[Bayesian statistic: an approach fitted to clinic].
Meyer, N; Vinzio, S; Goichot, B
2009-03-01
Bayesian statistic has known a growing success though quite limited. This is surprising since Bayes' theorem on which this paradigm relies is frequently used by the clinicians. There is a direct link between the routine diagnostic test and the Bayesian statistic. This link is the Bayes' theorem which allows one to compute positive and negative predictive values of a test. The principle of this theorem is extended to simple statistical situations as an introduction to Bayesian statistic. The conceptual simplicity of Bayesian statistic should make for a greater acceptance in the biomedical world.
Bayesian Statistics: A Place in Educational Research?
ERIC Educational Resources Information Center
Diamond, James
The use of Bayesian statistics as the basis of classical analysis of data is described. Bayesian analysis is a set of procedures for changing opinions about a given phenomenon based upon rational observation of a set of data. The Bayesian arrives at a set of prior beliefs regarding some states of nature; he observes data in a study and then…
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.
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.
Philosophy and the practice of Bayesian statistics
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
Philosophy and the practice of Bayesian statistics.
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.
Approximate Bayesian computation with functional statistics.
Soubeyrand, Samuel; Carpentier, Florence; Guiton, François; Klein, Etienne K
2013-03-26
Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes.
Bayesian Cosmological inference beyond statistical isotropy
NASA Astrophysics Data System (ADS)
Souradeep, Tarun; Das, Santanu; Wandelt, Benjamin
2016-10-01
With advent of rich data sets, computationally challenge of inference in cosmology has relied on stochastic sampling method. First, I review the widely used MCMC approach used to infer cosmological parameters and present a adaptive improved implementation SCoPE developed by our group. Next, I present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method with a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. The general, principled, approach to a Bayesian inference of the covariance structure in a random field on a sphere presented here has huge potential for application to other many aspects of cosmology and astronomy, as well as, more distant areas of research like geosciences and climate modelling.
Bayesian statistical studies of the Ramachandran distribution.
Pertsemlidis, Alexander; Zelinka, Jan; Fondon, John W; Henderson, R Keith; Otwinowski, Zbyszek
2005-01-01
We describe a method for the generation of knowledge-based potentials and apply it to the observed torsional angles of known protein structures. The potential is derived using Bayesian reasoning, and is useful as a prior for further such reasoning in the presence of additional data. The potential takes the form of a probability density function, which is described by a small number of coefficients with the number of necessary coefficients determined by tests based on statistical significance and entropy. We demonstrate the methods in deriving one such potential corresponding to two dimensions, the Ramachandran plot. In contrast to traditional histogram-based methods, the function is continuous and differentiable. These properties allow us to use the function as a force term in the energy minimization of appropriately described structures. The method can easily be extended to other observable angles and higher dimensions, or to include sequence dependence and should find applications in structure determination and validation.
Bayesian statistics in medical devices: innovation sparked by the FDA.
Campbell, Gregory
2011-09-01
Bayesian statistical methodology has been used for more than 10 years in medical device premarket submissions to the U.S. Food and Drug Administration (FDA). A complete list of the publicly available information associated with these FDA applications is presented. In addition to the increasing number of Bayesian methodological papers in the statistical journals, a number of successful Bayesian clinical trials in the biomedical journals have been recently reported. Some challenges that require more methodological development are discussed. The promise of using Bayesian methods for incorporation of prior information as well as for conducting adaptive trials is great.
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…
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…
Kruschke, John K; Liddell, Torrin M
2017-02-07
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.
Travel time inversion by Bayesian Inferential Statistics
NASA Astrophysics Data System (ADS)
Mauerberger, Stefan; Holschneider, Matthias
2015-04-01
We are presenting a fully Bayesian approach in inferential statistics determining the posterior probability distribution for non- directly observable quantities (e. g. Earth model parameters). In contrast to deterministic methods established in Geophysics we are following a probabilistic approach focusing on exploring variabilities and uncertainties of model parameters. The aim of that work will be to quantify how well a parameter is determined by a priory knowledge (e. g. existing earth models, rock properties) completed by data obtained from multiple sources (e. g. seismograms, well logs, outcrops). Therefore we are considering the system in question as a Gaussian random process. As a consequence, the randomness in that system is completely determined by its mean and covariance function. Our prior knowledge of that conceptional random process is represented by its a priori mean. The associated covariance function - which quantifies the statistical correlation at two different points - forms the basis of rules for interpolating values at points for which there are no observations. Measurements are assumed to be linear (or linearized) observational functionals of the system. The desired quantities we want to predict are thought to be linear functionals, too. Due to linearity, observables and predictions are Gaussian distributed as well depending on the prior mean and covariance function. The presented approach calculates the prediction's conditional probability distribution posterior to a set of measurements. That conditional probability combines observations and prior knowledge yielding the posterior distribution, an updated distribution for the predictions. As proof of concept, the estimation of propagation velocities in a simple travel-time model is presented. The two observational functionals considered are travel-times and point measures of the velocity model. The system in question is the underlying velocity model itself, assumed as Gaussian random process
An introduction to Bayesian statistics in health psychology.
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.
Daniel Goodman’s empirical approach to Bayesian statistics
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.
Bayesian statistics: estimating plant demographic parameters
James S. Clark; Michael Lavine
2001-01-01
There are times when external information should be brought tobear on an ecological analysis. experiments are never conducted in a knowledge-free context. The inference we draw from an observation may depend on everything else we know about the process. Bayesian analysis is a method that brings outside evidence into the analysis of experimental and observational data...
Bayesian Analysis of Order-Statistics Models for Ranking Data.
ERIC Educational Resources Information Center
Yu, Philip L. H.
2000-01-01
Studied the order-statistics models, extending the usual normal order-statistics model into one in which the underlying random variables followed a multivariate normal distribution. Used a Bayesian approach and the Gibbs sampling technique. Applied the proposed method to analyze presidential election data from the American Psychological…
Bayesian Statistical Inference for Coefficient Alpha. ACT Research Report Series.
ERIC Educational Resources Information Center
Li, Jun Corser; Woodruff, David J.
Coefficient alpha is a simple and very useful index of test reliability that is widely used in educational and psychological measurement. Classical statistical inference for coefficient alpha is well developed. This paper presents two methods for Bayesian statistical inference for a single sample alpha coefficient. An approximate analytic method…
The Bayesian Statistics behind Calibration Concordance
NASA Astrophysics Data System (ADS)
Chen, Yang; Vinay Kashyap, David van Dyk, Xufei Wang, Xiao-Li Meng, Herman Marshall
2017-06-01
Calibration data for instruments used for astrophysical measurements are usually obtained by observing different astronomical objects with well-understood characteristics simultaneously with different detectors. How to adjust the effective areas of the detectors to achieve concordance among the sources observed by the several detectors is the problem of interest. The calibration concordance problem can be addressed by introducing a log-Normal approach for a multiplicative mean model given by physics. In this context, I will introduce concepts of Bayesian hierarchical model, log-Normal regression model and shrinkage estimators, and give intuitive interpretations of the model and the results. Model fitting is achieved by running the Markov chain Monte Carlo (MCMC) algorithm, the basics of which will also be covered.
Bayesian statistics in environmental engineering planning
Englehardt, J.D.; Simon, T.W.
1999-07-01
Today's engineer must be able to quantify both uncertainty due to information limitations, and the variability of natural processes, in order to determine risk. Nowhere is this emphasis on risk assessment more evident than in environmental engineering. The use of Bayesian inference for the rigorous assessment of risk based on available information is reviewed in this paper. Several example environmental engineering planning applications are presented: (1) assessment of losses involving the evaluation of proposed revisions to the South Florida Building Code after Hurricane Andrew; (2) development of a model to predict oil spill consequences due to proposed changes in the oil transportation network in the Gulf of Mexico; (3) studies of ambient concentrations of perchloroethylene surrounding dry cleaners and of tire particulates in residential areas near roadways in Miami, FL; (4) risk assessment from contaminated soils at a cleanup of an old transformer dump site.
Nonparametric Bayesian predictive distributions for future order statistics
Richard A. Johnson; James W. Evans; David W. Green
1999-01-01
We derive the predictive distribution for a specified order statistic, determined from a future random sample, under a Dirichlet process prior. Two variants of the approach are treated and some limiting cases studied. A practical application to monitoring the strength of lumber is discussed including choices of prior expectation and comparisons made to a Bayesian...
A BAYESIAN STATISTICAL APPROACH FOR THE EVALUATION OF CMAQ
Bayesian statistical methods are used to evaluate Community Multiscale Air Quality (CMAQ) model simulations of sulfate aerosol over a section of the eastern US for 4-week periods in summer and winter 2001. The observed data come from two U.S. Environmental Protection Agency data ...
A BAYESIAN STATISTICAL APPROACH FOR THE EVALUATION OF CMAQ
Bayesian statistical methods are used to evaluate Community Multiscale Air Quality (CMAQ) model simulations of sulfate aerosol over a section of the eastern US for 4-week periods in summer and winter 2001. The observed data come from two U.S. Environmental Protection Agency data ...
Bayesian Case Influence Measures for Statistical Models with Missing Data
Zhu, Hongtu; Ibrahim, Joseph G.; Cho, Hyunsoon; Tang, Niansheng
2011-01-01
We examine three Bayesian case influence measures including the φ-divergence, Cook's posterior mode distance and Cook's posterior mean distance for identifying a set of influential observations for a variety of statistical models with missing data including models for longitudinal data and latent variable models in the absence/presence of missing data. Since it can be computationally prohibitive to compute these Bayesian case influence measures in models with missing data, we derive simple first-order approximations to the three Bayesian case influence measures by using the Laplace approximation formula and examine the applications of these approximations to the identification of influential sets. All of the computations for the first-order approximations can be easily done using Markov chain Monte Carlo samples from the posterior distribution based on the full data. Simulated data and an AIDS dataset are analyzed to illustrate the methodology. PMID:23399928
Some Bayesian statistical techniques useful in estimating frequency and density
Johnson, D.H.
1977-01-01
This paper presents some elementary applications of Bayesian statistics to problems faced by wildlife biologists. Bayesian confidence limits for frequency of occurrence are shown to be generally superior to classical confidence limits. Population density can be estimated from frequency data if the species is sparsely distributed relative to the size of the sample plot. For other situations, limits are developed based on the normal distribution and prior knowledge that the density is non-negative, which insures that the lower confidence limit is non-negative. Conditions are described under which Bayesian confidence limits are superior to those calculated with classical methods; examples are also given on how prior knowledge of the density can be used to sharpen inferences drawn from a new sample.
Monden, Rei; de Vos, Stijn; Morey, Richard; Wagenmakers, Eric-Jan; de Jonge, Peter; Roest, Annelieke M
2016-12-01
The Food and Drug Administration (FDA) uses a p < 0.05 null-hypothesis significance testing framework to evaluate "substantial evidence" for drug efficacy. This framework only allows dichotomous conclusions and does not quantify the strength of evidence supporting efficacy. The efficacy of FDA-approved antidepressants for the treatment of anxiety disorders was re-evaluated in a Bayesian framework that quantifies the strength of the evidence. Data from 58 double-blind placebo-controlled trials were retrieved from the FDA for the second-generation antidepressants for the treatment of anxiety disorders. Bayes factors (BFs) were calculated for all treatment arms compared to placebo and were compared with the corresponding p-values and the FDA conclusion categories. BFs ranged from 0.07 to 131,400, indicating a range of no support of evidence to strong evidence for the efficacy. Results also indicate a varying strength of evidence between the trials with p < 0.05. In sum, there were large differences in BFs across trials. Among trials providing "substantial evidence" according to the FDA, only 27 out of 59 dose groups obtained strong support for efficacy according to the typically used cutoff of BF ≥ 20. The Bayesian framework can provide valuable information on the strength of the evidence for drug efficacy. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Human Motion Retrieval Based on Statistical Learning and Bayesian Fusion
Xiao, Qinkun; Song, Ren
2016-01-01
A novel motion retrieval approach based on statistical learning and Bayesian fusion is presented. The approach includes two primary stages. (1) In the learning stage, fuzzy clustering is utilized firstly to get the representative frames of motions, and the gesture features of the motions are extracted to build a motion feature database. Based on the motion feature database and statistical learning, the probability distribution function of different motion classes is obtained. (2) In the motion retrieval stage, the query motion feature is extracted firstly according to stage (1). Similarity measurements are then conducted employing a novel method that combines category-based motion similarity distances with similarity distances based on canonical correlation analysis. The two motion distances are fused using Bayesian estimation, and the retrieval results are ranked according to the fused values. The effectiveness of the proposed method is verified experimentally. PMID:27732673
Use of Bayesian statistical approach in diagnosing secondary hypertension.
Krzych, Lukasz Jerzy
2008-03-01
Bayes's theorem is predominantly used in diagnosing based on the results of various diagnostic tests. This statistical approach is intuitive in differential diagnosis as it explicitly takes into consideration data from medical history, physical examination, laboratory findings and imaging. Bayes's theorem states that the probability of disease occurrence (or occurrence of other outcome) after new information is obtained, called a posteriori probability, depends directly on an a priori probability and the value of likelihood ratio associated with a given test result. This paper describes basic Bayesian analysis in relation to the diagnosis of two types of secondary hypertension; primary aldosteronism and pheochromocytoma. This choice is based on two facts; primary aldosteronism is believed to be the most common and the most commonly detected cause of symptomatic hypertension and pheochromocytoma is thought to have rapid progress and stormy clinical course. This article aims to draw physicians' attention to and increase the knowledge of Bayesian analysis, and to describe its use in everyday clinical decision making. On the basis of this theorem's foundations, the discussion in relation to the issue of differential diagnosis between physicians, their patients, and medical students should also improve. When used in practice, one should be aware, however, of Bayesian analysis limitations concerning the diagnostic test application and limited knowledge of diagnostic test accuracy, and insecure or faulty a priori probability estimates.
Bayesian modeling of flexible cognitive control.
Jiang, Jiefeng; Heller, Katherine; Egner, Tobias
2014-10-01
"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. Copyright © 2014 Elsevier Ltd. All rights reserved.
Spectral Analysis of B Stars: An Application of Bayesian Statistics
NASA Astrophysics Data System (ADS)
Mugnes, J.-M.; Robert, C.
2012-12-01
To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.
Bayesian modeling of flexible cognitive control
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
Bayesian statistics and information fusion for GPS-denied navigation
NASA Astrophysics Data System (ADS)
Copp, Brian Lee
It is well known that satellite navigation systems are vulnerable to disruption due to jamming, spoofing, or obstruction of the signal. The desire for robust navigation of aircraft in GPS-denied environments has motivated the development of feature-aided navigation systems, in which measurements of environmental features are used to complement the dead reckoning solution produced by an inertial navigation system. Examples of environmental features which can be exploited for navigation include star positions, terrain elevation, terrestrial wireless signals, and features extracted from photographic data. Feature-aided navigation represents a particularly challenging estimation problem because the measurements are often strongly nonlinear, and the quality of the navigation solution is limited by the knowledge of nuisance parameters which may be difficult to model accurately. As a result, integration approaches based on the Kalman filter and its variants may fail to give adequate performance. This project develops a framework for the integration of feature-aided navigation techniques using Bayesian statistics. In this approach, the probability density function for aircraft horizontal position (latitude and longitude) is approximated by a two-dimensional point mass function defined on a rectangular grid. Nuisance parameters are estimated using a hypothesis based approach (Multiple Model Adaptive Estimation) which continuously maintains an accurate probability density even in the presence of strong nonlinearities. The effectiveness of the proposed approach is illustrated by the simulated use of terrain referenced navigation and wireless time-of-arrival positioning to estimate a reference aircraft trajectory. Monte Carlo simulations have shown that accurate position estimates can be obtained in terrain referenced navigation even with a strongly nonlinear altitude bias. The integration of terrain referenced and wireless time-of-arrival measurements is described along with
Bayesian Sensitivity Analysis of Statistical Models with Missing Data
ZHU, HONGTU; IBRAHIM, JOSEPH G.; TANG, NIANSHENG
2013-01-01
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures. PMID:24753718
Schmidt, Paul; Schmid, Volker J; Gaser, Christian; Buck, Dorothea; Bührlen, Susanne; Förschler, Annette; Mühlau, Mark
2013-01-01
Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.
Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation
Cobb, Loren; Krishnamurthy, Ashok; Mandel, Jan; Beezley, Jonathan D.
2014-01-01
We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely. PMID:25113590
Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation.
Cobb, Loren; Krishnamurthy, Ashok; Mandel, Jan; Beezley, Jonathan D
2014-07-01
We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely.
Defining statistical perceptions with an empirical Bayesian approach
NASA Astrophysics Data System (ADS)
Tajima, Satohiro
2013-04-01
Extracting statistical structures (including textures or contrasts) from a natural stimulus is a central challenge in both biological and engineering contexts. This study interprets the process of statistical recognition in terms of hyperparameter estimations and free-energy minimization procedures with an empirical Bayesian approach. This mathematical interpretation resulted in a framework for relating physiological insights in animal sensory systems to the functional properties of recognizing stimulus statistics. We applied the present theoretical framework to two typical models of natural images that are encoded by a population of simulated retinal neurons, and demonstrated that the resulting cognitive performances could be quantified with the Fisher information measure. The current enterprise yielded predictions about the properties of human texture perception, suggesting that the perceptual resolution of image statistics depends on visual field angles, internal noise, and neuronal information processing pathways, such as the magnocellular, parvocellular, and koniocellular systems. Furthermore, the two conceptually similar natural-image models were found to yield qualitatively different predictions, striking a note of warning against confusing the two models when describing a natural image.
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…
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…
Bayesian inference on the sphere beyond statistical isotropy
Das, Santanu; Souradeep, Tarun; Wandelt, Benjamin D. E-mail: wandelt@iap.fr
2015-10-01
We present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method as a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. SI violation in observed CMB maps arise due to known physical effects such as Doppler boost and weak lensing; yet unknown theoretical possibilities like cosmic topology and subtle violations of the cosmological principle, as well as, expected observational artefacts of scanning the sky with a non-circular beam, masking, foreground residuals, anisotropic noise, etc. We explicitly demonstrate the recovery of the input SI violation signals with their full statistics in simulated CMB maps. Our formalism easily adapts to exploring parametric physical models with non-SI covariance, as we illustrate for the inference of the parameters of a Doppler boosted sky map. Our approach promises to provide a robust quantitative evaluation of the evidence for SI violation related anomalies in the CMB sky by estimating the BipoSH spectra along with their complete posterior.
Brown, D Andrew; Lazar, Nicole A; Datta, Gauri S; Jang, Woncheol; McDowell, Jennifer E
2014-01-01
The analysis of functional neuroimaging data often involves the simultaneous testing for activation at thousands of voxels, leading to a massive multiple testing problem. This is true whether the data analyzed are time courses observed at each voxel or a collection of summary statistics such as statistical parametric maps (SPMs). It is known that classical multiplicity corrections become strongly conservative in the presence of a massive number of tests. Some more popular approaches for thresholding imaging data, such as the Benjamini-Hochberg step-up procedure for false discovery rate control, tend to lose precision or power when the assumption of independence of the data does not hold. Bayesian approaches to large scale simultaneous inference also often rely on the assumption of independence. We introduce a spatial dependence structure into a Bayesian testing model for the analysis of SPMs. By using SPMs rather than the voxel time courses, much of the computational burden of Bayesian analysis is mitigated. Increased power is demonstrated by using the dependence model to draw inference on a real dataset collected in a fMRI study of cognitive control. The model also is shown to lead to improved identification of neural activation patterns known to be associated with eye movement tasks. © 2013.
