. In contrast to the large number of ESTs available, including the ESTs from 23 developmental and tissue specific cDNA libraries contributed by the Salmon Genome Project (SGP), the number of sequences where the full-length of the cDNA insert has been determined has been small. Results High quality full-length insert sequences from 560 pre-smolt white muscle tissue specific cDNAs were generated, accession numbers [GenBank: BT043497 - BT044056]. Five hundred and ten (91%) of the transcripts were annotated using Gene Ontology (GO) terms and 440 of the FLIcs are likely to contain a complete coding sequence (cCDS). The sequence information was used to identify putative paralogs, characterize salmon Kozak motifs, polyadenylation signal variation and to identify motifs likely to be involved in the regulation of particular genes. Finally, conserved 7-mers in the 3'UTRs were identified, of which some were identical to miRNA target sequences. Conclusion This paper describes the first Atlantic salmon FLIcs from a tissue and developmental stage specific cDNA library. We have demonstrated that many FLIcs contained a complete coding sequence (cCDS). This suggests that the remaining c

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

ERIC Educational Resources Information Center

Levy, Roy

2011-01-01

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…

Bayesian estimates of the incidence of rare cancers in Europe.

PubMed

Botta, Laura; Capocaccia, Riccardo; Trama, Annalisa; Herrmann, Christian; Salmerón, Diego; De Angelis, Roberta; Mallone, Sandra; Bidoli, Ettore; Marcos-Gragera, Rafael; Dudek-Godeau, Dorota; Gatta, Gemma; Cleries, Ramon

2018-04-21

The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. We analyzed about 2,000,000 rare cancers diagnosed in 2000-2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. Copyright © 2018 Elsevier Ltd. All rights reserved.

Digest: Demographic inferences accounting for selection at linked sites†.

PubMed

Simon, Alexis; Duranton, Maud

2018-05-16

Complex demography and selection at linked sites can generate spurious signatures of divergent selection. Unfortunately, many attempts at demographic inference consider overly simple models and neglect the effect of selection at linked sites. In this issue, Rougemont and Bernatchez (2018) applied an approximate Bayesian computation (ABC) framework that accounts for indirect selection to reveal a complex history of secondary contacts in Atlantic salmon (Salmo salar) that might explain a high rate of latitudinal clines in this species. © 2018 The Author(s). Evolution © 2018 The Society for the Study of Evolution.

A Bayesian Model of the Memory Colour Effect.

PubMed

Witzel, Christoph; Olkkonen, Maria; Gegenfurtner, Karl R

2018-01-01

According to the memory colour effect, the colour of a colour-diagnostic object is not perceived independently of the object itself. Instead, it has been shown through an achromatic adjustment method that colour-diagnostic objects still appear slightly in their typical colour, even when they are colourimetrically grey. Bayesian models provide a promising approach to capture the effect of prior knowledge on colour perception and to link these effects to more general effects of cue integration. Here, we model memory colour effects using prior knowledge about typical colours as priors for the grey adjustments in a Bayesian model. This simple model does not involve any fitting of free parameters. The Bayesian model roughly captured the magnitude of the measured memory colour effect for photographs of objects. To some extent, the model predicted observed differences in memory colour effects across objects. The model could not account for the differences in memory colour effects across different levels of realism in the object images. The Bayesian model provides a particularly simple account of memory colour effects, capturing some of the multiple sources of variation of these effects.

A Bayesian Model of the Memory Colour Effect

PubMed Central

Olkkonen, Maria; Gegenfurtner, Karl R.

2018-01-01

According to the memory colour effect, the colour of a colour-diagnostic object is not perceived independently of the object itself. Instead, it has been shown through an achromatic adjustment method that colour-diagnostic objects still appear slightly in their typical colour, even when they are colourimetrically grey. Bayesian models provide a promising approach to capture the effect of prior knowledge on colour perception and to link these effects to more general effects of cue integration. Here, we model memory colour effects using prior knowledge about typical colours as priors for the grey adjustments in a Bayesian model. This simple model does not involve any fitting of free parameters. The Bayesian model roughly captured the magnitude of the measured memory colour effect for photographs of objects. To some extent, the model predicted observed differences in memory colour effects across objects. The model could not account for the differences in memory colour effects across different levels of realism in the object images. The Bayesian model provides a particularly simple account of memory colour effects, capturing some of the multiple sources of variation of these effects. PMID:29760874

Rediscovery of Good-Turing estimators via Bayesian nonparametrics.

PubMed

Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye

2016-03-01

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library. © 2015, The International Biometric Society.

Single nucleotide polymorphism (SNP) discovery in duplicated genomes: intron-primed exon-crossing (IPEC) as a strategy for avoiding amplification of duplicated loci in Atlantic salmon (Salmo salar) and other salmonid fishes

PubMed Central

Ryynänen, Heikki J; Primmer, Craig R

2006-01-01

Background Single nucleotide polymorphisms (SNPs) represent the most abundant type of DNA variation in the vertebrate genome, and their applications as genetic markers in numerous studies of molecular ecology and conservation of natural populations are emerging. Recent large-scale sequencing projects in several fish species have provided a vast amount of data in public databases, which can be utilized in novel SNP discovery in salmonids. However, the suggested duplicated nature of the salmonid genome may hamper SNP characterization if the primers designed in conserved gene regions amplify multiple loci. Results Here we introduce a new intron-primed exon-crossing (IPEC) method in an attempt to overcome this duplication problem, and also evaluate different priming methods for SNP discovery in Atlantic salmon (Salmo salar) and other salmonids. A total of 69 loci with differing priming strategies were screened in S. salar, and 27 of these produced ~13 kb of high-quality sequence data consisting of 19 SNPs or indels (one per 680 bp). The SNP frequency and the overall nucleotide diversity (3.99 × 10-4) in S. salar was lower than reported in a majority of other organisms, which may suggest a relative young population history for Atlantic salmon. A subset of primers used in cross-species analyses revealed considerable variation in the SNP frequencies and nucleotide diversities in other salmonids. Conclusion Sequencing success was significantly higher with the new IPEC primers; thus the total number of loci to screen in order to identify one potential polymorphic site was six times less with this new strategy. Given that duplication may hamper SNP discovery in some species, the IPEC method reported here is an alternative way of identifying novel polymorphisms in such cases. PMID:16872523

With or without you: predictive coding and Bayesian inference in the brain

PubMed Central

Aitchison, Laurence; Lengyel, Máté

2018-01-01

Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic / representational motif that can serve several different computational goals of which Bayesian inference is but one. Conversely, while Bayesian inference can utilize predictive coding, it can also be realized by a variety of other representations. We critically evaluate the experimental evidence supporting Bayesian predictive coding and discuss how to test it more directly. PMID:28942084

Effects of episodic acidification on Atlantic salmon (Salmo salar) smolts

USGS Publications Warehouse

Magee, J.A.; Obedzinski, M.; McCormick, S.D.; Kocik, J.F.

2003-01-01

The effect of episodic acidification on Atlantic salmon (Salmo salar) smolt physiology and survival in fresh water (FW) and seawater (SW) was investigated. Smolts were held in either ambient (control, pH 6.0-6.6), acidified (chronic, pH 4.4-6.1), or episodically acidified (episodic, pH reduction from control levels to pH ???5.2 for 48 h once weekly) river water for 31 days and then transferred to 34??? SW. Smolts fed little while in acidified conditions and chronic smolts did not grow in length or weight. In FW, chronic smolts experienced increases in hematocrit and plasma potassium and reductions in plasma sodium and chloride. Upon transfer to SW, chronic and episodic smolts experienced reductions in hematocrit, increases in plasma sodium, chloride, and potassium levels, and suffered mortalities. Gill Na+,K+-ATPase and citrate synthase activities were reduced by exposure to acid. For most parameters, the effect of episodic acid exposure was less than that of chronic acidification. Exposure to acidic conditions, even when short in duration and followed by a 30-h recovery period in suitable water (pH 6.5), led to a 35% mortality of smolts upon transfer to SW. This study highlights the importance of measuring and assessing sublethal stresses in FW and their ultimate effects in marine ecosystems.

A critical life stage of the Atlantic salmon Salmo salar: behaviour and survival during the smolt and initial post-smolt migration.

PubMed

Thorstad, E B; Whoriskey, F; Uglem, I; Moore, A; Rikardsen, A H; Finstad, B

2012-07-01

The anadromous life cycle of Atlantic salmon Salmo salar involves long migrations to novel environments and challenging physiological transformations when moving between salt-free and salt-rich waters. In this article, (1) environmental factors affecting the migration behaviour and survival of smolts and post-smolts during the river, estuarine and early marine phases, (2) how behavioural patterns are linked to survival and (3) how anthropogenic factors affect migration and survival are synthesized and reviewed based on published literature. The timing of the smolt migration is important in determining marine survival. The timing varies among rivers, most likely as a consequence of local adaptations, to ensure sea entry during optimal periods. Smolts and post-smolts swim actively and fast during migration, but in areas with strong currents, their own movements may be overridden by current-induced transport. Progression rates during the early marine migration vary between 0.4 and 3.0 body lengths s(-1) relative to the ground. Reported mortality is 0.3-7.0% (median 2.3) km(-1) during downriver migration, 0.6-36% (median 6.0) km(-1) in estuaries and 0.3-3.4% (median 1.4) km(-1) in coastal areas. Estuaries and river mouths are the sites of the highest mortalities, with predation being a common cause. The mortality rates varied more among studies in estuaries than in rivers and marine areas, which probably reflects the huge variation among estuaries in their characteristics. Behaviour and survival during migration may also be affected by pollution, fish farming, sea lice Lepeophtheirus salmonis, hydropower development and other anthropogenic activities that may be directly lethal, delay migration or have indirect effects by inhibiting migration. Total mortality reported during early marine migration (up to 5-230 km from the river mouths) in the studies available to date varies between 8 and 71%. Hence, the early marine migration is a life stage with high mortalities, due

Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration.

PubMed

Bartlett, Jonathan W; Keogh, Ruth H

2018-06-01

Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

Bayesian characterization of uncertainty in species interaction strengths.

PubMed

Wolf, Christopher; Novak, Mark; Gitelman, Alix I

2017-06-01

Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We consider a means of characterizing the uncertainty of a generalist predator's interaction strengths by formulating an observational method for estimating a predator's prey-specific per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. A key insight is the informative nature of several so-called non-informative priors that have been used in modeling the sparse data typical of predator feeding surveys. We introduce to ecology a new neutral prior and provide evidence for its superior performance. We use a case study to consider the attack rates in a New Zealand intertidal whelk predator, and we illustrate not only that Bayesian point estimates can be made to correspond with those obtained by frequentist approaches, but also that estimation uncertainty as described by 95% intervals is more useful and biologically realistic using the Bayesian method. In particular, unlike in bootstrap confidence intervals, the lower bounds of the Bayesian posterior intervals for attack rates do not include zero when a predator-prey interaction is in fact observed. We conclude that the Bayesian framework provides a straightforward, probabilistic characterization of interaction strength uncertainty, enabling future considerations of both the deterministic and stochastic drivers of interaction strength and their impact on food webs.

Bayesian molecular dating: opening up the black box.

PubMed

Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

2018-05-01

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Comparison of sampling techniques for Bayesian parameter estimation

NASA Astrophysics Data System (ADS)

Allison, Rupert; Dunkley, Joanna

2014-02-01

The posterior probability distribution for a set of model parameters encodes all that the data have to tell us in the context of a given model; it is the fundamental quantity for Bayesian parameter estimation. In order to infer the posterior probability distribution we have to decide how to explore parameter space. Here we compare three prescriptions for how parameter space is navigated, discussing their relative merits. We consider Metropolis-Hasting sampling, nested sampling and affine-invariant ensemble Markov chain Monte Carlo (MCMC) sampling. We focus on their performance on toy-model Gaussian likelihoods and on a real-world cosmological data set. We outline the sampling algorithms themselves and elaborate on performance diagnostics such as convergence time, scope for parallelization, dimensional scaling, requisite tunings and suitability for non-Gaussian distributions. We find that nested sampling delivers high-fidelity estimates for posterior statistics at low computational cost, and should be adopted in favour of Metropolis-Hastings in many cases. Affine-invariant MCMC is competitive when computing clusters can be utilized for massive parallelization. Affine-invariant MCMC and existing extensions to nested sampling naturally probe multimodal and curving distributions.

Cluster-cluster clustering

NASA Technical Reports Server (NTRS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

Bayesian truthing as experimental verification of C4ISR sensors

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Romanov, Volodymyr; Wang, Wenjian; Nielsen, Thomas; Kostrzewski, Andrew

2015-05-01

In this paper, the general methodology for experimental verification/validation of C4ISR and other sensors' performance, is presented, based on Bayesian inference, in general, and binary sensors, in particular. This methodology, called Bayesian Truthing, defines Performance Metrics for binary sensors in: physics, optics, electronics, medicine, law enforcement, C3ISR, QC, ATR (Automatic Target Recognition), terrorism related events, and many others. For Bayesian Truthing, the sensing medium itself is not what is truly important; it is how the decision process is affected.

Bayesian outcome-based strategy classification.

PubMed

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Deep Galex Observations of the Coma Cluster: Source Catalog and Galaxy Counts

NASA Technical Reports Server (NTRS)

Hammer, D.; Hornschemeier, A. E.; Mobasher, B.; Miller, N.; Smith, R.; Arnouts, S.; Milliard, B.; Jenkins, L.

2010-01-01

We present a source catalog from deep 26 ks GALEX observations of the Coma cluster in the far-UV (FUV; 1530 Angstroms) and near-UV (NUV; 2310 Angstroms) wavebands. The observed field is centered 0.9 deg. (1.6 Mpc) south-west of the Coma core, and has full optical photometric coverage by SDSS and spectroscopic coverage to r-21. The catalog consists of 9700 galaxies with GALEX and SDSS photometry, including 242 spectroscopically-confirmed Coma member galaxies that range from giant spirals and elliptical galaxies to dwarf irregular and early-type galaxies. The full multi-wavelength catalog (cluster plus background galaxies) is 80% complete to NUV=23 and FUV=23.5, and has a limiting depth at NUV=24.5 and FUV=25.0 which corresponds to a star formation rate of 10(exp -3) solar mass yr(sup -1) at the distance of Coma. The GALEX images presented here are very deep and include detections of many resolved cluster members superposed on a dense field of unresolved background galaxies. This required a two-fold approach to generating a source catalog: we used a Bayesian deblending algorithm to measure faint and compact sources (using SDSS coordinates as a position prior), and used the GALEX pipeline catalog for bright and/or extended objects. We performed simulations to assess the importance of systematic effects (e.g. object blends, source confusion, Eddington Bias) that influence source detection and photometry when using both methods. The Bayesian deblending method roughly doubles the number of source detections and provides reliable photometry to a few magnitudes deeper than the GALEX pipeline catalog. This method is also free from source confusion over the UV magnitude range studied here: conversely, we estimate that the GALEX pipeline catalogs are confusion limited at NUV approximately 23 and FUV approximately 24. We have measured the total UV galaxy counts using our catalog and report a 50% excess of counts across FUV=22-23.5 and NUV=21.5-23 relative to previous GALEX

Bayesian Posterior Odds Ratios: Statistical Tools for Collaborative Evaluations

ERIC Educational Resources Information Center

Hicks, Tyler; Rodríguez-Campos, Liliana; Choi, Jeong Hoon

2018-01-01

To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

Mean Field Variational Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Vrettas, M.; Cornford, D.; Opper, M.