Bayesian Statistical Inference in Psychology: Comment on Trafimow (2003)
ERIC Educational Resources Information Center
Lee, Michael D.; Wagenmakers, Eric-Jan
2005-01-01
D. Trafimow presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view…
Bayesian Statistical Inference in Psychology: Comment on Trafimow (2003)
ERIC Educational Resources Information Center
Lee, Michael D.; Wagenmakers, Eric-Jan
2005-01-01
D. Trafimow presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view…
Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements
NASA Astrophysics Data System (ADS)
Norberg, Johannes; Virtanen, Ilkka I.; Roininen, Lassi; Vierinen, Juha; Orispää, Mikko; Kauristie, Kirsti; Lehtinen, Markku S.
2016-04-01
We validate two-dimensional ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. Our tomography method is based on Bayesian statistical inversion with prior distribution given by its mean and covariance. We employ ionosonde measurements for the choice of the prior mean and covariance parameters and use the Gaussian Markov random fields as a sparse matrix approximation for the numerical computations. This results in a computationally efficient tomographic inversion algorithm with clear probabilistic interpretation. We demonstrate how this method works with simultaneous beacon satellite and ionosonde measurements obtained in northern Scandinavia. The performance is compared with results obtained with a zero-mean prior and with the prior mean taken from the International Reference Ionosphere 2007 model. In validating the results, we use EISCAT ultra-high-frequency incoherent scatter radar measurements as the ground truth for the ionization profile shape. We find that in comparison to the alternative prior information sources, ionosonde measurements improve the reconstruction by adding accurate information about the absolute value and the altitude distribution of electron density. With an ionosonde at continuous disposal, the presented method enhances stand-alone near-real-time ionospheric tomography for the given conditions significantly.
Bayesian Statistical Approach To Binary Asteroid Orbit Determination
NASA Astrophysics Data System (ADS)
Dmitrievna Kovalenko, Irina; Stoica, Radu S.
2015-08-01
Orbit determination from observations is one of the classical problems in celestial mechanics. Deriving the trajectory of binary asteroid with high precision is much more complicate than the trajectory of simple asteroid. Here we present a method of orbit determination based on the algorithm of Monte Carlo Markov Chain (MCMC). This method can be used for the preliminary orbit determination with relatively small number of observations, or for adjustment of orbit previously determined.The problem consists on determination of a conditional a posteriori probability density with given observations. Applying the Bayesian statistics, the a posteriori probability density of the binary asteroid orbital parameters is proportional to the a priori and likelihood probability densities. The likelihood function is related to the noise probability density and can be calculated from O-C deviations (Observed minus Calculated positions). The optionally used a priori probability density takes into account information about the population of discovered asteroids. The a priori probability density is used to constrain the phase space of possible orbits.As a MCMC method the Metropolis-Hastings algorithm has been applied, adding a globally convergent coefficient. The sequence of possible orbits derives through the sampling of each orbital parameter and acceptance criteria.The method allows to determine the phase space of every possible orbit considering each parameter. It also can be used to derive one orbit with the biggest probability density of orbital elements.
NASA Technical Reports Server (NTRS)
He, Yuning
2015-01-01
The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.
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.
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.
Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification
2010-01-13
Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification Paolo Zuliani, André Platzer , Edmund M. Clarke January 13...Legay, A. Platzer , and P. Zuliani. A Bayesian approach to Model Checking biological systems. In CMSB, volume 5688 of LNCS, pages 218–234, 2009. 21 [16
Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.
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).
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.
Statistical Surrogate Modeling of Atmospheric Dispersion Events Using Bayesian Adaptive Splines
NASA Astrophysics Data System (ADS)
Francom, D.; Sansó, B.; Bulaevskaya, V.; Lucas, D. D.
2016-12-01
Uncertainty in the inputs of complex computer models, including atmospheric dispersion and transport codes, is often assessed via statistical surrogate models. Surrogate models are computationally efficient statistical approximations of expensive computer models that enable uncertainty analysis. We introduce Bayesian adaptive spline methods for producing surrogate models that capture the major spatiotemporal patterns of the parent model, while satisfying all the necessities of flexibility, accuracy and computational feasibility. We present novel methodological and computational approaches motivated by a controlled atmospheric tracer release experiment conducted at the Diablo Canyon nuclear power plant in California. Traditional methods for building statistical surrogate models often do not scale well to experiments with large amounts of data. Our approach is well suited to experiments involving large numbers of model inputs, large numbers of simulations, and functional output for each simulation. Our approach allows us to perform global sensitivity analysis with ease. We also present an approach to calibration of simulators using field data.
VizieR Online Data Catalog: Bayesian statistics for massive stars (Mugnes+, 2015)
NASA Astrophysics Data System (ADS)
Mugnes, J.-M.; Robert, C.
2016-06-01
We use our spectral analysis method based on Bayesian statistics that simultaneously constrains four stellar parameters (effective temperature, surface gravity, projected rotational velocity, and microturbulence velocity) on a sample of B stars. (2 data files).
Impaired Bayesian learning for cognitive control in cocaine dependence.
Ide, Jaime S; Hu, Sien; Zhang, Sheng; Yu, Angela J; Li, Chiang-shan R
2015-06-01
Cocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework. A "sequential effect" is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject's belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p < 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups. By comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p < 0.003), as well as showing less behavioral adjustment in response to P(stop) (p < 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC. Cocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Ice Shelf Modeling: A Cross-Polar Bayesian Statistical Approach
NASA Astrophysics Data System (ADS)
Kirchner, N.; Furrer, R.; Jakobsson, M.; Zwally, H. J.
2010-12-01
Ice streams interlink glacial terrestrial and marine environments: embedded in a grounded inland ice such as the Antarctic Ice Sheet or the paleo ice sheets covering extensive parts of the Eurasian and Amerasian Arctic respectively, ice streams are major drainage agents facilitating the discharge of substantial portions of continental ice into the ocean. At their seaward side, ice streams can either extend onto the ocean as floating ice tongues (such as the Drygalsky Ice Tongue/East Antarctica), or feed large ice shelves (as is the case for e.g. the Siple Coast and the Ross Ice Shelf/West Antarctica). The flow behavior of ice streams has been recognized to be intimately linked with configurational changes in their attached ice shelves; in particular, ice shelf disintegration is associated with rapid ice stream retreat and increased mass discharge from the continental ice mass, contributing eventually to sea level rise. Investigations of ice stream retreat mechanism are however incomplete if based on terrestrial records only: rather, the dynamics of ice shelves (and, eventually, the impact of the ocean on the latter) must be accounted for. However, since floating ice shelves leave hardly any traces behind when melting, uncertainty regarding the spatio-temporal distribution and evolution of ice shelves in times prior to instrumented and recorded observation is high, calling thus for a statistical modeling approach. Complementing ongoing large-scale numerical modeling efforts (Pollard & DeConto, 2009), we model the configuration of ice shelves by using a Bayesian Hiearchial Modeling (BHM) approach. We adopt a cross-polar perspective accounting for the fact that currently, ice shelves exist mainly along the coastline of Antarctica (and are virtually non-existing in the Arctic), while Arctic Ocean ice shelves repeatedly impacted the Arctic ocean basin during former glacial periods. Modeled Arctic ocean ice shelf configurations are compared with geological spatial
Postscript: Bayesian Statistical Inference in Psychology: Comment on Trafimow (2003)
ERIC Educational Resources Information Center
Lee, Michael D.; Wagenmakers, Eric-Jan
2005-01-01
This paper comments on the response offered by Trafimow on Lee and Wagenmakers comments on Trafimow's original article. It seems our comment should have made it clear that the objective Bayesian approach we advocate views probabilities neither as relative frequencies nor as belief states, but as degrees of plausibility assigned to propositions in…
Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist.
Depaoli, Sarah; van de Schoot, Rens
2017-06-01
Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these 3 points of potential danger, we have developed a succinct checklist: the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis. We provide an account of "when to worry" for each of these issues related to: (a) issues to check before estimating the model, (b) issues to check after estimating the model but before interpreting results, (c) understanding the influence of priors, and (d) actions to take after interpreting results. To accompany these key points of concern, we will present diagnostic tools that can be used in conjunction with the development and assessment of a Bayesian model. We also include examples of how to interpret results when "problems" in estimation arise, as well as syntax and instructions for implementation. Our aim is to stress the importance of openness and transparency of all aspects of Bayesian estimation, and it is our hope that the WAMBS questionnaire can aid in this process. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
An improved Bayesian matting method based on image statistic characteristics
NASA Astrophysics Data System (ADS)
Sun, Wei; Luo, Siwei; Wu, Lina
2015-03-01
Image matting is an important task in image and video editing and has been studied for more than 30 years. In this paper we propose an improved interactive matting method. Starting from a coarse user-guided trimap, we first perform a color estimation based on texture and color information and use the result to refine the original trimap. Then with the new trimap, we apply soft matting process which is improved Bayesian matting with smoothness constraints. Experimental results on natural image show that this method is useful, especially for the images have similar texture feature in the background or the images which is hard to give a precise trimap.
TOWARDS A BAYESIAN PERSPECTIVE ON STATISTICAL DISCLOSURE LIMITATION
National statistical offices and other organizations collect data on individual subjects (person, businesses, organizations), typically while assuring the subject that data pertaining to them will be held confidential. These data provide the raw material for statistical data pro...
TOWARDS A BAYESIAN PERSPECTIVE ON STATISTICAL DISCLOSURE LIMITATION
National statistical offices and other organizations collect data on individual subjects (person, businesses, organizations), typically while assuring the subject that data pertaining to them will be held confidential. These data provide the raw material for statistical data pro...
Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.
Cecconi, Lorenzo; Grisotto, Laura; Catelan, Dolores; Lagazio, Corrado; Berrocal, Veronica; Biggeri, Annibale
2016-08-01
Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty.
Bayesian statistics for the calibration of the LISA Pathfinder experiment
NASA Astrophysics Data System (ADS)
Armano, M.; Audley, H.; Auger, G.; Binetruy, P.; Born, M.; Bortoluzzi, D.; Brandt, N.; Bursi, A.; Caleno, M.; Cavalleri, A.; Cesarini, A.; Cruise, M.; Danzmann, K.; Diepholz, I.; Dolesi, R.; Dunbar, N.; Ferraioli, L.; Ferroni, V.; Fitzsimons, E.; Freschi, M.; García Marirrodriga, C.; Gerndt, R.; Gesa, L.; Gibert, F.; Giardini, D.; Giusteri, R.; Grimani, C.; Harrison, I.; Heinzel, G.; Hewitson, M.; Hollington, D.; Hueller, M.; Huesler, J.; Inchauspé, H.; Jennrich, O.; Jetzer, P.; Johlander, B.; Karnesis, N.; Kaune, B.; Korsakova, N.; Killow, C.; Lloro, I.; Maarschalkerweerd, R.; Madden, S.; Mance, D.; Martin, V.; Martin-Porqueras, F.; Mateos, I.; McNamara, P.; Mendes, J.; Mitchell, E.; Moroni, A.; Nofrarias, M.; Paczkowski, S.; Perreur-Lloyd, M.; Pivato, P.; Plagnol, E.; Prat, P.; Ragnit, U.; Ramos-Castro, J.; Reiche, J.; Romera Perez, J. A.; Robertson, D.; Rozemeijer, H.; Russano, G.; Sarra, P.; Schleicher, A.; Slutsky, J.; Sopuerta, C. F.; Sumner, T.; Texier, D.; Thorpe, J.; Trenkel, C.; Tu, H. B.; Vitale, S.; Wanner, G.; Ward, H.; Waschke, S.; Wass, P.; Wealthy, D.; Wen, S.; Weber, W.; Wittchen, A.; Zanoni, C.; Ziegler, T.; Zweifel, P.
2015-05-01
The main goal of LISA Pathfinder (LPF) mission is to estimate the acceleration noise models of the overall LISA Technology Package (LTP) experiment on-board. This will be of crucial importance for the future space-based Gravitational-Wave (GW) detectors, like eLISA. Here, we present the Bayesian analysis framework to process the planned system identification experiments designed for that purpose. In particular, we focus on the analysis strategies to predict the accuracy of the parameters that describe the system in all degrees of freedom. The data sets were generated during the latest operational simulations organised by the data analysis team and this work is part of the LTPDA Matlab toolbox.
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
NASA Astrophysics Data System (ADS)
Prix, Reinhard; Krishnan, Badri
2009-10-01
We investigate the Bayesian framework for detection of continuous gravitational waves (GWs) in the context of targeted searches, where the phase evolution of the GW signal is assumed to be known, while the four amplitude parameters are unknown. We show that the orthodox maximum-likelihood statistic (known as F-statistic) can be rediscovered as a Bayes factor with an unphysical prior in amplitude parameter space. We introduce an alternative detection statistic ('B-statistic') using the Bayes factor with a more natural amplitude prior, namely an isotropic probability distribution for the orientation of GW sources. Monte Carlo simulations of targeted searches show that the resulting Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e., has a higher expected detection probability at equal false-alarm probability) than the frequentist F-statistic.
Yu, Jihnhee; Hutson, Alan D; Siddiqui, Adnan H; Kedron, Mary A
2016-02-01
In some small clinical trials, toxicity is not a primary endpoint; however, it often has dire effects on patients' quality of life and is even life-threatening. For such clinical trials, rigorous control of the overall incidence of adverse events is desirable, while simultaneously collecting safety information. In this article, we propose group sequential toxicity monitoring strategies to control overall toxicity incidents below a certain level as opposed to performing hypothesis testing, which can be incorporated into an existing study design based on the primary endpoint. We consider two sequential methods: a non-Bayesian approach in which stopping rules are obtained based on the 'future' probability of an excessive toxicity rate; and a Bayesian adaptation modifying the proposed non-Bayesian approach, which can use the information obtained at interim analyses. Through an extensive Monte Carlo study, we show that the Bayesian approach often provides better control of the overall toxicity rate than the non-Bayesian approach. We also investigate adequate toxicity estimation after the studies. We demonstrate the applicability of our proposed methods in controlling the symptomatic intracranial hemorrhage rate for treating acute ischemic stroke patients. © The Author(s) 2012.
Statistical detection of EEG synchrony using empirical bayesian inference.
Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven
2015-01-01
There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.
A BAYESIAN STATISTICAL APPROACHES FOR THE EVALUATION OF CMAQ
This research focuses on the application of spatial statistical techniques for the evaluation of the Community Multiscale Air Quality (CMAQ) model. The upcoming release version of the CMAQ model was run for the calendar year 2001 and is in the process of being evaluated by EPA an...
Johnson, Eric D; Tubau, Elisabet
2016-09-27
Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reasoning (e.g., the structural mapping between the presented and requested relations) can help explain the remaining difficulties. With a non-Bayesian inference that required identical arithmetic but afforded a more direct structural mapping, performance was universally high. Furthermore, reducing the relational demands of the task through questions that directed reasoners to use the presented statistics, as compared with questions that prompted the representation of a second, similar sample, also significantly improved reasoning. Distinct error patterns were also observed between these presented- and similar-sample scenarios, which suggested differences in relational-reasoning strategies. On the other hand, while higher numeracy was associated with better Bayesian reasoning, higher-numerate reasoners were not immune to the relational complexity of the task. Together, these findings validate the relational-reasoning view of Bayesian problem solving and highlight the importance of considering not only the presented task structure, but also the complexity of the structural alignment between the presented and requested relations.
Hickey, Graeme L; Kefford, Ben J; Dunlop, Jason E; Craig, Peter S
2008-11-01
Species sensitivity distributions (SSDs) may accurately predict the proportion of species in a community that are at hazard from environmental contaminants only if they contain sensitivity data from a large sample of species representative of the mix of species present in the locality or habitat of interest. With current widely accepted ecotoxicological methods, however, this rarely occurs. Two recent suggestions address this problem. First, use rapid toxicity tests, which are less rigorous than conventional tests, to approximate experimentally the sensitivity of many species quickly and in approximate proportion to naturally occurring communities. Second, use expert judgements regarding the sensitivity of higher taxonomic groups (e.g., orders) and Bayesian statistical methods to construct SSDs that reflect the richness (or perceived importance) of these groups. Here, we describe and analyze several models from a Bayesian perspective to construct SSDs from data derived using rapid toxicity testing, combining both rapid test data and expert opinion. We compare these new models with two frequentist approaches, Kaplan-Meier and a log-normal distribution, using a large data set on the salinity sensitivity of freshwater macroinvertebrates from Victoria (Australia). The frequentist log-normal analysis produced a SSD that overestimated the hazard to species relative to the Kaplan-Meier and Bayesian analyses. Of the Bayesian analyses investigated, the introduction of a weighting factor to account for the richness (or importance) of taxonomic groups influenced the calculated hazard to species. Furthermore, Bayesian methods allowed us to determine credible intervals representing SSD uncertainty. We recommend that rapid tests, expert judgements, and novel Bayesian statistical methods be used so that SSDs reflect communities of organisms found in nature.
Bayesian Bigot? Statistical Discrimination, Stereotypes, and Employer Decision Making
Pager, Devah; Karafin, Diana
2010-01-01
Much of the debate over the underlying causes of discrimination centers on the rationality of employer decision making. Economic models of statistical discrimination emphasize the cognitive utility of group estimates as a means of dealing with the problems of uncertainty. Sociological and social-psychological models, by contrast, question the accuracy of group-level attributions. Although mean differences may exist between groups on productivity-related characteristics, these differences are often inflated in their application, leading to much larger differences in individual evaluations than would be warranted by actual group-level trait distributions. In this study, the authors examine the nature of employer attitudes about black and white workers and the extent to which these views are calibrated against their direct experiences with workers from each group. They use data from fifty-five in-depth interviews with hiring managers to explore employers’ group-level attributions and their direct observations to develop a model of attitude formation and employer learning. PMID:20686633
Bayesian Bigot? Statistical Discrimination, Stereotypes, and Employer Decision Making.
Pager, Devah; Karafin, Diana
2009-01-01
Much of the debate over the underlying causes of discrimination centers on the rationality of employer decision making. Economic models of statistical discrimination emphasize the cognitive utility of group estimates as a means of dealing with the problems of uncertainty. Sociological and social-psychological models, by contrast, question the accuracy of group-level attributions. Although mean differences may exist between groups on productivity-related characteristics, these differences are often inflated in their application, leading to much larger differences in individual evaluations than would be warranted by actual group-level trait distributions. In this study, the authors examine the nature of employer attitudes about black and white workers and the extent to which these views are calibrated against their direct experiences with workers from each group. They use data from fifty-five in-depth interviews with hiring managers to explore employers' group-level attributions and their direct observations to develop a model of attitude formation and employer learning.