2012-04-01

Current data assimilation schemes propose a range of approximate solutions to the classical data assimilation problem, particularly state estimation. Broadly there are three main active research areas: ensemble Kalman filter methods which rely on statistical linearization of the model evolution equations, particle filters which provide a discrete point representation of the posterior filtering or smoothing distribution and 4DVAR methods which seek the most likely posterior smoothing solution. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the most probably posterior distribution over the states, within the family of non-stationary Gaussian processes. Our original work on variational Bayesian approaches to data assimilation sought the best approximating time varying Gaussian process to the posterior smoothing distribution for stochastic dynamical systems. This approach was based on minimising the Kullback-Leibler divergence between the true posterior over paths, and our Gaussian process approximation. So long as the observation density was sufficiently high to bring the posterior smoothing density close to Gaussian the algorithm proved very effective, on lower dimensional systems. However for higher dimensional systems, the algorithm was computationally very demanding. We have been developing a mean field version of the algorithm which treats the state variables at a given time as being independent in the posterior approximation, but still accounts for their relationships between each other in the mean solution arising from the original dynamical system. In this work we present the new mean field variational Bayesian approach, illustrating its performance on a range of classical data assimilation problems. We discuss the potential and limitations of the new approach. We emphasise that the variational Bayesian approach we adopt, in contrast to other variational approaches, provides a bound on the marginal likelihood of

Carcass analog addition enhances juvenile Atlantic salmon (Salmo salar) growth and condition

USGS Publications Warehouse

Guyette, Margaret Q.; Loftin, Cynthia S.; Zydlewski, Joseph D.

2013-01-01

Our study used historic marine-derived nutrient (MDN) delivery timing to simulate potential effects of restored connectivity on juvenile Atlantic salmon (ATS; Salmo salar) growth and condition. Four headwater streams were stocked with ATS young of the year (YOY) and received carcass analog additions (0.10 kg·m–2 wetted area) in treatment reaches to match the timing of sea lamprey (Petromyzon marinus) spawning. Individual ATS mass was 33%–48% greater and standard length was 9%–15% greater in treatment reaches relative to control reaches for 4 months following nutrient additions. Percent total lipids in YOY ATS were twice as great in treatment reaches 1 month following carcass analog additions and remained elevated in treatment fish for 2 more months. Absolute growth rates, based on otolith microstructure analysis, correlated with water temperature fluctuations in all reaches and were elevated by an average of 0.07 mm·day–1 in treatment reaches for 1 month following carcass analog additions. Simulated sea lamprey MDNs increased juvenile ATS growth, which, via potential increases in overwinter survival and decreases in smolt age, may contribute to population persistence and ecosystem productivity.

Uses and misuses of Bayes' rule and Bayesian classifiers in cybersecurity

NASA Astrophysics Data System (ADS)

Bard, Gregory V.

2017-12-01

This paper will discuss the applications of Bayes' Rule and Bayesian Classifiers in Cybersecurity. While the most elementary form of Bayes' rule occurs in undergraduate coursework, there are more complicated forms as well. As an extended example, Bayesian spam filtering is explored, and is in many ways the most triumphant accomplishment of Bayesian reasoning in computer science, as nearly everyone with an email address has a spam folder. Bayesian Classifiers have also been responsible significant cybersecurity research results; yet, because they are not part of the standard curriculum, few in the mathematics or information-technology communities have seen the exact definitions, requirements, and proofs that comprise the subject. Moreover, numerous errors have been made by researchers (described in this paper), due to some mathematical misunderstandings dealing with conditional independence, or other badly chosen assumptions. Finally, to provide instructors and researchers with real-world examples, 25 published cybersecurity papers that use Bayesian reasoning are given, with 2-4 sentence summaries of the focus and contributions of each paper.

Population forecasts for Bangladesh, using a Bayesian methodology.

PubMed

Mahsin, Md; Hossain, Syed Shahadat

2012-12-01

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

NASA Astrophysics Data System (ADS)

Swinburne, Thomas D.; Perez, Danny

2018-05-01

A massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Peng, E-mail: peng@ices.utexas.edu; Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch

2016-07-01

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by themore » so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online

Space Shuttle RTOS Bayesian Network

NASA Technical Reports Server (NTRS)

Morris, A. Terry; Beling, Peter A.

2001-01-01

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores

SNP-array reveals genome-wide patterns of geographical and potential adaptive divergence across the natural range of Atlantic salmon (Salmo salar).

PubMed

Bourret, Vincent; Kent, Matthew P; Primmer, Craig R; Vasemägi, Anti; Karlsson, Sten; Hindar, Kjetil; McGinnity, Philip; Verspoor, Eric; Bernatchez, Louis; Lien, Sigbjørn

2013-02-01

Atlantic salmon (Salmo salar) is one of the most extensively studied fish species in the world due to its significance in aquaculture, fisheries and ongoing conservation efforts to protect declining populations. Yet, limited genomic resources have hampered our understanding of genetic architecture in the species and the genetic basis of adaptation to the wide range of natural and artificial environments it occupies. In this study, we describe the development of a medium-density Atlantic salmon single nucleotide polymorphism (SNP) array based on expressed sequence tags (ESTs) and genomic sequencing. The array was used in the most extensive assessment of population genetic structure performed to date in this species. A total of 6176 informative SNPs were successfully genotyped in 38 anadromous and freshwater wild populations distributed across the species natural range. Principal component analysis clearly differentiated European and North American populations, and within Europe, three major regional genetic groups were identified for the first time in a single analysis. We assessed the potential for the array to disentangle neutral and putative adaptive divergence of SNP allele frequencies across populations and among regional groups. In Europe, secondary contact zones were identified between major clusters where endogenous and exogenous barriers could be associated, rendering the interpretation of environmental influence on potentially adaptive divergence equivocal. A small number of markers highly divergent in allele frequencies (outliers) were observed between (multiple) freshwater and anadromous populations, between northern and southern latitudes, and when comparing Baltic populations to all others. We also discuss the potential future applications of the SNP array for conservation, management and aquaculture. © 2012 Blackwell Publishing Ltd.

COSMOABC: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Ishida, E. E. O.; Vitenti, S. D. P.; Penna-Lima, M.; Cisewski, J.; de Souza, R. S.; Trindade, A. M. M.; Cameron, E.; Busti, V. C.; COIN Collaboration

2015-11-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present COSMOABC, a Python ABC sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled COSMOABC with the NUMCOSMO library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. COSMOABC is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX.

A new specific reference gene based on growth hormone gene (GH1) used for detection and relative quantification of Aquadvantage® GM salmon (Salmo salar L.) in food products.

PubMed

Hafsa, Ahmed Ben; Nabi, Nesrine; Zellama, Mohamed Salem; Said, Khaled; Chaouachi, Maher

2016-01-01

Genetic transformation of fish is mainly oriented towards the improvement of growth for the benefit of the aquaculture. Actually, Atlantic salmon (Salmo salar) is the species most transformed to achieve growth rates quite large compared to the wild. To anticipate the presence of contaminations with GM salmon in fish markets and the lack of labeling regulations with a mandatory threshold, the proper methods are needed to test the authenticity of the ingredients. A quantitative real-time polymerase chain reaction (QRT-PCR) method was used in this study. Ct values were obtained and validated using 15 processed food containing salmon. The relative and absolute limits of detection were 0.01% and 0.01 ng/μl of genomic DNA, respectively. Results demonstrate that the developed QRT-PCR method is suitable specifically for identification of S. salar in food ingredients based on the salmon growth hormone gene 1 (GH1). The processes used to develop the specific salmon reference gene case study are intended to serve as a model for performing quantification of Aquadvantage® GM salmon on future genetically modified (GM) fish to be commercialized. Copyright © 2015 Elsevier Ltd. All rights reserved.

Arteriosclerosis in the ventral aorta and epicarditis in the bulbus arteriosus of Atlantic salmon (Salmo salar L).