Bayesian approach for counting experiment statistics applied to a neutrino point source analysis
NASA Astrophysics Data System (ADS)
Bose, D.; Brayeur, L.; Casier, M.; de Vries, K. D.; Golup, G.; van Eijndhoven, N.
2013-12-01
In this paper we present a model independent analysis method following Bayesian statistics to analyse data from a generic counting experiment and apply it to the search for neutrinos from point sources. We discuss a test statistic defined following a Bayesian framework that will be used in the search for a signal. In case no signal is found, we derive an upper limit without the introduction of approximations. The Bayesian approach allows us to obtain the full probability density function for both the background and the signal rate. As such, we have direct access to any signal upper limit. The upper limit derivation directly compares with a frequentist approach and is robust in the case of low-counting observations. Furthermore, it allows also to account for previous upper limits obtained by other analyses via the concept of prior information without the need of the ad hoc application of trial factors. To investigate the validity of the presented Bayesian approach, we have applied this method to the public IceCube 40-string configuration data for 10 nearby blazars and we have obtained a flux upper limit, which is in agreement with the upper limits determined via a frequentist approach. Furthermore, the upper limit obtained compares well with the previously published result of IceCube, using the same data set.
Bayesian Statistical Analysis Applied to NAA Data for Neutron Flux Spectrum Determination
NASA Astrophysics Data System (ADS)
Chiesa, D.; Previtali, E.; Sisti, M.
2014-04-01
In this paper, we present a statistical method, based on Bayesian statistics, to evaluate the neutron flux spectrum from the activation data of different isotopes. The experimental data were acquired during a neutron activation analysis (NAA) experiment [A. Borio di Tigliole et al., Absolute flux measurement by NAA at the Pavia University TRIGA Mark II reactor facilities, ENC 2012 - Transactions Research Reactors, ISBN 978-92-95064-14-0, 22 (2012)] performed at the TRIGA Mark II reactor of Pavia University (Italy). In order to evaluate the neutron flux spectrum, subdivided in energy groups, we must solve a system of linear equations containing the grouped cross sections and the activation rate data. We solve this problem with Bayesian statistical analysis, including the uncertainties of the coefficients and the a priori information about the neutron flux. A program for the analysis of Bayesian hierarchical models, based on Markov Chain Monte Carlo (MCMC) simulations, is used to define the problem statistical model and solve it. The energy group fluxes and their uncertainties are then determined with great accuracy and the correlations between the groups are analyzed. Finally, the dependence of the results on the prior distribution choice and on the group cross section data is investigated to confirm the reliability of the analysis.
Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.
Chen, Wenan; Larrabee, Beth R; Ovsyannikova, Inna G; Kennedy, Richard B; Haralambieva, Iana H; Poland, Gregory A; Schaid, Daniel J
2015-07-01
Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.
Bayesian change point estimation in Poisson-based control charts
NASA Astrophysics Data System (ADS)
Assareh, Hassan; Noorossana, Rassoul; L Mengersen, Kerrie
2013-11-01
Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change, a linear trend and a known multiple number of changes in the Poisson rate. The Markov chain Monte Carlo is used to obtain posterior distributions of the change point parameters and corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the well-known c-, Poisson exponentially weighted moving average (EWMA) and Poisson cumulative sum (CUSUM) control charts for different change type scenarios. We also apply the Deviance Information Criterion as a model selection criterion in the Bayesian context, to find the best change point model for a given dataset where there is no prior knowledge about the change type in the process. In comparison with built-in estimators of EWMA and CUSUM charts and ML based estimators, the Bayesian estimator performs reasonably well and remains a strong alternative. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising.
Pizurica, Aleksandra; Philips, Wilfried; Lemahieu, Ignace; Acheroy, Marc
2002-01-01
This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.
A new model test in high energy physics in frequentist and Bayesian statistical formalisms
NASA Astrophysics Data System (ADS)
Kamenshchikov, A.
2017-01-01
A problem of a new physical model test given observed experimental data is a typical one for modern experiments of high energy physics (HEP). A solution of the problem may be provided with two alternative statistical formalisms, namely frequentist and Bayesian, which are widely spread in contemporary HEP searches. A characteristic experimental situation is modeled from general considerations and both the approaches are utilized in order to test a new model. The results are juxtaposed, what demonstrates their consistency in this work. An effect of a systematic uncertainty treatment in the statistical analysis is also considered.
NASA Astrophysics Data System (ADS)
Albert, Carlo; Ulzega, Simone; Stoop, Ruedi
2016-04-01
Measured time-series of both precipitation and runoff are known to exhibit highly non-trivial statistical properties. For making reliable probabilistic predictions in hydrology, it is therefore desirable to have stochastic models with output distributions that share these properties. When parameters of such models have to be inferred from data, we also need to quantify the associated parametric uncertainty. For non-trivial stochastic models, however, this latter step is typically very demanding, both conceptually and numerically, and always never done in hydrology. Here, we demonstrate that methods developed in statistical physics make a large class of stochastic differential equation (SDE) models amenable to a full-fledged Bayesian parameter inference. For concreteness we demonstrate these methods by means of a simple yet non-trivial toy SDE model. We consider a natural catchment that can be described by a linear reservoir, at the scale of observation. All the neglected processes are assumed to happen at much shorter time-scales and are therefore modeled with a Gaussian white noise term, the standard deviation of which is assumed to scale linearly with the system state (water volume in the catchment). Even for constant input, the outputs of this simple non-linear SDE model show a wealth of desirable statistical properties, such as fat-tailed distributions and long-range correlations. Standard algorithms for Bayesian inference fail, for models of this kind, because their likelihood functions are extremely high-dimensional intractable integrals over all possible model realizations. The use of Kalman filters is illegitimate due to the non-linearity of the model. Particle filters could be used but become increasingly inefficient with growing number of data points. Hamiltonian Monte Carlo algorithms allow us to translate this inference problem to the problem of simulating the dynamics of a statistical mechanics system and give us access to most sophisticated methods
NASA Astrophysics Data System (ADS)
Rubin, D.; Aldering, G.; Barbary, K.; Boone, K.; Chappell, G.; Currie, M.; Deustua, S.; Fagrelius, P.; Fruchter, A.; Hayden, B.; Lidman, C.; Nordin, J.; Perlmutter, S.; Saunders, C.; Sofiatti, C.; Supernova Cosmology Project, The
2015-11-01
While recent supernova (SN) cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current SN cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, unexplained dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real SN observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was primarily performed blinded, in that the basic framework was first validated on simulated data before transitioning to real data. We also discuss possible extensions of the method.
Novick, Steven; Shen, Yan; Yang, Harry; Peterson, John; LeBlond, Dave; Altan, Stan
2015-01-01
Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F2 parameter, an extension of the commonly used f2 statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f2 and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f2 statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.
Biau, David J; Boulezaz, Samuel; Casabianca, Laurent; Hamadouche, Moussa; Anract, Philippe; Chevret, Sylvie
2017-08-22
The common frequentist approach is limited in providing investigators with appropriate measures for conducting a new trial. To answer such important questions and one has to look at Bayesian statistics. As a worked example, we conducted a Bayesian cumulative meta-analysis to summarize the benefit of patient-specific instrumentation on the alignment of total knee replacement from previously published evidence. Data were sourced from Medline, Embase, and Cochrane databases. All randomised controlled comparisons of the effect of patient-specific instrumentation on the coronal alignment of total knee replacement were included. The main outcome was the risk difference measured by the proportion of failures in the control group minus the proportion of failures in the experimental group. Through Bayesian statistics, we estimated cumulatively over publication time of the trial results: the posterior probabilities that the risk difference was more than 5 and 10%; the posterior probabilities that given the results of all previous published trials an additional fictive trial would achieve a risk difference of at least 5%; and the predictive probabilities that observed failure rate differ from 5% across arms. Thirteen trials were identified including 1092 patients, 554 in the experimental group and 538 in the control group. The cumulative mean risk difference was 0.5% (95% CrI: -5.7%; +4.5%). The posterior probabilities that the risk difference be superior to 5 and 10% was less than 5% after trial #4 and trial #2 respectively. The predictive probability that the difference in failure rates was at least 5% dropped from 45% after the first trial down to 11% after the 13th. Last, only unrealistic trial design parameters could change the overall evidence accumulated to date. Bayesian probabilities are readily understandable when discussing the relevance of performing a new trial. It provides investigators the current probability that an experimental treatment be superior to a
Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt
2017-01-01
Background 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. Methods 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. Results 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
Thomas: building Bayesian statistical expert systems to aid in clinical decision making.
Lehmann, H P; Shortliffe, E H
1991-08-01
Knowledge-based system for classical statistical analysis must separate the task of analyzing data from that of using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert system allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to construct statistical models dynamically; (3) to update a statistical model based on the user's prior beliefs and on data from, the methodological concerns evinced by, the study. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The clinical user of such a system can interact with the system at a semantic level appropriate to her fund of methodological knowledge, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker interpret the results of a published randomized clinical trial.
THOMAS: Building Bayesian Statistical Expert Systems to Aid in Clinical Decision Making
Lehmann, Harold P.; Shortliffe, Edward H.
1990-01-01
Previous knowledge-based systems for statistical analysis separate the numeric knowledge needed for data analysis from the heuristic knowledge employed in using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert systems allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to update a statistical model based on the user's prior beliefs and on data from, and methodological concerns evinced by, the study; (3) to construct statistical models dynamically. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge, such as methodological knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The user of such a system can interact with the system at the level of general methodological concerns, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker to interpret the results of a published randomized clinical trial.
Predictive data-derived Bayesian statistic-transport model and simulator of sunken oil mass
NASA Astrophysics Data System (ADS)
Echavarria Gregory, Maria Angelica
Sunken oil is difficult to locate because remote sensing techniques cannot as yet provide views of sunken oil over large areas. Moreover, the oil may re-suspend and sink with changes in salinity, sediment load, and temperature, making deterministic fate models difficult to deploy and calibrate when even the presence of sunken oil is difficult to assess. For these reasons, together with the expense of field data collection, there is a need for a statistical technique integrating limited data collection with stochastic transport modeling. Predictive Bayesian modeling techniques have been developed and demonstrated for exploiting limited information for decision support in many other applications. These techniques brought to a multi-modal Lagrangian modeling framework, representing a near-real time approach to locating and tracking sunken oil driven by intrinsic physical properties of field data collected following a spill after oil has begun collecting on a relatively flat bay bottom. Methods include (1) development of the conceptual predictive Bayesian model and multi-modal Gaussian computational approach based on theory and literature review; (2) development of an object-oriented programming and combinatorial structure capable of managing data, integration and computation over an uncertain and highly dimensional parameter space; (3) creating a new bi-dimensional approach of the method of images to account for curved shoreline boundaries; (4) confirmation of model capability for locating sunken oil patches using available (partial) real field data and capability for temporal projections near curved boundaries using simulated field data; and (5) development of a stand-alone open-source computer application with graphical user interface capable of calibrating instantaneous oil spill scenarios, obtaining sets maps of relative probability profiles at different prediction times and user-selected geographic areas and resolution, and capable of performing post
A Bayesian Formulation of Behavioral Control
ERIC Educational Resources Information Center
Huys, Quentin J. M.; Dayan, Peter
2009-01-01
Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or…
A Bayesian Formulation of Behavioral Control
ERIC Educational Resources Information Center
Huys, Quentin J. M.; Dayan, Peter
2009-01-01
Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or…
MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control
NASA Astrophysics Data System (ADS)
Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming
2017-09-01
Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Harrigan, George G; Harrison, Jay M
2012-01-01
New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.
Shen, Jian; Zhao, Yuan
2010-01-01
Nonpoint source load estimation is an essential part of the development of the bacterial total maximum daily load (TMDL) mandated by the Clean Water Act. However, the currently widely used watershed-receiving water modeling approach is usually associated with a high level of uncertainty and requires long-term observational data and intensive training effort. The load duration curve (LDC) method recommended by the EPA provides a simpler way to estimate bacteria loading. This method, however, does not take into consideration the specific fate and transport mechanisms of the pollutant and cannot address the uncertainty. In this study, a Bayesian statistical approach is applied to the Escherichia coli TMDL development of a stream on the Eastern Shore of Virginia to inversely estimate watershed bacteria loads from the in-stream monitoring data. The mechanism of bacteria transport is incorporated. The effects of temperature, bottom slope, and flow on allowable and existing load calculations are discussed. The uncertainties associated with load estimation are also fully described. Our method combines the merits of LDC, mechanistic modeling, and Bayesian statistics, while overcoming some of the shortcomings associated with these methods. It is a cost-effective tool for bacteria TMDL development and can be modified and applied to multi-segment streams as well.
Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
Burr, Tom
2013-01-01
Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. PMID:24288668
NASA Astrophysics Data System (ADS)
Takamizawa, Hisashi; Itoh, Hiroto; Nishiyama, Yutaka
2016-10-01
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Exploring the Connection Between Sampling Problems in Bayesian Inference and Statistical Mechanics
NASA Technical Reports Server (NTRS)
Pohorille, Andrew
2006-01-01
The Bayesian and statistical mechanical communities often share the same objective in their work - estimating and integrating probability distribution functions (pdfs) describing stochastic systems, models or processes. Frequently, these pdfs are complex functions of random variables exhibiting multiple, well separated local minima. Conventional strategies for sampling such pdfs are inefficient, sometimes leading to an apparent non-ergodic behavior. Several recently developed techniques for handling this problem have been successfully applied in statistical mechanics. In the multicanonical and Wang-Landau Monte Carlo (MC) methods, the correct pdfs are recovered from uniform sampling of the parameter space by iteratively establishing proper weighting factors connecting these distributions. Trivial generalizations allow for sampling from any chosen pdf. The closely related transition matrix method relies on estimating transition probabilities between different states. All these methods proved to generate estimates of pdfs with high statistical accuracy. In another MC technique, parallel tempering, several random walks, each corresponding to a different value of a parameter (e.g. "temperature"), are generated and occasionally exchanged using the Metropolis criterion. This method can be considered as a statistically correct version of simulated annealing. An alternative approach is to represent the set of independent variables as a Hamiltonian system. Considerab!e progress has been made in understanding how to ensure that the system obeys the equipartition theorem or, equivalently, that coupling between the variables is correctly described. Then a host of techniques developed for dynamical systems can be used. Among them, probably the most powerful is the Adaptive Biasing Force method, in which thermodynamic integration and biased sampling are combined to yield very efficient estimates of pdfs. The third class of methods deals with transitions between states described
Evaluation of Oceanic Transport Statistics By Use of Transient Tracers and Bayesian Methods
NASA Astrophysics Data System (ADS)
Trossman, D. S.; Thompson, L.; Mecking, S.; Bryan, F.; Peacock, S.
2013-12-01
Key variables that quantify the time scales over which atmospheric signals penetrate into the oceanic interior and their uncertainties are computed using Bayesian methods and transient tracers from both models and observations. First, the mean residence times, subduction rates, and formation rates of Subtropical Mode Water (STMW) and Subpolar Mode Water (SPMW) in the North Atlantic and Subantarctic Mode Water (SAMW) in the Southern Ocean are estimated by combining a model and observations of chlorofluorocarbon-11 (CFC-11) via Bayesian Model Averaging (BMA), statistical technique that weights model estimates according to how close they agree with observations. Second, a Bayesian method is presented to find two oceanic transport parameters associated with the age distribution of ocean waters, the transit-time distribution (TTD), by combining an eddying global ocean model's estimate of the TTD with hydrographic observations of CFC-11, temperature, and salinity. Uncertainties associated with objectively mapping irregularly spaced bottle data are quantified by making use of a thin-plate spline and then propagated via the two Bayesian techniques. It is found that the subduction of STMW, SPMW, and SAMW is mostly an advective process, but up to about one-third of STMW subduction likely owes to non-advective processes. Also, while the formation of STMW is mostly due to subduction, the formation of SPMW is mostly due to other processes. About half of the formation of SAMW is due to subduction and half is due to other processes. A combination of air-sea flux, acting on relatively short time scales, and turbulent mixing, acting on a wide range of time scales, is likely the dominant SPMW erosion mechanism. Air-sea flux is likely responsible for most STMW erosion, and turbulent mixing is likely responsible for most SAMW erosion. Two oceanic transport parameters, the mean age of a water parcel and the half-variance associated with the TTD, estimated using the model's tracers as
Statistical Inference at Work: Statistical Process Control as an Example
ERIC Educational Resources Information Center
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Statistical Inference at Work: Statistical Process Control as an Example
ERIC Educational Resources Information Center
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Wu, Jianyong; Gronewold, Andrew D; Rodriguez, Roberto A; Stewart, Jill R; Sobsey, Mark D
2014-02-01
Rapid quantification of viral pathogens in drinking and recreational water can help reduce waterborne disease risks. For this purpose, samples in small volume (e.g. 1L) are favored because of the convenience of collection, transportation and processing. However, the results of viral analysis are often subject to uncertainty. To overcome this limitation, we propose an approach that integrates Bayesian statistics, efficient concentration methods, and quantitative PCR (qPCR) to quantify viral pathogens in water. Using this approach, we quantified human adenoviruses (HAdVs) in eighteen samples of source water collected from six drinking water treatment plants. HAdVs were found in seven samples. In the other eleven samples, HAdVs were not detected by qPCR, but might have existed based on Bayesian inference. Our integrated approach that quantifies uncertainty provides a better understanding than conventional assessments of potential risks to public health, particularly in cases when pathogens may present a threat but cannot be detected by traditional methods.
2016-05-31
information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. West Virginia University...Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling Dr. Suzanne Bell, West Virginia University 1. Basis of the
A Bayesian statistical model for hybrid metrology to improve measurement accuracy
NASA Astrophysics Data System (ADS)
Silver, R. M.; Zhang, N. F.; Barnes, B. M.; Qin, J.; Zhou, H.; Dixson, R.
2011-05-01
We present a method to combine measurements from different techniques that reduces uncertainties and can improve measurement throughput. The approach directly integrates the measurement analysis of multiple techniques that can include different configurations or platforms. This approach has immediate application when performing model-based optical critical dimension (OCD) measurements. When modeling optical measurements, a library of curves is assembled through the simulation of a multi-dimensional parameter space. Parametric correlation and measurement noise lead to measurement uncertainty in the fitting process with fundamental limitations resulting from the parametric correlations. A strategy to decouple parametric correlation and reduce measurement uncertainties is described. We develop the rigorous underlying Bayesian statistical model and apply this methodology to OCD metrology. We then introduce an approach to damp the regression process to achieve more stable and rapid regression fitting. These methods that use a priori information are shown to reduce measurement uncertainty and improve throughput while also providing an improved foundation for comprehensive reference metrology.
Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall
2016-01-01
Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
Onisko, Agnieszka; Druzdzel, Marek J.; Austin, R. Marshall
2016-01-01
Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan–Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion: Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches. PMID:28163973
NASA Astrophysics Data System (ADS)
Moradkhani, Hamid
2015-04-01
Drought forecasting is vital for resource management and planning. Both societal and agricultural requirements for water weigh heavily on the natural environment, which may become scarce in the event of drought. Although drought forecasts are an important tool for managing water in hydrologic systems, these forecasts are plagued by uncertainties, owing to the complexities of water dynamics and the spatial heterogeneities of pertinent variables. Due to these uncertainties, it is necessary to frame forecasts in a probabilistic manner. Here we present a statistical-dynamical probabilistic drought forecast framework within Bayesian networks. The statistical forecast model applies a family of multivariate distribution functions to forecast future drought conditions given the drought status in the past. The advantage of the statistical forecast model is that it develops conditional probabilities of a given forecast variable, and returns the highest probable forecast along with an assessment of the uncertainty around that value. The dynamical model relies on data assimilation to characterize the initial land surface condition uncertainty which correspondingly reflect on drought forecast. In addition, the recovery of drought will be examined. From these forecasts, it is found that drought recovery is a longer process than suggested in recent literature. Drought in land surface variables (snow, soil moisture) is shown to be persistent up to a year in certain locations, depending on the intensity of the drought. Location within the basin appears to be a driving factor in the ability of the land surface to recover from drought, allowing for differentiation between drought prone and drought resistant regions.
How to construct the optimal Bayesian measurement in quantum statistical decision theory
NASA Astrophysics Data System (ADS)
Tanaka, Fuyuhiko
Recently, much more attention has been paid to the study aiming at the application of fundamental properties in quantum theory to information processing and technology. In particular, modern statistical methods have been recognized in quantum state tomography (QST), where we have to estimate a density matrix (positive semidefinite matrix of trace one) representing a quantum system from finite data collected in a certain experiment. When the dimension of the density matrix gets large (from a few hundred to millions), it gets a nontrivial problem. While a specific measurement is often given and fixed in QST, we are also able to choose a measurement itself according to the purpose of QST by using qunatum statistical decision theory. Here we propose a practical method to find the best projective measurement in the Bayesian sense. We assume that a prior distribution (e.g., the uniform distribution) and a convex loss function (e.g., the squared error) are given. In many quantum experiments, these assumptions are not so restrictive. We show that the best projective measurement and the best statistical inference based on the measurement outcome exist and that they are obtained explicitly by using the Monte Carlo optimization. The Grant-in-Aid for Scientific Research (B) (No. 26280005).
Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control
NASA Technical Reports Server (NTRS)
Schumann, Johann; Mbaya, Timmy; Menghoel, Ole
2011-01-01
Modern aircraft, both piloted fly-by-wire commercial aircraft as well as UAVs, more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software. In this paper, we discuss the use of Bayesian networks (BNs) to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We will focus on the approach to develop reliable and robust health models for the combined software and sensor systems.
A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
Aeschbacher, Simon; Beaumont, Mark A.; Futschik, Andreas
2012-01-01
The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θanc = 4Neu) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L2-loss performs best. Applying that method to the ibex data, we estimate θ^anc≈1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10−4 and 3.5 × 10−3 per locus per generation. The proportion of males with access to matings is estimated as ω^≈0.21, which is in good agreement with recent independent estimates. PMID:22960215
Automated parameter estimation for biological models using Bayesian statistical model checking
2015-01-01
Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system. Results Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. Conclusions We have developed a new algorithmic technique for discovering parameters in complex stochastic models of
To be certain about the uncertainty: Bayesian statistics for (13) C metabolic flux analysis.
Theorell, Axel; Leweke, Samuel; Wiechert, Wolfgang; Nöh, Katharina
2017-11-01
(13) C Metabolic Fluxes Analysis ((13) C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of (13) C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to (13) C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in (13) C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer. © 2017 Wiley Periodicals, Inc.
Chen, Wenan; McDonnell, Shannon K; Thibodeau, Stephen N; Tillmans, Lori S; Schaid, Daniel J
2016-11-01
Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.
Harrison, Jay M; Breeze, Matthew L; Harrigan, George G
2011-08-01
Statistical comparisons of compositional data generated on genetically modified (GM) crops and their near-isogenic conventional (non-GM) counterparts typically rely on classical significance testing. This manuscript presents an introduction to Bayesian methods for compositional analysis along with recommendations for model validation. The approach is illustrated using protein and fat data from two herbicide tolerant GM soybeans (MON87708 and MON87708×MON89788) and a conventional comparator grown in the US in 2008 and 2009. Guidelines recommended by the US Food and Drug Administration (FDA) in conducting Bayesian analyses of clinical studies on medical devices were followed. This study is the first Bayesian approach to GM and non-GM compositional comparisons. The evaluation presented here supports a conclusion that a Bayesian approach to analyzing compositional data can provide meaningful and interpretable results. We further describe the importance of method validation and approaches to model checking if Bayesian approaches to compositional data analysis are to be considered viable by scientists involved in GM research and regulation.
Hewett, Paul; Bullock, William H
2014-01-01
For more than 20 years CSX Transportation (CSXT) has collected exposure measurements from locomotive engineers and conductors who are potentially exposed to diesel emissions. The database included measurements for elemental and total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, carbon monoxide, and nitrogen dioxide. This database was statistically analyzed and summarized, and the resulting statistics and exposure profiles were compared to relevant occupational exposure limits (OELs) using both parametric and non-parametric descriptive and compliance statistics. Exposure ratings, using the American Industrial Health Association (AIHA) exposure categorization scheme, were determined using both the compliance statistics and Bayesian Decision Analysis (BDA). The statistical analysis of the elemental carbon data (a marker for diesel particulate) strongly suggests that the majority of levels in the cabs of the lead locomotives (n = 156) were less than the California guideline of 0.020 mg/m(3). The sample 95th percentile was roughly half the guideline; resulting in an AIHA exposure rating of category 2/3 (determined using BDA). The elemental carbon (EC) levels in the trailing locomotives tended to be greater than those in the lead locomotive; however, locomotive crews rarely ride in the trailing locomotive. Lead locomotive EC levels were similar to those reported by other investigators studying locomotive crew exposures and to levels measured in urban areas. Lastly, both the EC sample mean and 95%UCL were less than the Environmental Protection Agency (EPA) reference concentration of 0.005 mg/m(3). With the exception of nitrogen dioxide, the overwhelming majority of the measurements for total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, and combustion gases in the cabs of CSXT locomotives were either non-detects or considerably less than the working OELs for the years represented in the database. When compared to the previous American
Viscous magnetization, archaeology and Bayesian statistics of small samples from Israel and England
NASA Astrophysics Data System (ADS)
Borradaile, Graham J.
2003-05-01
Certain limestones remagnetize viscously and noticeably over archaeological time-intervals, after their reorientation into monuments. The laboratory demagnetization temperatures (TUB) for the VRM increase with the installation age; with rates of ~0.07 C0/year for Israel chalk and ~0.1C0/year for English chalk. The empirical relationship may be used to date enigmatic buildings or geomorphological features (e.g., land slips). Such correlations also give some insight into the viscous remagnetization process over time intervals τ <= 4000 years, which are unobtainable in laboratory studies. The TUB-age relationship for the viscous remagnetization appears to follow a power law, linearized as log10(τ) ≈ b log10 (TUB). Different pelagic limestones follow different curves and, whereas conventional regression estimates the power law exponent b, the small sample size recommends a Bayesian statistical approach. From sites constructed with pelagic chalk from eastern England, precise prior information (b = 0.761) is compared with less precise information for much more ancient sites in northern Israel (b = 0.873). The collective posterior correlation shows a generalized power law exponent b = 0.849. That regression explains 84.9% of the collective variance in age (r2 = 0.849). Of course, site-specific calibration is required for archaeological age determinations.
Francis, Royce A; Vanbriesen, Jeanne M; Small, Mitchell J
2010-02-15
Statistical models are developed for bromine incorporation in the trihalomethane (THM), trihaloacetic acids (THAA), dihaloacetic acid (DHAA), and dihaloacetonitrile (DHAN) subclasses of disinfection byproducts (DBPs) using distribution system samples from plants applying only free chlorine as a primary or residual disinfectant in the Information Collection Rule (ICR) database. The objective of this study is to characterize the effect of water quality conditions before, during, and post-treatment on distribution system bromine incorporation into DBP mixtures. Bayesian Markov Chain Monte Carlo (MCMC) methods are used to model individual DBP concentrations and estimate the coefficients of the linear models used to predict the bromine incorporation fraction for distribution system DBP mixtures in each of the four priority DBP classes. The bromine incorporation models achieve good agreement with the data. The most important predictors of bromine incorporation fraction across DBP classes are alkalinity, specific UV absorption (SUVA), and the bromide to total organic carbon ratio (Br:TOC) at the first point of chlorine addition. Free chlorine residual in the distribution system, distribution system residence time, distribution system pH, turbidity, and temperature only slightly influence bromine incorporation. The bromide to applied chlorine (Br:Cl) ratio is not a significant predictor of the bromine incorporation fraction (BIF) in any of the four classes studied. These results indicate that removal of natural organic matter and the location of chlorine addition are important treatment decisions that have substantial implications for bromine incorporation into disinfection byproduct in drinking waters.
Automated parameter estimation for biological models using Bayesian statistical model checking.
Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K
2015-01-01
Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.
NASA Astrophysics Data System (ADS)
Stenning, D. C.; Wagner-Kaiser, R.; Robinson, E.; van Dyk, D. A.; von Hippel, T.; Sarajedini, A.; Stein, N.
2016-07-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations. Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties—age, metallicity, helium abundance, distance, absorption, and initial mass—are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show how model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).
NASA Astrophysics Data System (ADS)
Cubillos, Patricio; Harrington, Joseph; Blecic, Jasmina; Stemm, Madison M.; Lust, Nate B.; Foster, Andrew S.; Rojo, Patricio M.; Loredo, Thomas J.
2014-11-01
Multi-wavelength secondary-eclipse and transit depths probe the thermo-chemical properties of exoplanets. In recent years, several research groups have developed retrieval codes to analyze the existing data and study the prospects of future facilities. However, the scientific community has limited access to these packages. Here we premiere the open-source Bayesian Atmospheric Radiative Transfer (BART) code. We discuss the key aspects of the radiative-transfer algorithm and the statistical package. The radiation code includes line databases for all HITRAN molecules, high-temperature H2O, TiO, and VO, and includes a preprocessor for adding additional line databases without recompiling the radiation code. Collision-induced absorption lines are available for H2-H2 and H2-He. The parameterized thermal and molecular abundance profiles can be modified arbitrarily without recompilation. The generated spectra are integrated over arbitrary bandpasses for comparison to data. BART's statistical package, Multi-core Markov-chain Monte Carlo (MC3), is a general-purpose MCMC module. MC3 implements the Differental-evolution Markov-chain Monte Carlo algorithm (ter Braak 2006, 2009). MC3 converges 20-400 times faster than the usual Metropolis-Hastings MCMC algorithm, and in addition uses the Message Passing Interface (MPI) to parallelize the MCMC chains. We apply the BART retrieval code to the HD 209458b data set to estimate the planet's temperature profile and molecular abundances. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.
NASA Astrophysics Data System (ADS)
Riccio, A.; Caporaso, L.; di Giuseppe, F.; Bonafè, G.; Gobbi, G. P.; Angelini, A.
2010-09-01
The nowadays availability of low-cost commercial LIDAR/ceilometer, provides the opportunity to widely employ these active instruments to furnish continuous observation of the planetary boundary layer (PBL) evolution which could serve the scope of both air-quality model initialization and numerical weather prediction system evaluation. Their range-corrected signal is in fact proportional to the aerosol backscatter cross section, and therefore, in clear conditions, it allows to track the PBL evolution using aerosols as markers. The LIDAR signal is then processed to retrieve an estimate of the PBL mixing height. A standard approach uses the so called wavelet covariance transform (WCT) method which consists in the convolution of the vertical signal with a step function, which is able to detect local discontinuities in the backscatter profile. There are, nevertheless, several drawbacks which have to be considered when the WCT method is employed. Since water droplets may have a very large extinction and backscattering cross section, the presence of rain, clouds or fog decreases the returning signal causing interference and uncertainties in the mixing height retrievals. Moreover, if vertical mixing is scarce, aerosols remain suspended in a persistent residual layer which is detected even if it is not significantly connected to the actual mixing height. Finally, multiple layers are also cause of uncertainties. In this work we present a novel methodology to infer the height of planetary boundary layers (PBLs) from LIDAR data which corrects the unrealistic fluctuations introduced by the WCT method. It implements the assimilation of WCT-PBL heights estimations into a Bayesian statistical inference procedure which includes a physical model for the boundary layer (bulk model) as the first guess hypothesis. A hierarchical Bayesian Markov chain Monte Carlo (MCMC) approach is then used to explore the posterior state space and calculate the data likelihood of previously assigned
Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.
Raso, Giovanna; Schur, Nadine; Utzinger, Jürg; Koudou, Benjamin G; Tchicaya, Emile S; Rohner, Fabian; N'goran, Eliézer K; Silué, Kigbafori D; Matthys, Barbara; Assi, Serge; Tanner, Marcel; Vounatsou, Penelope
2012-05-09
In Côte d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d'Ivoire at high spatial resolution. Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d'Ivoire, including uncertainty. Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. The malaria risk map at high spatial resolution gives an
Bayesian Statistical Analysis of Historical and Late Holocene Rates of Sea-Level Change
NASA Astrophysics Data System (ADS)
Cahill, Niamh; Parnell, Andrew; Kemp, Andrew; Horton, Benjamin
2014-05-01
A fundamental concern associated with climate change is the rate at which sea levels are rising. Studies of past sea level (particularly beyond the instrumental data range) allow modern sea-level rise to be placed in a more complete context. Considering this, we perform a Bayesian statistical analysis on historical and late Holocene rates of sea-level change. The data that form the input to the statistical model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. The aims are to estimate rates of sea-level rise, to determine when modern rates of sea-level rise began and to observe how these rates have been changing over time. Many of the current methods for doing this use simple linear regression to estimate rates. This is often inappropriate as it is too rigid and it can ignore uncertainties that arise as part of the data collection exercise. This can lead to over confidence in the sea-level trends being characterized. The proposed Bayesian model places a Gaussian process prior on the rate process (i.e. the process that determines how rates of sea-level are changing over time). The likelihood of the observed data is the integral of this process. When dealing with proxy reconstructions, this is set in an errors-in-variables framework so as to take account of age uncertainty. It is also necessary, in this case, for the model to account for glacio-isostatic adjustment, which introduces a covariance between individual age and sea-level observations. This method provides a flexible fit and it allows for the direct estimation of the rate process with full consideration of all sources of uncertainty. Analysis of tide-gauge datasets and proxy reconstructions in this way means that changing rates of sea level can be estimated more comprehensively and accurately than previously possible. The model captures the continuous and dynamic evolution of sea-level change and results show that not only are modern sea levels rising but that the rates
Harrison, Jay M; Breeze, Matthew L; Berman, Kristina H; Harrigan, George G
2013-03-01
Bayesian approaches to evaluation of crop composition data allow simpler interpretations than traditional statistical significance tests. An important advantage of Bayesian approaches is that they allow formal incorporation of previously generated data through prior distributions in the analysis steps. This manuscript describes key steps to ensure meaningful and transparent selection and application of informative prior distributions. These include (i) review of previous data in the scientific literature to form the prior distributions, (ii) proper statistical model specification and documentation, (iii) graphical analyses to evaluate the fit of the statistical model to new study data, and (iv) sensitivity analyses to evaluate the robustness of results to the choice of prior distribution. The validity of the prior distribution for any crop component is critical to acceptance of Bayesian approaches to compositional analyses and would be essential for studies conducted in a regulatory setting. Selection and validation of prior distributions for three soybean isoflavones (daidzein, genistein, and glycitein) and two oligosaccharides (raffinose and stachyose) are illustrated in a comparative assessment of data obtained on GM and non-GM soybean seed harvested from replicated field sites at multiple locations in the US during the 2009 growing season.
An overview of component qualification using Bayesian statistics and energy methods.
Dohner, Jeffrey Lynn
2011-09-01
The below overview is designed to give the reader a limited understanding of Bayesian and Maximum Likelihood (MLE) estimation; a basic understanding of some of the mathematical tools to evaluate the quality of an estimation; an introduction to energy methods and a limited discussion of damage potential. This discussion then goes on to presented a limited presentation as to how energy methods and Bayesian estimation are used together to qualify components. Example problems with solutions have been supplied as a learning aid. Bold letters are used to represent random variables. Un-bolded letter represent deterministic values. A concluding section presents a discussion of attributes and concerns.
NASA Astrophysics Data System (ADS)
Rolinski, S.; Müller, C.; Lotze-Campen, H.; Bondeau, A.
2010-12-01
information on boundary conditions such as water and light availability or temperature sensibility. Based on the given limitation factors, a number of sensitive parameters are chosen, e.g. for the phenological development, biomass allocation, and different management regimes. These are introduced to a sensitivity analysis and Bayesian parameter evaluation using the R package FME (Soetart & Petzoldt, Journal of Statistical Software, 2010). Given the extremely different climatic conditions at the FluxNet grass sites, the premises for the global sensitivity analysis are very promising.
Statistical Physics for Adaptive Distributed Control
NASA Technical Reports Server (NTRS)
Wolpert, David H.
2005-01-01
A viewgraph presentation on statistical physics for distributed adaptive control is shown. The topics include: 1) The Golden Rule; 2) Advantages; 3) Roadmap; 4) What is Distributed Control? 5) Review of Information Theory; 6) Iterative Distributed Control; 7) Minimizing L(q) Via Gradient Descent; and 8) Adaptive Distributed Control.
NASA Astrophysics Data System (ADS)
Schöniger, Anneli; Wöhling, Thomas; Nowak, Wolfgang
2015-09-01
Bayesian model averaging (BMA) ranks the plausibility of alternative conceptual models according to Bayes' theorem. A prior belief about each model's adequacy is updated to a posterior model probability based on the skill to reproduce observed data and on the principle of parsimony. The posterior model probabilities are then used as model weights for model ranking, selection, or averaging. Despite the statistically rigorous BMA procedure, model weights can become uncertain quantities due to measurement noise in the calibration data set or due to uncertainty in model input. Uncertain weights may in turn compromise the reliability of BMA results. We present a new statistical concept to investigate this weighting uncertainty, and thus, to assess the significance of model weights and the confidence in model ranking. Our concept is to resample the uncertain input or output data and then to analyze the induced variability in model weights. In the special case of weighting uncertainty due to measurement noise in the calibration data set, we interpret statistics of Bayesian model evidence to assess the distance of a model's performance from the theoretical upper limit. To illustrate our suggested approach, we investigate the reliability of soil-plant model selection following up on a study by Wöhling et al. (2015). Results show that the BMA routine should be equipped with our suggested upgrade to (1) reveal the significant but otherwise undetected impact of measurement noise on model ranking results and (2) to decide whether the considered set of models should be extended with better performing alternatives.
Sethi, Anurag; Anunciado, Divina; Tian, Jianhui; Vu, Dung M.; Gnanakaran, S.
2013-01-01
As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts. PMID:24187427
Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.
Epstein, Michael; Calderhead, Ben; Girolami, Mark A; Sivilotti, Lucia G
2016-07-26
The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel
Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods
NASA Astrophysics Data System (ADS)
Davis, A. D.
2015-12-01
The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity
Higgins, H M; Huxley, J N; Wapenaar, W; Green, M J
2014-01-01
The clinical beliefs (expectations and demands) of veterinarians regarding herd-level strategies to control mastitis, lameness, and Johne's disease were quantified in a numerical format; 94 veterinarians working in England (UK) were randomly selected and, during interviews, a statistical technique called probabilistic elicitation was used to capture their clinical expectations as probability distributions. The results revealed that markedly different clinical expectations existed for all 3 diseases, and many pairs of veterinarians had expectations with nonoverlapping 95% Bayesian credible intervals. For example, for a 3-yr lameness intervention, the most pessimistic veterinarian was centered at an 11% population mean reduction in lameness prevalence (95% credible interval: 0-21%); the most enthusiastic veterinarian was centered at a 58% reduction (95% credible interval: 38-78%). This suggests that a major change in beliefs would be required to achieve clinical agreement. Veterinarians' clinical expectations were used as priors in Bayesian models where they were combined with synthetic data (from randomized clinical trials of different sizes) to explore the effect of new evidence on current clinical opinion. The mathematical models make predictions based on the assumption that veterinarians will update their beliefs logically. For example, for the lameness intervention, a 200-farm clinical trial that estimated a 30% mean reduction in lameness prevalence was predicted to be reasonably convincing to the most pessimist veterinarian; that is, in light of this data, they were predicted to believe there would be a 0.92 probability of exceeding the median clinical demand of this sample of veterinarians, which was a 20% mean reduction in lameness. Currently, controversy exists over the extent to which veterinarians update their beliefs logically, and further research on this is needed. This study has demonstrated that probabilistic elicitation and a Bayesian framework are
Higgins, H. M.; Huxley, J. N.; Wapenaar, W.; Green, M.J.
2017-01-01
The clinical beliefs (expectations and demands) of veterinarians regarding herd-level strategies to control mastitis, lameness and Johne’s disease were quantified in a numerical format; 94 veterinarians working in England (UK) were randomly selected and during interviews, a statistical technique called ‘probabilistic elicitation’ was used to capture their clinical expectations as probability distributions. The results revealed that markedly different clinical expectations existed for all 3 diseases, and many pairs of veterinarians had expectations with non-overlapping 95% Bayesian credible intervals; for example, for a 3 yr lameness intervention, the most pessimistic veterinarian was centred at an 11% population mean reduction in lameness prevalence (95% credible interval: 0-21%); the most enthusiastic veterinarian was centred at a 58% reduction (95% credible interval: 38-78%). This suggests that a major change in beliefs would be required to achieve clinical agreement. The veterinarians’ clinical expectations were used as priors in Bayesian models where they were combined with synthetic data (from randomized clinical trials of different sizes) in order to explore the impact of new evidence on current clinical opinion. The mathematical models make predictions based on the assumption that veterinarians will update their beliefs logically. For example, for the lameness intervention, a 200 farm clinical trial that estimated a 30% mean reduction in lameness prevalence was predicted to be reasonably convincing to the most pessimist veterinarian; i.e. in light of this data, they were predicted to believe there would be a 0.92 probability of exceeding the median clinical demand of this sample of veterinarians, which was a 20% mean reduction in lameness. Currently controversy exists over the extent to which veterinarians update their beliefs logically, and further research on this is needed. This study has demonstrated that probabilistic elicitation and a Bayesian
Gomez-Ramirez, Jaime; Sanz, Ricardo
2013-09-01
One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.
An absolute chronology for early Egypt using radiocarbon dating and Bayesian statistical modelling.
Dee, Michael; Wengrow, David; Shortland, Andrew; Stevenson, Alice; Brock, Fiona; Girdland Flink, Linus; Bronk Ramsey, Christopher
2013-11-08
The Egyptian state was formed prior to the existence of verifiable historical records. Conventional dates for its formation are based on the relative ordering of artefacts. This approach is no longer considered sufficient for cogent historical analysis. Here, we produce an absolute chronology for Early Egypt by combining radiocarbon and archaeological evidence within a Bayesian paradigm. Our data cover the full trajectory of Egyptian state formation and indicate that the process occurred more rapidly than previously thought. We provide a timeline for the First Dynasty of Egypt of generational-scale resolution that concurs with prevailing archaeological analysis and produce a chronometric date for the foundation of Egypt that distinguishes between historical estimates.
STATISTICS OF MEASURING NEUTRON STAR RADII: ASSESSING A FREQUENTIST AND A BAYESIAN APPROACH
Özel, Feryal; Psaltis, Dimitrios
2015-09-10
Measuring neutron star radii with spectroscopic and timing techniques relies on the combination of multiple observables to break the degeneracies between the mass and radius introduced by general relativistic effects. Here, we explore a previously used frequentist and a newly proposed Bayesian framework to obtain the most likely value and the uncertainty in such a measurement. We find that for the expected range of masses and radii and for realistic measurement errors, the frequentist approach suffers from biases that are larger than the accuracy in the radius measurement required to distinguish between the different equations of state. In contrast, in the Bayesian framework, the inferred uncertainties are larger, but the most likely values do not suffer from such biases. We also investigate ways of quantifying the degree of consistency between different spectroscopic measurements from a single source. We show that a careful assessment of the systematic uncertainties in the measurements eliminates the need for introducing ad hoc biases, which lead to artificially large inferred radii.
Uncovering selection bias in case-control studies using Bayesian post-stratification.
Geneletti, S; Best, N; Toledano, M B; Elliott, P; Richardson, S
2013-07-10
Case-control studies are particularly prone to selection bias, which can affect odds ratio estimation. Approaches to discovering and adjusting for selection bias have been proposed in the literature using graphical and heuristic tools as well as more complex statistical methods. The approach we propose is based on a survey-weighting method termed Bayesian post-stratification and follows from the conditional independences that characterise selection bias. We use our approach to perform a selection bias sensitivity analysis by using ancillary data sources that describe the target case-control population to re-weight the odds ratio estimates obtained from the study. The method is applied to two case-control studies, the first investigating the association between exposure to electromagnetic fields and acute lymphoblastic leukaemia in children and the second investigating the association between maternal occupational exposure to hairspray and a congenital anomaly in male babies called hypospadias. In both case-control studies, our method showed that the odds ratios were only moderately sensitive to selection bias.
Sandoval-Castellanos, Edson; Palkopoulou, Eleftheria; Dalén, Love
2014-01-01
Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.
Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task
Stevenson, Ian H.; Fernandes, Hugo L.; Vilares, Iris; Wei, Kunlin; Körding, Konrad P.
2009-01-01
A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task. PMID:20041205
Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems
NASA Technical Reports Server (NTRS)
He, Yuning; Davies, Misty Dawn
2014-01-01
The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.
Statistical quality control for VLSIC fabrication processes
Mozumder, P.K.
1989-01-01
As the complexity of VLSICs increase and the device dimension shrink, random fluctuations become the main reason limiting the par metric yield. Whenever a new process is developed, the initial yield are low. The rate of climbing the learning curve is slow, i.e., the time necessary to bring the yield above an economically acceptable value can be unacceptably long, resulting in lost revenue and competitive edge in the market. The slow rates of climbing the learning curve and the low initial yields can be countered by using design methodologies that take into account the random fluctuations in the fabrication processes, and using statistical on-line and off-line control during the wafer fabrication. An integrated CAD-CAM approach with profit maximization as the objective is necessary to design and fabricate present day VLSICs. In this thesis the author proposes a methodology for monitoring and statistically controlling VLSIC manufacturing processes as part of an integrated CAD-CAM system. Present day statistical quality control systems fail to function satisfactorily due to lack of in-situ and in-line data, and absence of statistical techniques that take into account the multi-dimensionality of the data. A concerted effort has to be made to increase the number of in-situ parameters that are measured during the fabrication process using new generation equipment and sensors. Algorithms for identifying the minimal set of observable in-situ and in-line parameters that have to be measured to monitor the fabrication process are presented. The methodology for statistical quality control is based on the exploration of the multivariate distribution of the observed in-process parameters in the region of acceptability specified by the customer. Criteria for comparing the distributions of the normal process to that of the process under control are used to make the quality control decisions.
Applied Behavior Analysis and Statistical Process Control?
ERIC Educational Resources Information Center
Hopkins, B. L.
1995-01-01
Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…
Applied Behavior Analysis and Statistical Process Control?
ERIC Educational Resources Information Center
Hopkins, B. L.
1995-01-01
Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…
Statistical process control for total quality
NASA Astrophysics Data System (ADS)
Ali, Syed W.
1992-06-01
The paper explains the techniques and applications of statistical process control (SPC). Examples of control charts used in the Poseidon program of the NASA ocean topography experiment (TOPEX) and a brief discussion of Taguchi methods are presented. It is noted that SPC involves everyone in process improvement by providing objective, workable data. It permits continuous improvement instead of merely aiming for all parts to be within a tolerance band.
ERIC Educational Resources Information Center
Denbleyker, John Nickolas
2012-01-01
The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better…
ERIC Educational Resources Information Center
Denbleyker, John Nickolas
2012-01-01
The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better…
Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion
Johnson, Stephanie; van de Meent, Jan-Willem; Phillips, Rob; Wiggins, Chris H.; Lindén, Martin
2014-01-01
The bacterial transcription factor LacI loops DNA by binding to two separate locations on the DNA simultaneously. Despite being one of the best-studied model systems for transcriptional regulation, the number and conformations of loop structures accessible to LacI remain unclear, though the importance of multiple coexisting loops has been implicated in interactions between LacI and other cellular regulators of gene expression. To probe this issue, we have developed a new analysis method for tethered particle motion, a versatile and commonly used in vitro single-molecule technique. Our method, vbTPM, performs variational Bayesian inference in hidden Markov models. It learns the number of distinct states (i.e. DNA–protein conformations) directly from tethered particle motion data with better resolution than existing methods, while easily correcting for common experimental artifacts. Studying short (roughly 100 bp) LacI-mediated loops, we provide evidence for three distinct loop structures, more than previously reported in single-molecule studies. Moreover, our results confirm that changes in LacI conformation and DNA-binding topology both contribute to the repertoire of LacI-mediated loops formed in vitro, and provide qualitatively new input for models of looping and transcriptional regulation. We expect vbTPM to be broadly useful for probing complex protein–nucleic acid interactions. PMID:25120267
Bayesian statistics applied to the location of the source of explosions at Stromboli Volcano, Italy
Saccorotti, G.; Chouet, B.; Martini, M.; Scarpa, R.
1998-01-01
We present a method for determining the location and spatial extent of the source of explosions at Stromboli Volcano, Italy, based on a Bayesian inversion of the slowness vector derived from frequency-slowness analyses of array data. The method searches for source locations that minimize the error between the expected and observed slowness vectors. For a given set of model parameters, the conditional probability density function of slowness vectors is approximated by a Gaussian distribution of expected errors. The method is tested with synthetics using a five-layer velocity model derived for the north flank of Stromboli and a smoothed velocity model derived from a power-law approximation of the layered structure. Application to data from Stromboli allows for a detailed examination of uncertainties in source location due to experimental errors and incomplete knowledge of the Earth model. Although the solutions are not constrained in the radial direction, excellent resolution is achieved in both transverse and depth directions. Under the assumption that the horizontal extent of the source does not exceed the crater dimension, the 90% confidence region in the estimate of the explosive source location corresponds to a small volume extending from a depth of about 100 m to a maximum depth of about 300 m beneath the active vents, with a maximum likelihood source region located in the 120- to 180-m-depth interval.
Lim, Matthew Y; O'Brien, Jonathon; Paulo, Joao A; Gygi, Steven P
2017-10-06
Phosphorylation stoichiometry, or occupancy, is one element of phosphoproteomics that can add useful biological context(1). We previously developed a method to assess phosphorylation stoichiometry on a proteome-wide scale(2). The stoichiometry calculation relies on identifying and measuring the levels of each nonphosphorylated counterpart peptide with and without phosphatase treatment. The method, however, is problematic in that low stoichiometry phosphopeptides can return negative stoichiometry values if measurement error is larger than the percent stoichiometry. Here, we have improved the stoichiometry method through the use of isobaric labeling with 10-plex TMT reagents. In this way, 5 phosphatase treated and 5 untreated samples are compared simultaneously so that each stoichiometry is represented by 5 ratio measurements with no missing values. We applied the method to determine basal stoichiometries of HCT116 cells growing in culture. With this method, we analyzed 5 biological replicates simultaneously with no need for phosphopeptide enrichment. Additionally, we developed a Bayesian model to estimate phosphorylation stoichiometry as a parameter confined to an interval between 0 and 1 implemented as an R/Stan script. Consequently, both point and interval estimates are consistent with the plausible range of values for stoichiometry. Finally, we report absolute stoichiometry measurements with credible intervals for 6,772 phosphopeptides containing at least a single phosphorylation site.
Statistical flaw characterization through Bayesian shape inversion from scattered wave observations
NASA Astrophysics Data System (ADS)
McMahan, Jerry A.; Criner, Amanda K.
2016-02-01
A method is discussed to characterize the shape of a flaw from noisy far-field measurements of a scattered wave. The scattering model employed is a two-dimensional Helmholtz equation which quantifies scattering due to interrogating signals from various physical phenomena such as acoustics or electromagnetics. The well-known inherent ill-posedness of the inverse scattering problem is addressed via Bayesian regularization. The method is loosely related to the approach described in [1] which uses the framework of [2] to prove the well-posedness of the infinite-dimensional problem and derive estimates of the error for a particular discretization approach. The method computes the posterior probability density for the flaw shape from the scattered field observations, taking into account prior assumptions which are used to describe any a priori knowledge of the flaw. We describe the computational approach to the forward problem as well as the Markov chain Monte Carlo (MCMC) based approach to approximating the posterior. We present simulation results for some hypothetical flaw shapes with varying levels of observation error and arrangement of observation points. The results show how the posterior probability density can be used to visualize the shape of the flaw taking into account the quantitative confidence in the quality of the estimation and how various arrangements of the measurements and interrogating signals affect the estimation
A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation
Smith, Anne C; Shah, Sudhin A; Hudson, Andrew E; Purpura, Keith P; Victor, Jonathan D; Brown, Emery N; Schiff, Nicholas D
2009-01-01
Deep brain stimulation (DBS) is an established therapy for Parkinson’s Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of these applications, quantitative assessments of the behavioral effects of DBS are crucial to determine whether the therapy is effective and, if so, how stimulation parameters can be optimized. Behavioral analyses for DBS are challenging because subject performance is typically assessed from only a small set of discrete measurements made on a discrete rating scale, the time course of DBS effects is unknown, and between-subject differences are often large. We demonstrate how Bayesian state-space methods can be used to characterize the relationship between DBS and behavior comparing our approach with logistic regression in two experiments: the effects of DBS on attention of a macaque monkey performing a reaction-time task, and the effects of DBS on motor behavior of a human patient in a minimally conscious state. The state-space analysis can assess the magnitude of DBS behavioral facilitation (positive or negative) at specific time points and has important implications for developing principled strategies to optimize DBS paradigms. PMID:19576932
Bayesian clinical trials in action.
Lee, J Jack; Chu, Caleb T
2012-11-10
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.
Neural network uncertainty assessment using Bayesian statistics: a remote sensing application
NASA Technical Reports Server (NTRS)
Aires, F.; Prigent, C.; Rossow, W. B.
2004-01-01
Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component
Radiative transfer meets Bayesian statistics: where does a galaxy's [C II] emission come from?
NASA Astrophysics Data System (ADS)
Accurso, G.; Saintonge, A.; Bisbas, T. G.; Viti, S.
2017-01-01
The [C II] 158 μm emission line can arise in all phases of the interstellar medium (ISM), therefore being able to disentangle the different contributions is an important yet unresolved problem when undertaking galaxy-wide, integrated [C II] observations. We present a new multiphase 3D radiative transfer interface that couples STARBURST99, a stellar spectrophotometric code, with the photoionization and astrochemistry codes MOCASSIN and 3D-PDR. We model entire star-forming regions, including the ionized, atomic, and molecular phases of the ISM, and apply a Bayesian inference methodology to parametrize how the fraction of the [C II] emission originating from molecular regions, f_{[C II],mol}, varies as a function of typical integrated properties of galaxies in the local Universe. The main parameters responsible for the variations of f_{[C II],mol} are specific star formation rate (SSFR), gas phase metallicity, H II region electron number density (ne), and dust mass fraction. For example, f_{[C II],mol} can increase from 60 to 80 per cent when either ne increases from 101.5 to 102.5 cm-3, or SSFR decreases from 10-9.6 to 10-10.6 yr-1. Our model predicts for the Milky Way that f_{[C II],mol} = 75.8 ± 5.9 per cent, in agreement with the measured value of 75 per cent. When applying the new prescription to a complete sample of galaxies from the Herschel Reference Survey, we find that anywhere from 60 to 80 per cent of the total integrated [C II] emission arises from molecular regions.
Neural network uncertainty assessment using Bayesian statistics: a remote sensing application
NASA Technical Reports Server (NTRS)
Aires, F.; Prigent, C.; Rossow, W. B.
2004-01-01
Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component
A Dynamic Bayesian Network Model for the Production and Inventory Control
NASA Astrophysics Data System (ADS)
Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol
In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.
NASA Astrophysics Data System (ADS)
Schöniger, Anneli; Wöhling, Thomas; Nowak, Wolfgang
2014-05-01
Bayesian model averaging ranks the predictive capabilities of alternative conceptual models based on Bayes' theorem. The individual models are weighted with their posterior probability to be the best one in the considered set of models. Finally, their predictions are combined into a robust weighted average and the predictive uncertainty can be quantified. This rigorous procedure does, however, not yet account for possible instabilities due to measurement noise in the calibration data set. This is a major drawback, since posterior model weights may suffer a lack of robustness related to the uncertainty in noisy data, which may compromise the reliability of model ranking. We present a new statistical concept to account for measurement noise as source of uncertainty for the weights in Bayesian model averaging. Our suggested upgrade reflects the limited information content of data for the purpose of model selection. It allows us to assess the significance of the determined posterior model weights, the confidence in model selection, and the accuracy of the quantified predictive uncertainty. Our approach rests on a brute-force Monte Carlo framework. We determine the robustness of model weights against measurement noise by repeatedly perturbing the observed data with random realizations of measurement error. Then, we analyze the induced variability in posterior model weights and introduce this "weighting variance" as an additional term into the overall prediction uncertainty analysis scheme. We further determine the theoretical upper limit in performance of the model set which is imposed by measurement noise. As an extension to the merely relative model ranking, this analysis provides a measure of absolute model performance. To finally decide, whether better data or longer time series are needed to ensure a robust basis for model selection, we resample the measurement time series and assess the convergence of model weights for increasing time series length. We illustrate
NASA Astrophysics Data System (ADS)
Savoy, H.; Renard, P.; Straubhaar, J.; Rubin, Y.
2016-12-01
Contaminant transport in aquifers is highly dependent on the spatial heterogeneity of hydraulic conductivity. This heterogeneity can exhibit complex spatial patterns in certain geologies, such as from braided river deposits. In order to address this spatial complexity, multipoint statistics methods can be used to generate random fields based on training images. This poster explores inferring conceptual models of heterogeneity from a variety of possible training images using multi-scale and multi-type hydrogeological data via the Method of Anchored Distributions (MAD). MAD has previously been applied in the inference of variogram parameters and this study is the first application of MAD to multipoint statistics and training images as random parameters. The collection of training images used in the study includes images inspired by natural channel networks plus variogram- and object-based random fields with approximately the same low-order statistics. The goal of this study is to showcase the applicability of coupling MAD and multipoint statistics, two generic methods that can constrain the uncertainty attributed to spatial heterogeneity in hydrology and other environmental sciences.