PubMed

Dalum, A S; Kristthorsdottir, K H; Griffiths, D J; Bjørklund, K; Poppe, T T

2017-06-01

Spontaneous mortality of seemingly healthy, farmed Atlantic salmon (Salmo salar L) is an increasing problem in Norwegian aquaculture. In this study, we present a morphological study of the previously undescribed syndrome of arteriosclerosis of the ventral aorta and epicarditis of the adjacent bulbus arteriosus found in farmed Atlantic salmon, with wild-captured fish as a control group. Both the ventral aorta and epicardium are vital for correct arterial compliance and vascular resistance in the respiratory capillaries of the gills. We discuss the possible implications of ventral aorta arteriosclerosis and epicarditis for blood vascular health and in particular for the increasing frequency of spontaneous gill bleeding in farmed salmon. As both these conditions primarily occur in farmed salmon, we suggest that they should be considered pathological. © 2016 John Wiley & Sons Ltd.

Bayesian Analysis of Longitudinal Data Using Growth Curve Models

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J.

2007-01-01

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…

Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation

ERIC Educational Resources Information Center

Ross, Steven J.; Mackey, Beth

2015-01-01

This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…

Ockham's razor and Bayesian analysis. [statistical theory for systems evaluation

NASA Technical Reports Server (NTRS)

Jefferys, William H.; Berger, James O.

1992-01-01

'Ockham's razor', the ad hoc principle enjoining the greatest possible simplicity in theoretical explanations, is presently shown to be justifiable as a consequence of Bayesian inference; Bayesian analysis can, moreover, clarify the nature of the 'simplest' hypothesis consistent with the given data. By choosing the prior probabilities of hypotheses, it becomes possible to quantify the scientific judgment that simpler hypotheses are more likely to be correct. Bayesian analysis also shows that a hypothesis with fewer adjustable parameters intrinsically possesses an enhanced posterior probability, due to the clarity of its predictions.

The effect of shelter on welfare of juvenile Atlantic salmon Salmo salar reared under a feed restriction regimen.

PubMed

Persson, L; Alanärä, A

2014-09-01

This study investigated whether shelter (presence or absence) affected the frequency of fin damage in Atlantic salmon Salmo salar exposed to feed restrictions (0·73 or 0·33% of body mass day(-1) ). The presence of shelter had a positive effect on the pectoral fins at both feed ration levels and on the dorsal fin at the higher ration level but it had a negative effect on survival. The reduced feed rations resulted in fish of the same size and nutritional status as wild fish. The provision of shelter has potential to mitigate the negative effects of feed restrictions on fin quality, but the optimal shelter design requires some additional investigation. © 2014 The Fisheries Society of the British Isles.

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

A Bayesian approach to reliability and confidence

NASA Technical Reports Server (NTRS)

Barnes, Ron

1989-01-01

The historical evolution of NASA's interest in quantitative measures of reliability assessment is outlined. The introduction of some quantitative methodologies into the Vehicle Reliability Branch of the Safety, Reliability and Quality Assurance (SR and QA) Division at Johnson Space Center (JSC) was noted along with the development of the Extended Orbiter Duration--Weakest Link study which will utilize quantitative tools for a Bayesian statistical analysis. Extending the earlier work of NASA sponsor, Richard Heydorn, researchers were able to produce a consistent Bayesian estimate for the reliability of a component and hence by a simple extension for a system of components in some cases where the rate of failure is not constant but varies over time. Mechanical systems in general have this property since the reliability usually decreases markedly as the parts degrade over time. While they have been able to reduce the Bayesian estimator to a simple closed form for a large class of such systems, the form for the most general case needs to be attacked by the computer. Once a table is generated for this form, researchers will have a numerical form for the general solution. With this, the corresponding probability statements about the reliability of a system can be made in the most general setting. Note that the utilization of uniform Bayesian priors represents a worst case scenario in the sense that as researchers incorporate more expert opinion into the model, they will be able to improve the strength of the probability calculations.

Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.

Robust Bayesian Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Yuan, Ke-Hai

2003-01-01

Bayesian factor analysis (BFA) assumes the normal distribution of the current sample conditional on the parameters. Practical data in social and behavioral sciences typically have significant skewness and kurtosis. If the normality assumption is not attainable, the posterior analysis will be inaccurate, although the BFA depends less on the current…

Testing adaptive toolbox models: a Bayesian hierarchical approach.

PubMed

Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

2013-01-01

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

Nonlinear and non-Gaussian Bayesian based handwriting beautification

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2013-03-01

A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Applications of Bayesian spectrum representation in acoustics

NASA Astrophysics Data System (ADS)

Botts, Jonathan M.

This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

Dietary soybean protein concentrate-induced intestinal disorder in marine farmed Atlantic salmon, Salmo salar is associated with alterations in gut microbiota.

PubMed

Green, Timothy J; Smullen, Richard; Barnes, Andrew C

2013-09-27

The aquaculture industry has made substantial progress in reducing the fishmeal content of feeds for carnivorous species, driven by demand for improved sustainability and reduced cost. Soybean protein concentrate (SPC) is an attractive replacement for fishmeal, but intestinal disorders have been reported in Atlantic salmon (Salmo salar) fed these diets at high seawater temperatures, with preliminary evidence suggesting SPC induces these disorders by altering the intestinal microbiota. We compared the intestinal microbiota of marine-farmed S. salar fed experimental diets with varying levels of SPC in mid- and late-summer. Terminal restriction fragment length polymorphism (T-RFLP) and 16S rRNA clone library analysis revealed the microbiota adherent to the intestinal tract of salmon is complex at the population level, but simple and highly variable at the individual level. Temporal changes were observed with the bacterial diversity increasing in the intestinal tract in late summer. A Verrucomicrobia was the most frequently observed ribotype in early summer, whilst an Aliivibrio was the most frequently observed ribotype in late summer. Feeding SPC to salmon increased the bacterial diversity of the intestinal tract and resulted in the presence of bacteria not normally associated with marine fish (Escherichia and Propionibacterium). These diet-induced changes to the intestinal-microbiome could be ameliorated by inclusion of a prebiotic (mannan-oligosaccharide or MOS) to the diet. None of the experimental diets induced inflammation of the intestine as assessed by histopathology and expression of inflammatory cytokines. Our results support the "dysbiosis" hypothesis that SPC adversely affects the intestinal microbiota of Atlantic salmon. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2012-01-01

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

Maladaptation and phenotypic mismatch in hatchery-reared Atlantic salmon Salmo salar released in the wild.

PubMed

Stringwell, R; Lock, A; Stutchbury, C J; Baggett, E; Taylor, J; Gough, P J; Garcia de Leaniz, C

2014-12-01

Changes in body shape, fluctuating asymmetry (FA) and crypsis were compared among Atlantic salmon Salmo salar fry kept as controls in captivity and those released and subsequently recaptured in the wild according to a before-after-control-impact (BACI) design. Hatchery fish that survived in the wild became more cryptic and displayed a much lower incidence of fin erosion and of asymmetric individuals than control fish kept in captivity. Significant differences in body shape were also apparent, and survivors had longer heads, thicker caudal peduncles and a more streamlined body shape than hatchery controls as early as 20 days following stocking, most likely as a result of phenotypic plasticity and non-random, selective mortality of maladapted phenotypes. Hatchery-reared fish typically perform poorly in the wild and the results of this study indicate that this may be due to phenotypic mismatch, i.e. because hatcheries generate fish that are phenotypically mismatched to the natural environment. © 2014 The Fisheries Society of the British Isles.

Integrating across scales: Effectively applying science for the successful conservation of Atlantic salmon (Salmo salar)

USGS Publications Warehouse

Mather, M. E.; Parrish, D.L.; Folt, C.L.; DeGraaf, R.M.

1998-01-01

Atlantic salmon (Salmo salar) is an excellent species on which to focus synthetic, integrative investigations because it is an economically important species that captures the public imagination, is heavily impacted by humans, uses several ecosystems over its life, and is the subject of a large body of extant literature. The following 24 papers were solicited to provide the biological basis for effective and innovative approaches that biologists, managers, and social scientists can use to develop policies that sustain Atlantic salmon and related species. Together these papers highlight the need for and benefits of (a) synthesizing within populations, (b) choosing the appropriate scale, (c) comparing across populations using rigorous, focused, question-oriented methods, (d) integrating across disciplines, (e) incorporating the human perspective, (f) linking multiple ecosystems, and (g) applied problem solving. To show how Atlantic salmon can guide research and conservation efforts for other species in other systems, we review the justification for the supplement and summarize the defining concepts that emerge from the volume.

First description of atypical furunculosis in freshwater farmed Atlantic salmon, Salmo salar L., in Chile.

PubMed

Godoy, M; Gherardelli, V; Heisinger, A; Fernández, J; Olmos, P; Ovalle, L; Ilardi, P; Avendaño-Herrera, R

2010-05-01

We report the first isolation, identification and characterization of a group of Chilean strains of atypical Aeromonas salmonicida isolated from freshwater farmed Atlantic salmon, Salmo salar. Affected fish showed superficial ulcers and pale liver with or without petechial haemorrhages. Outbreaks of the disease occurred in two farms in the south of Chile about 2200 km apart. Five strains were isolated in pure culture and identified by serological assays and immunofluorescence tests as belonging to Aeromonas salmonicida. Although the bacterial isolates were phenotypically homogeneous, minor differences with the reference strain A. salmonicida subsp. salmonicida ATCC 33658 were noted. Three specific primer sets and partial 16S rRNA gene sequencing allowed the identification of the Chilean isolates as atypical A. salmonicida, with A. salmonicida subsp. achromogenes and A. salmonicida subsp. masoucida as their closest relatives (100% sequence similarity). Molecular typing indicated that the atypical isolates belong to two genetic groups that were associated with the geographical origin.

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

ERIC Educational Resources Information Center

Chen, Jianshen; Kaplan, David

2015-01-01

Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different…

A study of finite mixture model: Bayesian approach on financial time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

[Bayesian statistics in medicine -- part II: main applications and inference].

PubMed

Montomoli, C; Nichelatti, M

2008-01-01

Bayesian statistics is not only used when one is dealing with 2-way tables, but it can be used for inferential purposes. Using the basic concepts presented in the first part, this paper aims to give a simple overview of Bayesian methods by introducing its foundation (Bayes' theorem) and then applying this rule to a very simple practical example; whenever possible, the elementary processes at the basis of analysis are compared to those of frequentist (classical) statistical analysis. The Bayesian reasoning is naturally connected to medical activity, since it appears to be quite similar to a diagnostic process.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.

PubMed

Schmidt, Amand F; Klugkist, Irene; Klungel, Olaf H; Nielen, Mirjam; de Boer, Anthonius; Hoes, Arno W; Groenwold, Rolf H H

2015-04-01

Findings from nonrandomized studies on safety or efficacy of treatment in patient subgroups may trigger postlaunch randomized clinical trials (RCTs). In the analysis of such RCTs, results from nonrandomized studies are typically ignored. This study explores the trade-off between bias and power of Bayesian RCT analysis incorporating information from nonrandomized studies. A simulation study was conducted to compare frequentist with Bayesian analyses using noninformative and informative priors in their ability to detect interaction effects. In simulated subgroups, the effect of a hypothetical treatment differed between subgroups (odds ratio 1.00 vs. 2.33). Simulations varied in sample size, proportions of the subgroups, and specification of the priors. As expected, the results for the informative Bayesian analyses were more biased than those from the noninformative Bayesian analysis or frequentist analysis. However, because of a reduction in posterior variance, informative Bayesian analyses were generally more powerful to detect an effect. In scenarios where the informative priors were in the opposite direction of the RCT data, type 1 error rates could be 100% and power 0%. Bayesian methods incorporating data from nonrandomized studies can meaningfully increase power of interaction tests in postlaunch RCTs. Copyright © 2015 Elsevier Inc. All rights reserved.

Bayesian analysis of caustic-crossing microlensing events

NASA Astrophysics Data System (ADS)

Cassan, A.; Horne, K.; Kains, N.; Tsapras, Y.; Browne, P.

2010-06-01

Aims: Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping to decide whether a planet is detected by an event. Cassan introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods: We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results: Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Formation history of open clusters constrained by detailed asteroseismology of red giant stars observed by Kepler

NASA Astrophysics Data System (ADS)

Corsaro, Enrico; Lee, Yueh-Ning; García, Rafael A.; Hennebelle, Patrick; Mathur, Savita; Beck, Paul G.; Mathis, Stephane; Stello, Dennis; Bouvier, Jérôme

2017-10-01

Stars originate by the gravitational collapse of a turbulent molecular cloud of a diffuse medium, and are often observed to form clusters. Stellar clusters therefore play an important role in our understanding of star formation and of the dynamical processes at play. However, investigating the cluster formation is diffcult because the density of the molecular cloud undergoes a change of many orders of magnitude. Hierarchical-step approaches to decompose the problem into different stages are therefore required, as well as reliable assumptions on the initial conditions in the clouds. We report for the first time the use of the full potential of NASA Kepler asteroseismic observations coupled with 3D numerical simulations, to put strong constraints on the early formation stages of open clusters. Thanks to a Bayesian peak bagging analysis of about 50 red giant members of NGC 6791 and NGC 6819, the two most populated open clusters observed in the nominal Kepler mission, we derive a complete set of detailed oscillation mode properties for each star, with thousands of oscillation modes characterized. We therefore show how these asteroseismic properties lead us to a discovery about the rotation history of stellar clusters. Finally, our observational findings will be compared with hydrodynamical simulations for stellar cluster formation to constrain the physical processes of turbulence, rotation, and magnetic fields that are in action during the collapse of the progenitor cloud into a proto-cluster.

Prokaryotic diversity and biogeochemical characteristics of benthic microbial ecosystems at La Brava, a hypersaline lake at Salar de Atacama, Chile.

PubMed

Farias, Maria Eugenia; Rasuk, Maria Cecilia; Gallagher, Kimberley L; Contreras, Manuel; Kurth, Daniel; Fernandez, Ana Beatriz; Poiré, Daniel; Novoa, Fernando; Visscher, Pieter T

2017-01-01

Benthic microbial ecosystems of Laguna La Brava, Salar de Atacama, a high altitude hypersaline lake, were characterized in terms of bacterial and archaeal diversity, biogeochemistry, (including O2 and sulfide depth profiles and mineralogy), and physicochemical characteristics. La Brava is one of several lakes in the Salar de Atacama where microbial communities are growing in extreme conditions, including high salinity, high solar insolation, and high levels of metals such as lithium, arsenic, magnesium, and calcium. Evaporation creates hypersaline conditions in these lakes and mineral precipitation is a characteristic geomicrobiological feature of these benthic ecosystems. In this study, the La Brava non-lithifying microbial mats, microbialites, and rhizome-associated concretions were compared to each other and their diversity was related to their environmental conditions. All the ecosystems revealed an unusual community where Euryarchaeota, Crenarchaeota, Acetothermia, Firmicutes and Planctomycetes were the most abundant groups, and cyanobacteria, typically an important primary producer in microbial mats, were relatively insignificant or absent. This suggests that other microorganisms, and possibly novel pathways unique to this system, are responsible for carbon fixation. Depth profiles of O2 and sulfide showed active production and respiration. The mineralogy composition was calcium carbonate (as aragonite) and increased from mats to microbialites and rhizome-associated concretions. Halite was also present. Further analyses were performed on representative microbial mats and microbialites by layer. Different taxonomic compositions were observed in the upper layers, with Archaea dominating the non-lithifying mat, and Planctomycetes the microbialite. The bottom layers were similar, with Euryarchaeota, Crenarchaeota and Planctomycetes as dominant phyla. Sequences related to Cyanobacteria were very scarce. These systems may contain previously uncharacterized

Prokaryotic diversity and biogeochemical characteristics of benthic microbial ecosystems at La Brava, a hypersaline lake at Salar de Atacama, Chile

PubMed Central

Rasuk, Maria Cecilia; Gallagher, Kimberley L.; Contreras, Manuel; Kurth, Daniel; Fernandez, Ana Beatriz; Poiré, Daniel; Novoa, Fernando; Visscher, Pieter T.