Giambartolomei, Claudia; Vukcevic, Damjan; Schadt, Eric E.; Franke, Lude; Hingorani, Aroon D.; Wallace, Chris; Plagnol, Vincent
2014-01-01
Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to
Statistical quality control through overall vibration analysis
NASA Astrophysics Data System (ADS)
Carnero, M. ^{a.} Carmen; González-Palma, Rafael; Almorza, David; Mayorga, Pedro; López-Escobar, Carlos
2010-05-01
The present study introduces the concept of statistical quality control in automotive wheel bearings manufacturing processes. Defects on products under analysis can have a direct influence on passengers' safety and comfort. At present, the use of vibration analysis on machine tools for quality control purposes is not very extensive in manufacturing facilities. Noise and vibration are common quality problems in bearings. These failure modes likely occur under certain operating conditions and do not require high vibration amplitudes but relate to certain vibration frequencies. The vibration frequencies are affected by the type of surface problems (chattering) of ball races that are generated through grinding processes. The purpose of this paper is to identify grinding process variables that affect the quality of bearings by using statistical principles in the field of machine tools. In addition, an evaluation of the quality results of the finished parts under different combinations of process variables is assessed. This paper intends to establish the foundations to predict the quality of the products through the analysis of self-induced vibrations during the contact between the grinding wheel and the parts. To achieve this goal, the overall self-induced vibration readings under different combinations of process variables are analysed using statistical tools. The analysis of data and design of experiments follows a classical approach, considering all potential interactions between variables. The analysis of data is conducted through analysis of variance (ANOVA) for data sets that meet normality and homoscedasticity criteria. This paper utilizes different statistical tools to support the conclusions such as chi squared, Shapiro-Wilks, symmetry, Kurtosis, Cochran, Hartlett, and Hartley and Krushal-Wallis. The analysis presented is the starting point to extend the use of predictive techniques (vibration analysis) for quality control. This paper demonstrates the existence
Dolejsi, Erich; Bodenstorfer, Bernhard; Frommlet, Florian
2014-01-01
The prevailing method of analyzing GWAS data is still to test each marker individually, although from a statistical point of view it is quite obvious that in case of complex traits such single marker tests are not ideal. Recently several model selection approaches for GWAS have been suggested, most of them based on LASSO-type procedures. Here we will discuss an alternative model selection approach which is based on a modification of the Bayesian Information Criterion (mBIC2) which was previously shown to have certain asymptotic optimality properties in terms of minimizing the misclassification error. Heuristic search strategies are introduced which attempt to find the model which minimizes mBIC2, and which are efficient enough to allow the analysis of GWAS data. Our approach is implemented in a software package called MOSGWA. Its performance in case control GWAS is compared with the two algorithms HLASSO and d-GWASelect, as well as with single marker tests, where we performed a simulation study based on real SNP data from the POPRES sample. Our results show that MOSGWA performs slightly better than HLASSO, where specifically for more complex models MOSGWA is more powerful with only a slight increase in Type I error. On the other hand according to our simulations GWASelect does not at all control the type I error when used to automatically determine the number of important SNPs. We also reanalyze the GWAS data from the Wellcome Trust Case-Control Consortium and compare the findings of the different procedures, where MOSGWA detects for complex diseases a number of interesting SNPs which are not found by other methods.
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.
Patri, Jean-François; Diard, Julien; Perrier, Pascal
2015-12-01
The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.
Miaou, Shaw-Pin; Song, Joon Jin
2005-07-01
limitation of using the naïve approach in ranking is illustrated. Second, following the model based approach, the choice of decision parameters and consideration of treatability are discussed. Third, several statistical ranking criteria that have been used in biomedical, health, and other scientific studies are presented from a Bayesian perspective. Their applications in roadway safety are then demonstrated using two data sets: one for individual urban intersections and one for rural two-lane roads at the county level. As part of the demonstration, it is shown how multivariate spatial GLMM can be used to model traffic crashes of several injury severity types simultaneously and how the model can be used within a Bayesian framework to rank sites by crash cost per vehicle-mile traveled (instead of by crash frequency rate). Finally, the significant impact of spatial effects on the overall model goodness-of-fit and site ranking performances are discussed for the two data sets examined. The paper is concluded with a discussion on possible directions in which the study can be extended.
Statistical Process Control for KSC Processing
NASA Technical Reports Server (NTRS)
Ford, Roger G.; Delgado, Hector; Tilley, Randy
1996-01-01
The 1996 Summer Faculty Fellowship Program and Kennedy Space Center (KSC) served as the basis for a research effort into statistical process control for KSC processing. The effort entailed several tasks and goals. The first was to develop a customized statistical process control (SPC) course for the Safety and Mission Assurance Trends Analysis Group. The actual teaching of this course took place over several weeks. In addition, an Internet version of the same course complete with animation and video excerpts from the course when it was taught at KSC was developed. The application of SPC to shuttle processing took up the rest of the summer research project. This effort entailed the evaluation of SPC use at KSC, both present and potential, due to the change in roles for NASA and the Single Flight Operations Contractor (SFOC). Individual consulting on SPC use was accomplished as well as an evaluation of SPC software for KSC use in the future. A final accomplishment of the orientation of the author to NASA changes, terminology, data format, and new NASA task definitions will allow future consultation when the needs arise.
NASA Astrophysics Data System (ADS)
Mugnes, J.-M.; Robert, C.
2015-11-01
Spectral analysis is a powerful tool to investigate stellar properties and it has been widely used for decades now. However, the methods considered to perform this kind of analysis are mostly based on iteration among a few diagnostic lines to determine the stellar parameters. While these methods are often simple and fast, they can lead to errors and large uncertainties due to the required assumptions. Here, we present a method based on Bayesian statistics to find simultaneously the best combination of effective temperature, surface gravity, projected rotational velocity, and microturbulence velocity, using all the available spectral lines. Different tests are discussed to demonstrate the strength of our method, which we apply to 54 mid-resolution spectra of field and cluster B stars obtained at the Observatoire du Mont-Mégantic. We compare our results with those found in the literature. Differences are seen which are well explained by the different methods used. We conclude that the B-star microturbulence velocities are often underestimated. We also confirm the trend that B stars in clusters are on average faster rotators than field B stars.
Ayubi, Erfan; Mansournia, Mohammad Ali; Motlagh, Ali Ghanbari; Mosavi-Jarrahi, Alireza; Hosseini, Ali; Yazdani, Kamran
2017-01-01
The aim of this study was to explore the spatial pattern of female breast cancer (BC) incidence at the neighborhood level in Tehran, Iran. The present study included all registered incident cases of female BC from March 2008 to March 2011. The raw standardized incidence ratio (SIR) of BC for each neighborhood was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by a Besag, York, and Mollie spatial model and the spatial empirical Bayesian method. The purely spatial scan statistic was used to identify spatial clusters. There were 4,175 incident BC cases in the study area from 2008 to 2011, of which 3,080 were successfully geocoded to the neighborhood level. Higher than expected rates of BC were found in neighborhoods located in northern and central Tehran, whereas lower rates appeared in southern areas. The most likely cluster of higher than expected BC incidence involved neighborhoods in districts 3 and 6, with an observed-to-expected ratio of 3.92 (p<0.001), whereas the most likely cluster of lower than expected rates involved neighborhoods in districts 17, 18, and 19, with an observed-to-expected ratio of 0.05 (p<0.001). Neighborhood-level inequality in the incidence of BC exists in Tehran. These findings can serve as a basis for resource allocation and preventive strategies in at-risk areas.
Janss, LLG.; Van-Arendonk, JAM.; Brascamp, E. W.
1997-01-01
Presence of single genes affecting meat quality traits was investigated in F(2) individuals of a cross between Chinese Meishan and Western pig lines using phenotypic measurements on 11 traits. A Bayesian approach was used for inference about a mixed model of inheritance, postulating effects of polygenic background genes, action of a biallelic autosomal single gene and various nongenetic effects. Cooking loss, drip loss, two pH measurements, intramuscular fat, shearforce and back-fat thickness were traits found to be likely influenced by a single gene. In all cases, a recessive allele was found, which likely originates from the Meishan breed and is absent in the Western founder lines. By studying associations between genotypes assigned to individuals based on phenotypic measurements for various traits, it was concluded that cooking loss, two pH measurements and possibly backfat thickness are influenced by one gene, and that a second gene influences intramuscular fat and possibly shearforce and drip loss. Statistical findings were supported by demonstrating marked differences in variances of families of fathers inferred as carriers and those inferred as noncarriers. It is concluded that further molecular genetic research effort to map single genes affecting these traits based on the same experimental data has a high probability of success. PMID:9071593
Bayesian Clinical Trials in Action
Lee, J. Jack; Chu, Caleb T.
2012-01-01
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. The alternative Bayesian paradigm has been greatly enhanced by advancements in computational algorithms and computer hardware. Compared to its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and for studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation, and analysis, and Web-based applications, which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More such trials should be designed and conducted to refine the approach and demonstrate its real benefit in action. PMID:22711340
Statistical process control for electron beam monitoring.
López-Tarjuelo, Juan; Luquero-Llopis, Naika; García-Mollá, Rafael; Quirós-Higueras, Juan David; Bouché-Babiloni, Ana; Juan-Senabre, Xavier Jordi; de Marco-Blancas, Noelia; Ferrer-Albiach, Carlos; Santos-Serra, Agustín
2015-07-01
To assess the electron beam monitoring statistical process control (SPC) in linear accelerator (linac) daily quality control. We present a long-term record of our measurements and evaluate which SPC-led conditions are feasible for maintaining control. We retrieved our linac beam calibration, symmetry, and flatness daily records for all electron beam energies from January 2008 to December 2013, and retrospectively studied how SPC could have been applied and which of its features could be used in the future. A set of adjustment interventions designed to maintain these parameters under control was also simulated. All phase I data was under control. The dose plots were characterized by rising trends followed by steep drops caused by our attempts to re-center the linac beam calibration. Where flatness and symmetry trends were detected they were less-well defined. The process capability ratios ranged from 1.6 to 9.3 at a 2% specification level. Simulated interventions ranged from 2% to 34% of the total number of measurement sessions. We also noted that if prospective SPC had been applied it would have met quality control specifications. SPC can be used to assess the inherent variability of our electron beam monitoring system. It can also indicate whether a process is capable of maintaining electron parameters under control with respect to established specifications by using a daily checking device, but this is not practical unless a method to establish direct feedback from the device to the linac can be devised. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Distribution modeling of nonlinear inverse controllers under a Bayesian framework.
Herzallah, Randa; Lowe, David
2007-01-01
The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples.
Controlling the statistical properties of expanding maps
NASA Astrophysics Data System (ADS)
Galatolo, Stefano; Pollicott, Mark
2017-07-01
How can one change a system, in order to change its statistical properties in a prescribed way? In this note we consider a control problem related to the theory of linear response. Given an expanding map of the unit circle with an associated invariant density, we can consider the inverse problem of finding which first order changes in the transformation can achieve a given first order perturbation in the density. We show the general mathematical structure of the problem, the existence of many solutions in the case of expanding maps of the circle and the existence of optimal ones. We investigate in depth the example of the doubling map, where we give a complete solution of the problem.
The Statistical Dynamics of Nonequilibrium Control
NASA Astrophysics Data System (ADS)
Rotskoff, Grant Murray
Living systems, even at the scale of single molecules, are constantly adapting to changing environmental conditions. The physical response of a nanoscale system to external gradients or changing thermodynamic conditions can be chaotic, nonlinear, and hence difficult to control or predict. Nevertheless, biology has evolved systems that reliably carry out the cell's vital functions efficiently enough to ensure survival. Moreover, the development of new experimental techniques to monitor and manipulate single biological molecules has provided a natural testbed for theoretical investigations of nonequilibrium dynamics. This work focuses on developing paradigms for both understanding the principles of nonequilibrium dynamics and also for controlling such systems in the presence of thermal fluctuations. Throughout this work, I rely on a perspective based on two central ideas in nonequilibrium statistical mechanics: large deviation theory, which provides a formalism akin to thermodynamics for nonequilibrium systems, and the fluctuation theorems which identify time symmetry breaking with entropy production. I use the tools of large deviation theory to explore concepts like efficiency and optimal coarse-graining in microscopic dynamical systems. The results point to the extreme importance of rare events in nonequilibrium dynamics. In the context of rare dynamical events, I outline a formal approach to predict efficient control protocols for nonequilibrium systems and develop computational tools to solve the resulting high dimensional optimization problems. The final chapters of this work focus on applications to self-assembly dynamics. I show that the yield of desired structures can be enhanced by driving a system away from equilibrium, using analysis inspired by the theory of the hydrophobic effect. Finally, I demonstrate that nanoscale, protein shells can be modeled and controlled to robustly produce monodisperse, nonequilibrium structures strikingly similar to the
Hooper, Richard
2009-12-01
It is often repeated that a low P-value provides more persuasive evidence for a genuine effect if the power of the test is high. However, this is based on an argument which ignores the precise P-value in favor of simply observing whether P is less than some cut-off, and which oversimplifies the possible effect sizes. In a non-Bayesian framework, there are good reasons to think that power does not affect the evidence of a given P-value. Here I illustrate the relationship between pre-study power and the Bayesian interpretation of a P-value in realistic situations. A Bayesian calculation, using a conventional prior distribution for the effect size and a normal approximation to the sampling distribution of the sample estimate, where the datum is the precise P-value. Over the range of pre-study powers typical in published research, the Bayesian interpretation of a given P-value varies little with power. A Bayesian analysis with reasonable assumptions produces results remarkably in line with a more simple, non-Bayesian intuition-that the evidence against the null hypothesis provided by a precise P-value should not depend on power.
NASA Astrophysics Data System (ADS)
Wallace, D. J.; Rosenheim, B. E.; Roberts, M. L.; Burton, J. R.; Donnelly, J. P.; Woodruff, J. D.
2014-12-01
Is a small quantity of high-precision ages more robust than a higher quantity of lower-precision ages for sediment core chronologies? AMS Radiocarbon ages have been available to researchers for several decades now, and precision of the technique has continued to improve. Analysis and time cost is high, though, and projects are often limited in terms of the number of dates that can be used to develop a chronology. The Gas Ion Source at the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS), while providing lower-precision (uncertainty of order 100 14C y for a sample), is significantly less expensive and far less time consuming than conventional age dating and offers the unique opportunity for large amounts of ages. Here we couple two approaches, one analytical and one statistical, to investigate the utility of an age model comprised of these lower-precision ages for paleotempestology. We use a gas ion source interfaced to a gas-bench type device to generate radiocarbon dates approximately every 5 minutes while determining the order of sample analysis using the published Bayesian accumulation histories for deposits (Bacon). During two day-long sessions, several dates were obtained from carbonate shells in living position in a sediment core comprised of sapropel gel from Mangrove Lake, Bermuda. Samples were prepared where large shells were available, and the order of analysis was determined by the depth with the highest uncertainty according to Bacon. We present the results of these analyses as well as a prognosis for a future where such age models can be constructed from many dates that are quickly obtained relative to conventional radiocarbon dates. This technique currently is limited to carbonates, but development of a system for organic material dating is underway. We will demonstrate the extent to which sacrificing some analytical precision in favor of more dates improves age models.
Estoup, Arnaud; Lombaert, Eric; Marin, Jean-Michel; Guillemaud, Thomas; Pudlo, Pierre; Robert, Christian P; Cornuet, Jean-Marie
2012-09-01
Comparison of demo-genetic models using Approximate Bayesian Computation (ABC) is an active research field. Although large numbers of populations and models (i.e. scenarios) can be analysed with ABC using molecular data obtained from various marker types, methodological and computational issues arise when these numbers become too large. Moreover, Robert et al. (Proceedings of the National Academy of Sciences of the United States of America, 2011, 108, 15112) have shown that the conclusions drawn on ABC model comparison cannot be trusted per se and required additional simulation analyses. Monte Carlo inferential techniques to empirically evaluate confidence in scenario choice are very time-consuming, however, when the numbers of summary statistics (Ss) and scenarios are large. We here describe a methodological innovation to process efficient ABC scenario probability computation using linear discriminant analysis (LDA) on Ss before computing logistic regression. We used simulated pseudo-observed data sets (pods) to assess the main features of the method (precision and computation time) in comparison with traditional probability estimation using raw (i.e. not LDA transformed) Ss. We also illustrate the method on real microsatellite data sets produced to make inferences about the invasion routes of the coccinelid Harmonia axyridis. We found that scenario probabilities computed from LDA-transformed and raw Ss were strongly correlated. Type I and II errors were similar for both methods. The faster probability computation that we observed (speed gain around a factor of 100 for LDA-transformed Ss) substantially increases the ability of ABC practitioners to analyse large numbers of pods and hence provides a manageable way to empirically evaluate the power available to discriminate among a large set of complex scenarios. © 2012 Blackwell Publishing Ltd.
NASA Astrophysics Data System (ADS)
Norris, P. M.; da Silva, A. M., Jr.
2016-12-01
Norris and da Silva recently published a method to constrain a statistical model of sub-gridcolumn moisture variability using high-resolution satellite cloud data. The method can be used for large-scale model parameter estimation or cloud data assimilation (CDA). The gridcolumn model includes assumed-PDF intra-layer horizontal variability and a copula-based inter-layer correlation model. The observables used are MODIS cloud-top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient-based and allows jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. The new approach not only significantly reduces mean and standard deviation biases with respect to the assimilated observables, but also improves the simulated rotational-Ramman scattering cloud optical centroid pressure against independent (non-assimilated) retrievals from the OMI instrument. One obvious difficulty for the method, and other CDA methods, is the lack of information content in passive cloud observables on cloud vertical structure, beyond cloud-top and thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard is helpful, better honoring inversion structures in the background state.
High-Level Fusion using Bayesian Networks: Applications in Command and Control
2006-12-01
prior knowledge into the process. In recent years Bayesian probability theory, which provides a consistent framework for reasoning with uncertain...incorporated into the decision process. Bayesian probability theory provides a consistent mathematical framework for representing and manipulating...assessment process. Bayesian networks provide a computationally tractable method of implementing Bayesian probability theory [5, 6]. In recent years, they
NASA Astrophysics Data System (ADS)
Wahl, E. R.