2017-01-01

Benthic microbial ecosystems of Laguna La Brava, Salar de Atacama, a high altitude hypersaline lake, were characterized in terms of bacterial and archaeal diversity, biogeochemistry, (including O2 and sulfide depth profiles and mineralogy), and physicochemical characteristics. La Brava is one of several lakes in the Salar de Atacama where microbial communities are growing in extreme conditions, including high salinity, high solar insolation, and high levels of metals such as lithium, arsenic, magnesium, and calcium. Evaporation creates hypersaline conditions in these lakes and mineral precipitation is a characteristic geomicrobiological feature of these benthic ecosystems. In this study, the La Brava non-lithifying microbial mats, microbialites, and rhizome-associated concretions were compared to each other and their diversity was related to their environmental conditions. All the ecosystems revealed an unusual community where Euryarchaeota, Crenarchaeota, Acetothermia, Firmicutes and Planctomycetes were the most abundant groups, and cyanobacteria, typically an important primary producer in microbial mats, were relatively insignificant or absent. This suggests that other microorganisms, and possibly novel pathways unique to this system, are responsible for carbon fixation. Depth profiles of O2 and sulfide showed active production and respiration. The mineralogy composition was calcium carbonate (as aragonite) and increased from mats to microbialites and rhizome-associated concretions. Halite was also present. Further analyses were performed on representative microbial mats and microbialites by layer. Different taxonomic compositions were observed in the upper layers, with Archaea dominating the non-lithifying mat, and Planctomycetes the microbialite. The bottom layers were similar, with Euryarchaeota, Crenarchaeota and Planctomycetes as dominant phyla. Sequences related to Cyanobacteria were very scarce. These systems may contain previously uncharacterized

Bayesian posterior distributions without Markov chains.

PubMed

Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B

2012-03-01

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

The Application of Bayesian Analysis to Issues in Developmental Research

ERIC Educational Resources Information Center

Walker, Lawrence J.; Gustafson, Paul; Frimer, Jeremy A.

2007-01-01

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

A Tutorial Introduction to Bayesian Models of Cognitive Development

ERIC Educational Resources Information Center

Perfors, Amy; Tenenbaum, Joshua B.; Griffiths, Thomas L.; Xu, Fei

2011-01-01

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the "what", the "how", and the "why" of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for…

Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

2016-02-04

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Bayesian methods for characterizing unknown parameters of material models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

A Bayesian approach to multivariate measurement system assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

Identification and Migration of Primordial Germ Cells in Atlantic Salmon, Salmo salar: Characterization of Vasa, Dead End, and Lymphocyte Antigen 75 Genes

PubMed Central

Nagasawa, Kazue; Fernandes, Jorge MO; Yoshizaki, Goro; Miwa, Misako; Babiak, Igor

2013-01-01

No information exists on the identification of primordial germ cells (PGCs) in the super-order Protacanthopterygii, which includes the Salmonidae family and Atlantic salmon (Salmo salar L.), one of the most commercially important aquatic animals worldwide. In order to identify salmon PGCs, we cloned the full-length cDNA of vasa, dead end (dnd), and lymphocyte antigen 75 (ly75/CD205) genes as germ cell marker candidates, and analyzed their expression patterns in both adult and embryonic stages of Atlantic salmon. Semi-quantitative RT-PCR results showed that salmon vasa and dnd were specifically expressed in testis and ovary, and vasa, dnd, and ly75 mRNA were maternally deposited in the egg. vasa mRNA was consistently detected throughout embryogenesis while dnd and ly75 mRNA were gradually degraded during cleavages. In situ analysis revealed the localization of vasa and dnd mRNA and Ly75 protein in PGCs of hatched larvae. Whole-mount in situ hybridization detected vasa mRNA during embryogenesis, showing a distribution pattern somewhat different to that of zebrafish; specifically, at mid-blastula stage, vasa-expressing cells were randomly distributed at the central part of blastodisc, and then they migrated to the presumptive region of embryonic shield. Therefore, the typical vasa localization pattern of four clusters during blastulation, as found in zebrafish, was not present in Atlantic salmon. In addition, salmon PGCs could be specifically labeled with a green fluorescence protein (GFP) using gfp-rt-vasa 3′-UTR RNA microinjection for further applications. These findings may assist in understanding PGC development not only in Atlantic salmon but also in other salmonids. PMID:23239145

Spectral Bayesian Knowledge Tracing

ERIC Educational Resources Information Center

Falakmasir, Mohammad; Yudelson, Michael; Ritter, Steve; Koedinger, Ken

2015-01-01

Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is…

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Heuristics as Bayesian inference under extreme priors.

PubMed

Parpart, Paula; Jones, Matt; Love, Bradley C

2018-05-01

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Quality grading of Atlantic salmon (Salmo salar) by computer vision.

PubMed

Misimi, E; Erikson, U; Skavhaug, A

2008-06-01

In this study, we present a promising method of computer vision-based quality grading of whole Atlantic salmon (Salmo salar). Using computer vision, it was possible to differentiate among different quality grades of Atlantic salmon based on the external geometrical information contained in the fish images. Initially, before the image acquisition, the fish were subjectively graded and labeled into grading classes by a qualified human inspector in the processing plant. Prior to classification, the salmon images were segmented into binary images, and then feature extraction was performed on the geometrical parameters of the fish from the grading classes. The classification algorithm was a threshold-based classifier, which was designed using linear discriminant analysis. The performance of the classifier was tested by using the leave-one-out cross-validation method, and the classification results showed a good agreement between the classification done by human inspectors and by the computer vision. The computer vision-based method classified correctly 90% of the salmon from the data set as compared with the classification by human inspector. Overall, it was shown that computer vision can be used as a powerful tool to grade Atlantic salmon into quality grades in a fast and nondestructive manner by a relatively simple classifier algorithm. The low cost of implementation of today's advanced computer vision solutions makes this method feasible for industrial purposes in fish plants as it can replace manual labor, on which grading tasks still rely.

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

On the Adequacy of Bayesian Evaluations of Categorization Models: Reply to Vanpaemel and Lee (2012)

ERIC Educational Resources Information Center

Wills, Andy J.; Pothos, Emmanuel M.

2012-01-01

Vanpaemel and Lee (2012) argued, and we agree, that the comparison of formal models can be facilitated by Bayesian methods. However, Bayesian methods neither precede nor supplant our proposals (Wills & Pothos, 2012), as Bayesian methods can be applied both to our proposals and to their polar opposites. Furthermore, the use of Bayesian methods to…

Characterization of Mycobacterium salmoniphilum as causal agent of mycobacteriosis in Atlantic salmon, Salmo salar L., from a freshwater recirculation system.

PubMed

Aro, L; Correa, K; Martínez, A; Ildefonso, R; Yáñez, J M

2014-04-01

Thirty Atlantic salmon, Salmo salar L., with low corporal condition relative to other fish present in the culture system, were sampled from a freshwater recirculation pisciculture located in Chile. The most characteristic signs and lesions were cachexia and presence of multiple greyish-white granulomas within internal organs. The external and internal lesions, along with the microscopic, histologic and biochemical findings, were consistent with mycobacteriosis. The identification of Mycobacterium salmoniphilum as the causal agent of the lesions was possible through the use of molecular analyses. This study represents the first report of Mycobacterium salmoniphilum in a freshwater recirculation system and the first case of fish mycobacteriosis described in Chile. © 2013 John Wiley & Sons Ltd.

Using Alien Coins to Test Whether Simple Inference Is Bayesian

ERIC Educational Resources Information Center

Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

2016-01-01

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Ova fecundity in Scottish Atlantic salmon Salmo salar: predictions, selective forces and causal mechanisms.

PubMed

Bacon, P J; MacLean, J C; Malcolm, I A; Gurney, W S C

2012-08-01

Ova fecundities of Scottish Atlantic salmon Salmo salar, predicted from log(10) regression of ova numbers and female fork length (L(F)), differed widely between upland and lowland stocks within the same river, whereas sea-age, river and year factors had insignificant effects on fecundity once L(F) was accounted for. For upland fish, the relationship between log(10)L(F) and log(10) ova mass (M(O)) was stable between two datasets collected 40 years apart. Although upland and lowland females both produced comparable log(10)M(O) (log(10)L(F))(-1), lowland females partitioned this into 45% more, but smaller ova, whereas upland females produced fewer, but larger, eggs. The possible causes and implications of this are discussed for evolutionary perspectives (lifetime production), population structure (local tributary v. large catchments; environmental effects), population dynamics and stability (density-dependent control mechanisms) and fisheries management (stock-recruitment; short and long-term stock sustainability). © 2012 Marine Scotland. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Reproductive performance of alternative male phenotypes of growth hormone transgenic Atlantic salmon (Salmo salar)

PubMed Central

Moreau, Darek T R; Conway, Corinne; Fleming, Ian A

2011-01-01

Growth hormone (GH) transgenic Atlantic salmon (Salmo salar) is one of the first transgenic animals being considered for commercial farming, yet ecological and genetic concerns remain should they enter the wild and interact reproductively with wild fish. Here, we provide the first empirical data reporting on the breeding performance of GH transgenic Atlantic salmon males, including that of an alternative male reproductive phenotype (i.e. small, precocially mature parr), in pair-wise competitive trials within a naturalised stream mesocosm. Wild anadromous (i.e. large, migratory) males outperformed captively reared transgenic counterparts in terms of nest fidelity, quivering frequency and spawn participation. Similarly, despite displaying less aggression, captively reared nontransgenic mature parr were superior competitors to their transgenic counterparts in terms of nest fidelity and spawn participation. Moreover, nontransgenic parr had higher overall fertilisation success than transgenic parr, and their offspring were represented in more spawning trials. Although transgenic males displayed reduced breeding performance relative to nontransgenics, both male reproductive phenotypes demonstrated the ability to participate in natural spawning events and thus have the potential to contribute genes to subsequent generations. PMID:25568019

Assessing Vermont's stream health and biological integrity using artificial neural networks and Bayesian methods

NASA Astrophysics Data System (ADS)

Rizzo, D. M.; Fytilis, N.; Stevens, L.

2012-12-01

Environmental managers are increasingly required to monitor and forecast long-term effects and vulnerability of biophysical systems to human-generated stresses. Ideally, a study involving both physical and biological assessments conducted concurrently (in space and time) could provide a better understanding of the mechanisms and complex relationships. However, costs and resources associated with monitoring the complex linkages between the physical, geomorphic and habitat conditions and the biological integrity of stream reaches are prohibitive. Researchers have used classification techniques to place individual streams and rivers into a broader spatial context (hydrologic or health condition). Such efforts require environmental managers to gather multiple forms of information - quantitative, qualitative and subjective. We research and develop a novel classification tool that combines self-organizing maps with a Naïve Bayesian classifier to direct resources to stream reaches most in need. The Vermont Agency of Natural Resources has developed and adopted protocols for physical stream geomorphic and habitat assessments throughout the state of Vermont. Separate from these assessments, the Vermont Department of Environmental Conservation monitors the biological communities and the water quality in streams. Our initial hypothesis is that the geomorphic reach assessments and water quality data may be leveraged to reduce error and uncertainty associated with predictions of biological integrity and stream health. We test our hypothesis using over 2500 Vermont stream reaches (~1371 stream miles) assessed by the two agencies. In the development of this work, we combine a Naïve Bayesian classifier with a modified Kohonen Self-Organizing Map (SOM). The SOM is an unsupervised artificial neural network that autonomously analyzes inherent dataset properties using input data only. It is typically used to cluster data into similar categories when a priori classes do not exist. The

Finite‐fault Bayesian inversion of teleseismic body waves

USGS Publications Warehouse

Clayton, Brandon; Hartzell, Stephen; Moschetti, Morgan P.; Minson, Sarah E.

2017-01-01

Inverting geophysical data has provided fundamental information about the behavior of earthquake rupture. However, inferring kinematic source model parameters for finite‐fault ruptures is an intrinsically underdetermined problem (the problem of nonuniqueness), because we are restricted to finite noisy observations. Although many studies use least‐squares techniques to make the finite‐fault problem tractable, these methods generally lack the ability to apply non‐Gaussian error analysis and the imposition of nonlinear constraints. However, the Bayesian approach can be employed to find a Gaussian or non‐Gaussian distribution of all probable model parameters, while utilizing nonlinear constraints. We present case studies to quantify the resolving power and associated uncertainties using only teleseismic body waves in a Bayesian framework to infer the slip history for a synthetic case and two earthquakes: the 2011 Mw 7.1 Van, east Turkey, earthquake and the 2010 Mw 7.2 El Mayor–Cucapah, Baja California, earthquake. In implementing the Bayesian method, we further present two distinct solutions to investigate the uncertainties by performing the inversion with and without velocity structure perturbations. We find that the posterior ensemble becomes broader when including velocity structure variability and introduces a spatial smearing of slip. Using the Bayesian framework solely on teleseismic body waves, we find rake is poorly constrained by the observations and rise time is poorly resolved when slip amplitude is low.

Star Cluster Properties in Two LEGUS Galaxies Computed with Stochastic Stellar Population Synthesis Models

NASA Astrophysics Data System (ADS)

Krumholz, Mark R.; Adamo, Angela; Fumagalli, Michele; Wofford, Aida; Calzetti, Daniela; Lee, Janice C.; Whitmore, Bradley C.; Bright, Stacey N.; Grasha, Kathryn; Gouliermis, Dimitrios A.; Kim, Hwihyun; Nair, Preethi; Ryon, Jenna E.; Smith, Linda J.; Thilker, David; Ubeda, Leonardo; Zackrisson, Erik

2015-10-01

We investigate a novel Bayesian analysis method, based on the Stochastically Lighting Up Galaxies (slug) code, to derive the masses, ages, and extinctions of star clusters from integrated light photometry. Unlike many analysis methods, slug correctly accounts for incomplete initial mass function (IMF) sampling, and returns full posterior probability distributions rather than simply probability maxima. We apply our technique to 621 visually confirmed clusters in two nearby galaxies, NGC 628 and NGC 7793, that are part of the Legacy Extragalactic UV Survey (LEGUS). LEGUS provides Hubble Space Telescope photometry in the NUV, U, B, V, and I bands. We analyze the sensitivity of the derived cluster properties to choices of prior probability distribution, evolutionary tracks, IMF, metallicity, treatment of nebular emission, and extinction curve. We find that slug's results for individual clusters are insensitive to most of these choices, but that the posterior probability distributions we derive are often quite broad, and sometimes multi-peaked and quite sensitive to the choice of priors. In contrast, the properties of the cluster population as a whole are relatively robust against all of these choices. We also compare our results from slug to those derived with a conventional non-stochastic fitting code, Yggdrasil. We show that slug's stochastic models are generally a better fit to the observations than the deterministic ones used by Yggdrasil. However, the overall properties of the cluster populations recovered by both codes are qualitatively similar.

A Bayesian test for Hardy–Weinberg equilibrium of biallelic X-chromosomal markers

PubMed Central

Puig, X; Ginebra, J; Graffelman, J

2017-01-01

The X chromosome is a relatively large chromosome, harboring a lot of genetic information. Much of the statistical analysis of X-chromosomal information is complicated by the fact that males only have one copy. Recently, frequentist statistical tests for Hardy–Weinberg equilibrium have been proposed specifically for dealing with markers on the X chromosome. Bayesian test procedures for Hardy–Weinberg equilibrium for the autosomes have been described, but Bayesian work on the X chromosome in this context is lacking. This paper gives the first Bayesian approach for testing Hardy–Weinberg equilibrium with biallelic markers at the X chromosome. Marginal and joint posterior distributions for the inbreeding coefficient in females and the male to female allele frequency ratio are computed, and used for statistical inference. The paper gives a detailed account of the proposed Bayesian test, and illustrates it with data from the 1000 Genomes project. In that implementation, a novel approach to tackle multiple testing from a Bayesian perspective through posterior predictive checks is used. PMID:28900292

The effect of stocking with 0+ year age-class Atlantic salmon Salmo salar fry: a case study from the River Bush, Northern Ireland.