2008-12-01
A strict process model for pollen as a climate proxy is currently not approachable beyond localized spatial scales; more generally, the canonical model for vegetation-pollen registration itself requires assimilation of empirically-derived information. In this paper, a taxonomically "reduced-space" climate-pollen forward model is developed, based on the performance of a parallel inverse model. The goal is inclusion of the forward model in a Bayesian climate reconstruction framework, following a 4-step process. (1) Ratios of pollen types calibrated to temperature are examined to determine if they can equal or surpass the skill of multi-taxonomic calibrations using the modern analog technique (MAT) optimized with receiver operating characteristic (ROC) analysis. The first phase of this examination, using modern pollen data from SW N America, demonstrates that the ratio method can give calibrations as skillful as the MAT when vegetation representation (and associated climate gradients) are characterized by two dominant pollen taxa, in this case pine and oak. Paleotemperature reconstructions using the ratio method also compare well to MAT reconstructions, showing very minor differences. [Ratio values are defined as pine/(pine + oak), so they vary between 0 and 1.] (2) Uncertainty analysis is carried out in independent steps, which are combined to give overall probabilistic confidence ranges. Monte Carlo (MC) analysis utilizing Poisson distributions to model the inherent variability of pollen representation in relation to climate (assuming defined temperature normals at the modern calibration sites) allows independent statistical estimation of this component of uncertainty, for both the modern calibration and fossil pollen data sets. In turn, MC analysis utilizing normal distributions allows independent estimation of the addition to overall uncertainty from climate variation itself. (3) Because the quality tests in (1) indicate the ratio method has the capacity to carry
Planetary micro-rover operations on Mars using a Bayesian framework for inference and control
NASA Astrophysics Data System (ADS)
Post, Mark A.; Li, Junquan; Quine, Brendan M.
2016-03-01
With the recent progress toward the application of commercially-available hardware to small-scale space missions, it is now becoming feasible for groups of small, efficient robots based on low-power embedded hardware to perform simple tasks on other planets in the place of large-scale, heavy and expensive robots. In this paper, we describe design and programming of the Beaver micro-rover developed for Northern Light, a Canadian initiative to send a small lander and rover to Mars to study the Martian surface and subsurface. For a small, hardware-limited rover to handle an uncertain and mostly unknown environment without constant management by human operators, we use a Bayesian network of discrete random variables as an abstraction of expert knowledge about the rover and its environment, and inference operations for control. A framework for efficient construction and inference into a Bayesian network using only the C language and fixed-point mathematics on embedded hardware has been developed for the Beaver to make intelligent decisions with minimal sensor data. We study the performance of the Beaver as it probabilistically maps a simple outdoor environment with sensor models that include uncertainty. Results indicate that the Beaver and other small and simple robotic platforms can make use of a Bayesian network to make intelligent decisions in uncertain planetary environments.
Paddock, Susan M
2014-06-01
Examine how widely used statistical benchmarks of health care provider performance compare with histogram-based statistical benchmarks obtained via hierarchical Bayesian modeling. Publicly available data from 3,240 hospitals during April 2009-March 2010 on two process-of-care measures reported on the Medicare Hospital Compare website. Secondary data analyses of two process-of-care measures comparing statistical benchmark estimates and threshold exceedance determinations under various combinations of hospital performance measure estimates and benchmarking approaches. Statistical benchmarking approaches for determining top 10 percent performance varied with respect to which hospitals exceeded the performance benchmark; such differences were not found at the 50 percent threshold. Benchmarks derived from the histogram of provider performance under hierarchical Bayesian modeling provide a compromise between benchmarks based on direct (raw) estimates, which are overdispersed relative to the true distribution of provider performance and prone to high variance for small providers, and posterior mean provider performance, for which over-shrinkage and under-dispersion relative to the true provider performance distribution is a concern. Given the rewards and penalties associated with characterizing top performance, the ability of statistical benchmarks to summarize key features of the provider performance distribution should be examined. © Published 2014. This article is a U.S. Government work and is in the public domain in the U.S.A.
Paddock, Susan M
2014-01-01
Objective Examine how widely used statistical benchmarks of health care provider performance compare with histogram-based statistical benchmarks obtained via hierarchical Bayesian modeling. Data Sources Publicly available data from 3,240 hospitals during April 2009–March 2010 on two process-of-care measures reported on the Medicare Hospital Compare website. Study Design Secondary data analyses of two process-of-care measures comparing statistical benchmark estimates and threshold exceedance determinations under various combinations of hospital performance measure estimates and benchmarking approaches. Principal Findings Statistical benchmarking approaches for determining top 10 percent performance varied with respect to which hospitals exceeded the performance benchmark; such differences were not found at the 50 percent threshold. Benchmarks derived from the histogram of provider performance under hierarchical Bayesian modeling provide a compromise between benchmarks based on direct (raw) estimates, which are overdispersed relative to the true distribution of provider performance and prone to high variance for small providers, and posterior mean provider performance, for which over-shrinkage and under-dispersion relative to the true provider performance distribution is a concern. Conclusions Given the rewards and penalties associated with characterizing top performance, the ability of statistical benchmarks to summarize key features of the provider performance distribution should be examined. PMID:24461071
Statistical process control for radiotherapy quality assurance.
Pawlicki, Todd; Whitaker, Matthew; Boyer, Arthur L
2005-09-01
Every quality assurance process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, statistical process control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using process behavior charts were able to identify systematic changes in our daily quality assurance process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the process. The process behavior thresholds calculated from a subset of the data detected a 2% change in the process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the process.
Baccini, Michela; Mattei, Alessandra; Mealli, Fabrizia
2017-03-15
We conduct principal stratification and mediation analysis to investigate to what extent the positive overall effect of treatment on postoperative pain control is mediated by postoperative self administration of intra-venous analgesia by patients in a prospective, randomized, double-blind study. Using the Bayesian approach for inference, we estimate both associative and dissociative principal strata effects arising in principal stratification, as well as natural effects from mediation analysis. We highlight that principal stratification and mediation analysis focus on different causal estimands, answer different causal questions, and involve different sets of structural assumptions.
Whitehead, John; Cleary, Faye; Turner, Amanda
2015-05-30
In this paper, a Bayesian approach is developed for simultaneously comparing multiple experimental treatments with a common control treatment in an exploratory clinical trial. The sample size is set to ensure that, at the end of the study, there will be at least one treatment for which the investigators have a strong belief that it is better than control, or else they have a strong belief that none of the experimental treatments are substantially better than control. This criterion bears a direct relationship with conventional frequentist power requirements, while allowing prior opinion to feature in the analysis with a consequent reduction in sample size. If it is concluded that at least one of the experimental treatments shows promise, then it is envisaged that one or more of these promising treatments will be developed further in a definitive phase III trial. The approach is developed in the context of normally distributed responses sharing a common standard deviation regardless of treatment. To begin with, the standard deviation will be assumed known when the sample size is calculated. The final analysis will not rely upon this assumption, although the intended properties of the design may not be achieved if the anticipated standard deviation turns out to be inappropriate. Methods that formally allow for uncertainty about the standard deviation, expressed in the form of a Bayesian prior, are then explored. Illustrations of the sample sizes computed from the new method are presented, and comparisons are made with frequentist methods devised for the same situation.
Wright, David K.; MacEachern, Scott; Lee, Jaeyong
2014-01-01
The locations of diy-geδ-bay (DGB) sites in the Mandara Mountains, northern Cameroon are hypothesized to occur as a function of their ability to see and be seen from points on the surrounding landscape. A series of geostatistical, two-way and Bayesian logistic regression analyses were performed to test two hypotheses related to the intervisibility of the sites to one another and their visual prominence on the landscape. We determine that the intervisibility of the sites to one another is highly statistically significant when compared to 10 stratified-random permutations of DGB sites. Bayesian logistic regression additionally demonstrates that the visibility of the sites to points on the surrounding landscape is statistically significant. The location of sites appears to have also been selected on the basis of lower slope than random permutations of sites. Using statistical measures, many of which are not commonly employed in archaeological research, to evaluate aspects of visibility on the landscape, we conclude that the placement of DGB sites improved their conspicuousness for enhanced ritual, social cooperation and/or competition purposes. PMID:25383883
Applying Statistical Process Quality Control Methodology to Educational Settings.
ERIC Educational Resources Information Center
Blumberg, Carol Joyce
A subset of Statistical Process Control (SPC) methodology known as Control Charting is introduced. SPC methodology is a collection of graphical and inferential statistics techniques used to study the progress of phenomena over time. The types of control charts covered are the null X (mean), R (Range), X (individual observations), MR (moving…
NASA Astrophysics Data System (ADS)
Gehrmann, Romina A. S.; Schwalenberg, Katrin; Riedel, Michael; Spence, George D.; Spieß, Volkhard; Dosso, Stan E.
2016-01-01
This paper applies nonlinear Bayesian inversion to marine controlled source electromagnetic (CSEM) data collected near two sites of the Integrated Ocean Drilling Program (IODP) Expedition 311 on the northern Cascadia Margin to investigate subseafloor resistivity structure related to gas hydrate deposits and cold vents. The Cascadia margin, off the west coast of Vancouver Island, Canada, has a large accretionary prism where sediments are under pressure due to convergent plate boundary tectonics. Gas hydrate deposits and cold vent structures have previously been investigated by various geophysical methods and seabed drilling. Here, we invert time-domain CSEM data collected at Sites U1328 and U1329 of IODP Expedition 311 using Bayesian methods to derive subsurface resistivity model parameters and uncertainties. The Bayesian information criterion is applied to determine the amount of structure (number of layers in a depth-dependent model) that can be resolved by the data. The parameter space is sampled with the Metropolis-Hastings algorithm in principal-component space, utilizing parallel tempering to ensure wider and efficient sampling and convergence. Nonlinear inversion allows analysis of uncertain acquisition parameters such as time delays between receiver and transmitter clocks as well as input electrical current amplitude. Marginalizing over these instrument parameters in the inversion accounts for their contribution to the geophysical model uncertainties. One-dimensional inversion of time-domain CSEM data collected at measurement sites along a survey line allows interpretation of the subsurface resistivity structure. The data sets can be generally explained by models with 1 to 3 layers. Inversion results at U1329, at the landward edge of the gas hydrate stability zone, indicate a sediment unconformity as well as potential cold vents which were previously unknown. The resistivities generally increase upslope due to sediment erosion along the slope. Inversion
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.
Artificial Intelligence Approach to Support Statistical Quality Control Teaching
ERIC Educational Resources Information Center
Reis, Marcelo Menezes; Paladini, Edson Pacheco; Khator, Suresh; Sommer, Willy Arno
2006-01-01
Statistical quality control--SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and…
Using Statistical Process Control to Enhance Student Progression
ERIC Educational Resources Information Center
Hanna, Mark D.; Raichura, Nilesh; Bernardes, Ednilson
2012-01-01
Public interest in educational outcomes has markedly increased in the most recent decade; however, quality management and statistical process control have not deeply penetrated the management of academic institutions. This paper presents results of an attempt to use Statistical Process Control (SPC) to identify a key impediment to continuous…
Artificial Intelligence Approach to Support Statistical Quality Control Teaching
ERIC Educational Resources Information Center
Reis, Marcelo Menezes; Paladini, Edson Pacheco; Khator, Suresh; Sommer, Willy Arno
2006-01-01
Statistical quality control--SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and…
Darlington, Timothy R; Tokiyama, Stefanie; Lisberger, Stephen G
2017-08-01
Bayesian inference provides a cogent account of how the brain combines sensory information with "priors" based on past experience to guide many behaviors, including smooth pursuit eye movements. We now demonstrate very rapid adaptation of the pursuit system's priors for target direction and speed. We go on to leverage that adaptation to outline possible neural mechanisms that could cause pursuit to show features consistent with Bayesian inference. Adaptation of the prior causes changes in the eye speed and direction at the initiation of pursuit. The adaptation appears after a single trial and accumulates over repeated exposure to a given history of target speeds and directions. The influence of the priors depends on the reliability of visual motion signals: priors are more effective against the visual motion signals provided by low-contrast vs. high-contrast targets. Adaptation of the direction prior generalizes to eye speed and vice versa, suggesting that both priors could be controlled by a single neural mechanism. We conclude that the pursuit system can learn the statistics of visual motion rapidly and use those statistics to guide future behavior. Furthermore, a model that adjusts the gain of visual-motor transmission predicts the effects of recent experience on pursuit direction and speed, as well as the specifics of the generalization between the priors for speed and direction. We suggest that Bayesian inference in pursuit behavior is implemented by distinctly non-Bayesian internal mechanisms that use the smooth eye movement region of the frontal eye fields to control of the gain of visual-motor transmission.NEW & NOTEWORTHY Bayesian inference can account for the interaction between sensory data and past experience in many behaviors. Here, we show, using smooth pursuit eye movements, that the priors based on past experience can be adapted over a very short time frame. We also show that a single model based on direction-specific adaptation of the strength of
Swartz, Michael D; Thomas, Duncan C; Daw, E Warwick; Albers, Kees; Charlesworth, Jac C; Dyer, Thomas C; Fridley, Brooke L; Govil, Manika; Kraft, Peter; Kwon, Soonil; Logue, Mark W; Oh, Cheongeun; Pique-Regi, Roger; Saba, Laura; Schumacher, Fredrick R; Uh, Hae-Won
2007-01-01
The research presented in group 11 of the Genetic Analysis Workshop 15 (GAW15) falls into two major themes: Model selection approaches for gene mapping (both Bayesian and Frequentist); and other Bayesian methods. These methods either allow relaxation of some of the common assumptions, such as mode of inheritance, for studying complicated genetic systems, or allow incorporation of additional information into the model. Over half of the groups applied model selection methods on all three data sets, using models in which genetic markers were used as predictors for linkage, phenotype expression, or transmission to an affected offspring. Most groups employed variations of Stochastic Search Variable Selection as the model selection method of choice. A brief review of this class of methods is given in this summary paper, followed by highlights of other methods and overall summaries of each contribution to the GAW15 presentation group 11. These group contributions exhibit the value of framing genetic problems in terms of model selection, and highlight the impact of variable selection for gene mapping. (c) 2007 Wiley-Liss, Inc.
Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients
Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo
2016-01-01
In this work we present our efforts in building a model able to forecast patients’ changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling. PMID:28269842
Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.
Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo
2016-01-01
In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.
Holt, J; Leach, A W; Johnson, S; Tu, D M; Nhu, D T; Anh, N T; Quinlan, M M; Whittle, P J L; Mengersen, K; Mumford, J D
2017-07-13
The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst-case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain. © 2017 Society for Risk Analysis.
Yang, Yuqing; Chen, Ning; Chen, Ting
2017-01-25
The inference of associations between environmental factors and microbes and among microbes is critical to interpreting metagenomic data, but compositional bias, indirect associations resulting from common factors, and variance within metagenomic sequencing data limit the discovery of associations. To account for these problems, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints, to estimate absolute microbial abundance and simultaneously infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors. We empirically show the effectiveness of the mLDM model using synthetic data, data from the TARA Oceans project, and a colorectal cancer dataset. Finally, we apply mLDM to 16S sequencing data from the western English Channel and report several associations. Our model can be used on both natural environmental and human metagenomic datasets, promoting the understanding of associations in the microbial community.
NASA Astrophysics Data System (ADS)
Blanc, Guillermo A.; Kewley, Lisa; Vogt, Frédéric P. A.; Dopita, Michael A.
2015-01-01
We present a new method for inferring the metallicity (Z) and ionization parameter (q) of H II regions and star-forming galaxies using strong nebular emission lines (SELs). We use Bayesian inference to derive the joint and marginalized posterior probability density functions for Z and q given a set of observed line fluxes and an input photoionization model. Our approach allows the use of arbitrary sets of SELs and the inclusion of flux upper limits. The method provides a self-consistent way of determining the physical conditions of ionized nebulae that is not tied to the arbitrary choice of a particular SEL diagnostic and uses all the available information. Unlike theoretically calibrated SEL diagnostics, the method is flexible and not tied to a particular photoionization model. We describe our algorithm, validate it against other methods, and present a tool that implements it called IZI. Using a sample of nearby extragalactic H II regions, we assess the performance of commonly used SEL abundance diagnostics. We also use a sample of 22 local H II regions having both direct and recombination line (RL) oxygen abundance measurements in the literature to study discrepancies in the abundance scale between different methods. We find that oxygen abundances derived through Bayesian inference using currently available photoionization models in the literature can be in good (~30%) agreement with RL abundances, although some models perform significantly better than others. We also confirm that abundances measured using the direct method are typically ~0.2 dex lower than both RL and photoionization-model-based abundances.
Blanc, Guillermo A.; Kewley, Lisa; Vogt, Frédéric P. A.; Dopita, Michael A.
2015-01-10
We present a new method for inferring the metallicity (Z) and ionization parameter (q) of H II regions and star-forming galaxies using strong nebular emission lines (SELs). We use Bayesian inference to derive the joint and marginalized posterior probability density functions for Z and q given a set of observed line fluxes and an input photoionization model. Our approach allows the use of arbitrary sets of SELs and the inclusion of flux upper limits. The method provides a self-consistent way of determining the physical conditions of ionized nebulae that is not tied to the arbitrary choice of a particular SEL diagnostic and uses all the available information. Unlike theoretically calibrated SEL diagnostics, the method is flexible and not tied to a particular photoionization model. We describe our algorithm, validate it against other methods, and present a tool that implements it called IZI. Using a sample of nearby extragalactic H II regions, we assess the performance of commonly used SEL abundance diagnostics. We also use a sample of 22 local H II regions having both direct and recombination line (RL) oxygen abundance measurements in the literature to study discrepancies in the abundance scale between different methods. We find that oxygen abundances derived through Bayesian inference using currently available photoionization models in the literature can be in good (∼30%) agreement with RL abundances, although some models perform significantly better than others. We also confirm that abundances measured using the direct method are typically ∼0.2 dex lower than both RL and photoionization-model-based abundances.
Jin, Qian; He, Li-Jun; Zhang, Ai-Bing
2012-01-01
In the recent worldwide campaign for the global biodiversity inventory via DNA barcoding, a simple and easily used measure of confidence for assigning sequences to species in DNA barcoding has not been established so far, although the likelihood ratio test and the bayesian approach had been proposed to address this issue from a statistical point of view. The TDR (Two Dimensional non-parametric Resampling) measure newly proposed in this study offers users a simple and easy approach to evaluate the confidence of species membership in DNA barcoding projects. We assessed the validity and robustness of the TDR approach using datasets simulated under coalescent models, and an empirical dataset, and found that TDR measure is very robust in assessing species membership of DNA barcoding. In contrast to the likelihood ratio test and bayesian approach, the TDR method stands out due to simplicity in both concepts and calculations, with little in the way of restrictive population genetic assumptions. To implement this approach we have developed a computer program package (TDR1.0beta) freely available from ftp://202.204.209.200/education/video/TDR1.0beta.rar.
Vlad, Marcel Ovidiu; Tsuchiya, Masa; Oefner, Peter; Ross, John
2002-01-01
We investigate the statistical properties of systems with random chemical composition and try to obtain a theoretical derivation of the self-similar Dirichlet distribution, which is used empirically in molecular biology, environmental chemistry, and geochemistry. We consider a system made up of many chemical species and assume that the statistical distribution of the abundance of each chemical species in the system is the result of a succession of a variable number of random dilution events, which can be described by using the renormalization-group theory. A Bayesian approach is used for evaluating the probability density of the chemical composition of the system in terms of the probability densities of the abundances of the different chemical species. We show that for large cascades of dilution events, the probability density of the composition vector of the system is given by a self-similar probability density of the Dirichlet type. We also give an alternative formal derivation for the Dirichlet law based on the maximum entropy approach, by assuming that the average values of the chemical potentials of different species, expressed in terms of molar fractions, are constant. Although the maximum entropy approach leads formally to the Dirichlet distribution, it does not clarify the physical origin of the Dirichlet statistics and has serious limitations. The random theory of dilution provides a physical picture for the emergence of Dirichlet statistics and makes it possible to investigate its validity range. We discuss the implications of our theory in molecular biology, geochemistry, and environmental science.