PubMed

Kennedy, R J; Crozier, W W; Allen, M

2012-10-01

An enhancement programme based on stocking 0+ year age-class Atlantic salmon Salmo salar, conducted in the River Bush, Northern Ireland, U.K. over the period 1996-2005, was reviewed with reference to the performance and biological characteristics of wild fish. Wild ova to 0+ year fry (summer) survival was c. 8% with subsequent wild 0+ year fry-to-smolt survival c. 9%. Stocked unfed 0+ year juveniles gave c. 1% survival to smolt whilst fed 0+ year S. salar stocked in late summer exhibited survival at c. 5%. Stocking with unfed and fed fry contributed to increased smolt production and helped attain local management objectives between 2001 and 2005. Significant differences in biological characteristics were observed between wild and stocked-origin fish. Wild-smolt cohorts were dominated by 2+ year age-class fish on the River Bush whilst smolts originating from fed fry mostly comprised younger 1+ year individuals. The mean mass of 1+ year smolts derived from stocked fed fry was significantly lower than that of wild 1+ year smolts, although these differences were not evident between older age classes. Differences in run timing between wild smolts and smolts derived from stocked fry were also apparent with the stocked-origin fish tending to run earlier than wild fish. Although the stocking exercise was useful in terms of maximizing freshwater production, concerns over the quality of stocked-origin recruits and the long term consequences for productivity are highlighted. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Bayesian stable isotope mixing models

EPA Science Inventory

In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixtur...

Compulsive buying disorder clustering based on sex, age, onset and personality traits.

PubMed

Granero, Roser; Fernández-Aranda, Fernando; Baño, Marta; Steward, Trevor; Mestre-Bach, Gemma; Del Pino-Gutiérrez, Amparo; Moragas, Laura; Mallorquí-Bagué, Núria; Aymamí, Neus; Goméz-Peña, Mónica; Tárrega, Salomé; Menchón, José M; Jiménez-Murcia, Susana

2016-07-01

In spite of the revived interest in compulsive buying disorder (CBD), its classification into the contemporary nosologic systems continues to be debated, and scarce studies have addressed heterogeneity in the clinical phenotype through methodologies based on a person-centered approach. To identify empirical clusters of CBD employing personality traits, as well as patients' sex, age and the age of CBD onset as indicators. An agglomerative hierarchical clustering method defining a combination of the Schwarz Bayesian Information Criterion and log-likelihood was used. Three clusters were identified in a sample of n=110 patients attending a specialized CBD unit a) "male compulsive buyers" reported the highest prevalence of comorbid gambling disorder and the lowest levels of reward dependence; b) "female low-dysfunctional" mainly included employed women, with the highest level of education, the oldest age of onset, the lowest scores in harm avoidance and the highest levels of persistence, self-directedness and cooperativeness; and c) "female highly-dysfunctional" with the youngest age of onset, the highest levels of comorbid psychopathology and harm avoidance, and the lowest score in self-directedness. Sociodemographic characteristics and personality traits can be used to determine CBD clusters which represent different clinical subtypes. These subtypes should be considered when developing assessment instruments, preventive programs and treatment interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

Built environment and Property Crime in Seattle, 1998-2000: A Bayesian Analysis.

PubMed

Matthews, Stephen A; Yang, Tse-Chuan; Hayslett-McCall, Karen L; Ruback, R Barry

2010-06-01

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998-2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.

Quantum mechanics: The Bayesian theory generalized to the space of Hermitian matrices

NASA Astrophysics Data System (ADS)

Benavoli, Alessio; Facchini, Alessandro; Zaffalon, Marco

2016-10-01

We consider the problem of gambling on a quantum experiment and enforce rational behavior by a few rules. These rules yield, in the classical case, the Bayesian theory of probability via duality theorems. In our quantum setting, they yield the Bayesian theory generalized to the space of Hermitian matrices. This very theory is quantum mechanics: in fact, we derive all its four postulates from the generalized Bayesian theory. This implies that quantum mechanics is self-consistent. It also leads us to reinterpret the main operations in quantum mechanics as probability rules: Bayes' rule (measurement), marginalization (partial tracing), independence (tensor product). To say it with a slogan, we obtain that quantum mechanics is the Bayesian theory in the complex numbers.

Nonparametric Bayesian Modeling for Automated Database Schema Matching

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferragut, Erik M; Laska, Jason A

2015-01-01

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

A Bayesian perspective on magnitude estimation.

PubMed

Petzschner, Frederike H; Glasauer, Stefan; Stephan, Klaas E

2015-05-01

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

PubMed

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

Factors affecting variation in mortality of marine Atlantic salmon Salmo salar in Scotland.

PubMed

Soares, Silvia; Murray, Alexander G; Crumlish, Mags; Turnbull, James F; Green, Darren M

2013-03-26

Databases of site production have an important role to play in the investigation and understanding of diseases, since they store valuable amounts of disease and management data. Diseases pose an important constraint to economic expansion of aquaculture. They are dependent on the complex interacting factors of pathogen, environment, and host, and the causes of death can be related to nutritional, environmental, and genetic factors of the host or infectious agents. We examined the drivers of mortality from a single site-production database, which represented one-third of Scottish farmed salmon Salmo salar L. production in 2005, to determine whether mortality 'benchmarking' data could be generalised across sites and production cycles. We show that farm mortality records play an important role in studying mortality losses and identifying of management problems in production. We found that mortalities varied across the months of the year and with the time of year of initial stocking. Production cycles that started in the third quarter of the year had the highest mortality overall. Furthermore, we found site-to-site variation in mortality that may have been caused by either random occurrence of epidemics and environmental events or other local effects.

A Bayesian Approach for Image Segmentation with Shape Priors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Hang; Yang, Qing; Parvin, Bahram

2008-06-20

Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentationmore » through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.« less

Bayesian parameter estimation for chiral effective field theory

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah; Furnstahl, Richard; Phillips, Daniel; Klco, Natalie

2016-09-01

The low-energy constants (LECs) of a chiral effective field theory (EFT) interaction in the two-body sector are fit to observable data using a Bayesian parameter estimation framework. By using Bayesian prior probability distributions (pdfs), we quantify relevant physical expectations such as LEC naturalness and include them in the parameter estimation procedure. The final result is a posterior pdf for the LECs, which can be used to propagate uncertainty resulting from the fit to data to the final observable predictions. The posterior pdf also allows an empirical test of operator redundancy and other features of the potential. We compare results of our framework with other fitting procedures, interpreting the underlying assumptions in Bayesian probabilistic language. We also compare results from fitting all partial waves of the interaction simultaneously to cross section data compared to fitting to extracted phase shifts, appropriately accounting for correlations in the data. Supported in part by the NSF and DOE.

Bayesian learning of visual chunks by human observers

PubMed Central

Orbán, Gergő; Fiser, József; Aslin, Richard N.; Lengyel, Máté

2008-01-01

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input. PMID:18268353

Attention in a Bayesian Framework

PubMed Central

Whiteley, Louise; Sahani, Maneesh

2012-01-01

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action of, attention – unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental settings, where cues shape expectations about a small number of upcoming stimuli and thus convey “prior” information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena. PMID:22712010

Bayesian Item Selection in Constrained Adaptive Testing Using Shadow Tests

ERIC Educational Resources Information Center

Veldkamp, Bernard P.

2010-01-01

Application of Bayesian item selection criteria in computerized adaptive testing might result in improvement of bias and MSE of the ability estimates. The question remains how to apply Bayesian item selection criteria in the context of constrained adaptive testing, where large numbers of specifications have to be taken into account in the item…