Modular autopilot design and development featuring Bayesian non-parametric adaptive control
NASA Astrophysics Data System (ADS)
Stockton, Jacob
Over the last few decades, Unmanned Aircraft Systems, or UAS, have become a critical part of the defense of our nation and the growth of the aerospace sector. UAS have a great potential for the agricultural industry, first response, and ecological monitoring. However, the wide range of applications require many mission-specific vehicle platforms. These platforms must operate reliably in a range of environments, and in presence of significant uncertainties. The accepted practice for enabling autonomously flying UAS today relies on extensive manual tuning of the UAS autopilot parameters, or time consuming approximate modeling of the dynamics of the UAS. These methods may lead to overly conservative controllers or excessive development times. A comprehensive approach to the development of an adaptive, airframe-independent controller is presented. The control algorithm leverages a nonparametric, Bayesian approach to adaptation, and is used as a cornerstone for the development of a new modular autopilot. Promising simulation results are presented for the adaptive controller, as well as, flight test results for the modular autopilot.
Improving statistical analysis of matched case-control studies.
Conway, Aaron; Rolley, John X; Fulbrook, Paul; Page, Karen; Thompson, David R
2013-06-01
Matched case-control research designs can be useful because matching can increase power due to reduced variability between subjects. However, inappropriate statistical analysis of matched data could result in a change in the strength of association between the dependent and independent variables or a change in the significance of the findings. We sought to ascertain whether matched case-control studies published in the nursing literature utilized appropriate statistical analyses. Of 41 articles identified that met the inclusion criteria, 31 (76%) used an inappropriate statistical test for comparing data derived from case subjects and their matched controls. In response to this finding, we developed an algorithm to support decision-making regarding statistical tests for matched case-control studies.
NASA Technical Reports Server (NTRS)
Vangelder, B. H. W.
1978-01-01
Non-Bayesian statistics were used in simulation studies centered around laser range observations to LAGEOS. The capabilities of satellite laser ranging especially in connection with relative station positioning are evaluated. The satellite measurement system under investigation may fall short in precise determinations of the earth's orientation (precession and nutation) and earth's rotation as opposed to systems as very long baseline interferometry (VLBI) and lunar laser ranging (LLR). Relative station positioning, determination of (differential) polar motion, positioning of stations with respect to the earth's center of mass and determination of the earth's gravity field should be easily realized by satellite laser ranging (SLR). The last two features should be considered as best (or solely) determinable by SLR in contrast to VLBI and LLR.
Call Admission Control Scheme Based on Statistical Information
NASA Astrophysics Data System (ADS)
Fujiwara, Takayuki; Oki, Eiji; Shiomoto, Kohei
A call admission control (CAC) scheme based on statistical information is proposed, called the statistical CAC scheme. A conventional scheme needs to manage session information for each link to update the residual bandwidth of a network in real time. This scheme has a scalability problem in terms of network size. The statistical CAC rejects session setup requests in accordance to a pre-computed ratio, called the rejection ratio. The rejection ratio is computed by using statistical information about the bandwidth requested for each link so that the congestion probability is less than an upper bound specified by a network operator. The statistical CAC is more scalable in terms of network size than the conventional scheme because it does not need to keep accommodated session state information. Numerical results show that the statistical CAC, even without exact session state information, only slightly degrades network utilization compared with the conventional scheme.
Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy
Tang, Wanwan; Wu, Xuebing; Jiang, Rui; Li, Yanda
2009-01-01
The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease. PMID:19412524
NASA Astrophysics Data System (ADS)
Olugboji, T. M.; Lekic, V.; McDonough, W.
2017-07-01
We present a new approach for evaluating existing crustal models using ambient noise data sets and its associated uncertainties. We use a transdimensional hierarchical Bayesian inversion approach to invert ambient noise surface wave phase dispersion maps for Love and Rayleigh waves using measurements obtained from Ekström (2014). Spatiospectral analysis shows that our results are comparable to a linear least squares inverse approach (except at higher harmonic degrees), but the procedure has additional advantages: (1) it yields an autoadaptive parameterization that follows Earth structure without making restricting assumptions on model resolution (regularization or damping) and data errors; (2) it can recover non-Gaussian phase velocity probability distributions while quantifying the sources of uncertainties in the data measurements and modeling procedure; and (3) it enables statistical assessments of different crustal models (e.g., CRUST1.0, LITHO1.0, and NACr14) using variable resolution residual and standard deviation maps estimated from the ensemble. These assessments show that in the stable old crust of the Archean, the misfits are statistically negligible, requiring no significant update to crustal models from the ambient noise data set. In other regions of the U.S., significant updates to regionalization and crustal structure are expected especially in the shallow sedimentary basins and the tectonically active regions, where the differences between model predictions and data are statistically significant.
Bayesian sample size determination for case-control studies when exposure may be misclassified.
Joseph, Lawrence; Bélisle, Patrick
2013-12-01
Odds ratios are frequently used for estimating the effect of an exposure on the probability of disease in case-control studies. In planning such studies, methods for sample size determination are required to ensure sufficient accuracy in estimating odds ratios once the data are collected. Often, the exposure used in epidemiologic studies is not perfectly ascertained. This can arise from recall bias, the use of a proxy exposure measurement, uncertain work exposure history, and laboratory or other errors. The resulting misclassification can have large impacts on the accuracy and precision of estimators, and specialized estimation techniques have been developed to adjust for these biases. However, much less work has been done to account for the anticipated decrease in the precision of estimators at the design stage. Here, we develop methods for sample size determination for odds ratios in the presence of exposure misclassification by using several interval-based Bayesian criteria. By using a series of prototypical examples, we compare sample size requirements after adjustment for misclassification with those required when this problem is ignored. We illustrate the methods by planning a case-control study of the effect of late introduction of peanut to the diet of children to the subsequent development of peanut allergy.
Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation
NASA Technical Reports Server (NTRS)
He, Yuning; Lee, Herbert K. H.; Davies, Misty D.
2012-01-01
Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.
Manufacturing Squares: An Integrative Statistical Process Control Exercise
ERIC Educational Resources Information Center
Coy, Steven P.
2016-01-01
In the exercise, students in a junior-level operations management class are asked to manufacture a simple product. Given product specifications, they must design a production process, create roles and design jobs for each team member, and develop a statistical process control plan that efficiently and effectively controls quality during…
Manufacturing Squares: An Integrative Statistical Process Control Exercise
ERIC Educational Resources Information Center
Coy, Steven P.
2016-01-01
In the exercise, students in a junior-level operations management class are asked to manufacture a simple product. Given product specifications, they must design a production process, create roles and design jobs for each team member, and develop a statistical process control plan that efficiently and effectively controls quality during…
Using Paper Helicopters to Teach Statistical Process Control
ERIC Educational Resources Information Center
Johnson, Danny J.
2011-01-01
This hands-on project uses a paper helicopter to teach students how to distinguish between common and special causes of variability when developing and using statistical process control charts. It allows the student to experience a process that is out-of-control due to imprecise or incomplete product design specifications and to discover how the…
Using Paper Helicopters to Teach Statistical Process Control
ERIC Educational Resources Information Center
Johnson, Danny J.
2011-01-01
This hands-on project uses a paper helicopter to teach students how to distinguish between common and special causes of variability when developing and using statistical process control charts. It allows the student to experience a process that is out-of-control due to imprecise or incomplete product design specifications and to discover how the…
Statistical Design Model (SDM) of satellite thermal control subsystem
NASA Astrophysics Data System (ADS)
Mirshams, Mehran; Zabihian, Ehsan; Aarabi Chamalishahi, Mahdi
2016-07-01
Satellites thermal control, is a satellite subsystem that its main task is keeping the satellite components at its own survival and activity temperatures. Ability of satellite thermal control plays a key role in satisfying satellite's operational requirements and designing this subsystem is a part of satellite design. In the other hand due to the lack of information provided by companies and designers still doesn't have a specific design process while it is one of the fundamental subsystems. The aim of this paper, is to identify and extract statistical design models of spacecraft thermal control subsystem by using SDM design method. This method analyses statistical data with a particular procedure. To implement SDM method, a complete database is required. Therefore, we first collect spacecraft data and create a database, and then we extract statistical graphs using Microsoft Excel, from which we further extract mathematical models. Inputs parameters of the method are mass, mission, and life time of the satellite. For this purpose at first thermal control subsystem has been introduced and hardware using in the this subsystem and its variants has been investigated. In the next part different statistical models has been mentioned and a brief compare will be between them. Finally, this paper particular statistical model is extracted from collected statistical data. Process of testing the accuracy and verifying the method use a case study. Which by the comparisons between the specifications of thermal control subsystem of a fabricated satellite and the analyses results, the methodology in this paper was proved to be effective. Key Words: Thermal control subsystem design, Statistical design model (SDM), Satellite conceptual design, Thermal hardware
Statistical Process Control Charts for Public Health Monitoring
2014-12-01
process performance, remove existing sources of natural and unnatural variability, and identify any new sources of variability [1]. Control charts are SPC...can be used and refined over time [4]. The causes of any Phase I points outside the established control limits should be investigated. If the cause is...U.S. Army Public Health Command Statistical Process Control Charts for Public Health Monitoring PHR No. S.0023112 General Medical: 500A, Public
Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers
Throckmorton, Chandra S.; Colwell, Kenneth A.; Ryan, David B.; Sellers, Eric W.; Collins, Leslie M.
2013-01-01
P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate. PMID:23529202
Bauer, Robert; Gharabaghi, Alireza
2015-01-01
Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.
Mak, Timothy Shin Heng; Best, Nicky; Rushton, Lesley
2015-05-01
Exposure misclassification in case-control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative "robust Bayesian" approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.
No Control Genes Required: Bayesian Analysis of qRT-PCR Data
Matz, Mikhail V.; Wright, Rachel M.; Scott, James G.
2013-01-01
Background Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. Results In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Conclusions Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC
Real-time statistical quality control and ARM
Blough, D.K.
1992-05-01
An important component of the Atmospheric Radiation Measurement (ARM) Program is real-time quality control of data obtained from meteorological instruments. It is the goal of the ARM program to enhance the predictive capabilities of global circulation models by incorporating in them more detailed information on the radiative characteristics of the earth`s atmosphere. To this end, a number of Cloud and Radiation Testbeds (CART`s) will be built at various locations worldwide. Each CART will consist of an array of instruments designed to collect radiative data. The large amount of data obtained from these instruments necessitates real-time processing in order to flag outliers and possible instrument malfunction. The Bayesian dynamic linear model (DLM) proves to be an effective way of monitoring the time series data which each instrument generates. It provides a flexible yet powerful approach to detecting in real-time sudden shifts in a non-stationary multivariate time series. An application of these techniques to data arising from a remote sensing instrument to be used in the CART is provided. Using real data from a wind profiler, the ability of the DLM to detect outliers is studied. 5 refs.
Real-time statistical quality control and ARM
Blough, D.K.
1992-05-01
An important component of the Atmospheric Radiation Measurement (ARM) Program is real-time quality control of data obtained from meteorological instruments. It is the goal of the ARM program to enhance the predictive capabilities of global circulation models by incorporating in them more detailed information on the radiative characteristics of the earth's atmosphere. To this end, a number of Cloud and Radiation Testbeds (CART's) will be built at various locations worldwide. Each CART will consist of an array of instruments designed to collect radiative data. The large amount of data obtained from these instruments necessitates real-time processing in order to flag outliers and possible instrument malfunction. The Bayesian dynamic linear model (DLM) proves to be an effective way of monitoring the time series data which each instrument generates. It provides a flexible yet powerful approach to detecting in real-time sudden shifts in a non-stationary multivariate time series. An application of these techniques to data arising from a remote sensing instrument to be used in the CART is provided. Using real data from a wind profiler, the ability of the DLM to detect outliers is studied. 5 refs.
Statistical Process Control in the Practice of Program Evaluation.
ERIC Educational Resources Information Center
Posavac, Emil J.
1995-01-01
A technique developed to monitor the quality of manufactured products, statistical process control (SPC), incorporates several features that may prove attractive to evaluators. This paper reviews the history of SPC, suggests how the approach can enrich program evaluation, and illustrates its use in a hospital-based example. (SLD)
Statistical Process Control. Impact and Opportunities for Ohio.
ERIC Educational Resources Information Center
Brown, Harold H.
The first purpose of this study is to help the reader become aware of the evolution of Statistical Process Control (SPC) as it is being implemented and used in industry today. This is approached through the presentation of a brief historical account of SPC, from its inception through the technological miracle that has occurred in Japan. The…
Statistical porcess control in Deep Space Network operation
NASA Technical Reports Server (NTRS)
Hodder, J. A.
2002-01-01
This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).
Statistical Process Control. A Summary. FEU/PICKUP Project Report.
ERIC Educational Resources Information Center
Owen, M.; Clark, I.
A project was conducted to develop a curriculum and training materials to be used in training industrial operatives in statistical process control (SPC) techniques. During the first phase of the project, questionnaires were sent to 685 companies (215 of which responded) to determine where SPC was being used, what type of SPC firms needed, and how…
Statistical porcess control in Deep Space Network operation
NASA Technical Reports Server (NTRS)
Hodder, J. A.
2002-01-01
This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).
Bayesian Brains without Probabilities.
Sanborn, Adam N; Chater, Nick
2016-12-01
Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Yi, Nengjun; Kaklamani, Virginia G; Pasche, Boris
2011-01-01
Complex diseases such as cancers are influenced by interacting networks of genetic and environmental factors. However, a joint analysis of multiple genes and environmental factors is challenging, owing to potentially large numbers of correlated and complex variables. We describe Bayesian generalized linear models for simultaneously analyzing covariates, main effects of numerous loci, gene-gene and gene-environment interactions in population case-control studies. Our Bayesian models use Student-t prior distributions with different shrinkage parameters for different types of effects, allowing reliable estimates of main effects and interactions and hence increasing the power for detection of real signals. We implement a fast and stable algorithm for fitting models by extending available tools for classical generalized linear models to the Bayesian case. We propose a novel method to interpret and visualize models with multiple interactions by computing the average predictive probability. Simulations show that the method has the potential to dissect interacting networks of complex diseases. Application of the method to a large case-control study of adiponectin genes and colorectal cancer risk highlights the previous results and detects new epistatic interactions and sex-specific effects that warrant follow-up in independent studies.
Ensemble of Thermostatically Controlled Loads: Statistical Physics Approach.
Chertkov, Michael; Chernyak, Vladimir
2017-08-17
Thermostatically controlled loads, e.g., air conditioners and heaters, are by far the most widespread consumers of electricity. Normally the devices are calibrated to provide the so-called bang-bang control - changing from on to off, and vice versa, depending on temperature. We considered aggregation of a large group of similar devices into a statistical ensemble, where the devices operate following the same dynamics, subject to stochastic perturbations and randomized, Poisson on/off switching policy. Using theoretical and computational tools of statistical physics, we analyzed how the ensemble relaxes to a stationary distribution and established a relationship between the relaxation and the statistics of the probability flux associated with devices' cycling in the mixed (discrete, switch on/off, and continuous temperature) phase space. This allowed us to derive the spectrum of the non-equilibrium (detailed balance broken) statistical system and uncover how switching policy affects oscillatory trends and the speed of the relaxation. Relaxation of the ensemble is of practical interest because it describes how the ensemble recovers from significant perturbations, e.g., forced temporary switching off aimed at utilizing the flexibility of the ensemble to provide "demand response" services to change consumption temporarily to balance a larger power grid. We discuss how the statistical analysis can guide further development of the emerging demand response technology.
A Statistical Project Control Tool for Engineering Managers
NASA Technical Reports Server (NTRS)
Bauch, Garland T.
2001-01-01
This slide presentation reviews the use of a Statistical Project Control Tool (SPCT) for managing engineering projects. A literature review pointed to a definition of project success, (i.e., A project is successful when the cost, schedule, technical performance, and quality satisfy the customer.) The literature review also pointed to project success factors, and traditional project control tools, and performance measures that are detailed in the report. The essential problem is that with resources becoming more limited, and an increasing number or projects, project failure is increasing, there is a limitation of existing methods and systematic methods are required. The objective of the work is to provide a new statistical project control tool for project managers. Graphs using the SPCT method plotting results of 3 successful projects and 3 failed projects are reviewed, with success and failure being defined by the owner.
NASA Technical Reports Server (NTRS)
Norris, Peter M.; Da Silva, Arlindo M.
2016-01-01
A method is presented to constrain a statistical model of sub-gridcolumn moisture variability using high-resolution satellite cloud data. The method can be used for large-scale model parameter estimation or cloud data assimilation. The gridcolumn model includes assumed probability density function (PDF) intra-layer horizontal variability and a copula-based inter-layer correlation model. The observables used in the current study are Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient-based and allows jumps into regions of non-zero cloud probability. The current study uses a skewed-triangle distribution for layer moisture. The article also includes a discussion of the Metropolis and multiple-try Metropolis versions of MCMC.
Preliminary statistical assessment towards characterization of biobotic control.
Latif, Tahmid; Meng Yang; Lobaton, Edgar; Bozkurt, Alper
2016-08-01
Biobotic research involving neurostimulation of instrumented insects to control their locomotion is finding potential as an alternative solution towards development of centimeter-scale distributed swarm robotics. To improve the reliability of biobotic agents, their control mechanism needs to be precisely characterized. To achieve this goal, this paper presents our initial efforts for statistical assessment of the angular response of roach biobots to the applied bioelectrical stimulus. Subsequent findings can help to understand the effect of each stimulation parameter individually or collectively and eventually reach reliable and consistent biobotic control suitable for real life scenarios.
Archer, S C; Mc Coy, F; Wapenaar, W; Green, M J
2014-01-01
The aim of this research was to determine budgets for specific management interventions to control heifer mastitis in Irish dairy herds as an example of evidence synthesis and 1-step Bayesian micro-simulation in a veterinary context. Budgets were determined for different decision makers based on their willingness to pay. Reducing the prevalence of heifers with a high milk somatic cell count (SCC) early in the first lactation could be achieved through herd level management interventions for pre- and peri-partum heifers, however the cost effectiveness of these interventions is unknown. A synthesis of multiple sources of evidence, accounting for variability and uncertainty in the available data is invaluable to inform decision makers around likely economic outcomes of investing in disease control measures. One analytical approach to this is Bayesian micro-simulation, where the trajectory of different individuals undergoing specific interventions is simulated. The classic micro-simulation framework was extended to encompass synthesis of evidence from 2 separate statistical models and previous research, with the outcome for an individual cow or herd assessed in terms of changes in lifetime milk yield, disposal risk, and likely financial returns conditional on the interventions being simultaneously applied. The 3 interventions tested were storage of bedding inside, decreasing transition yard stocking density, and spreading of bedding evenly in the calving area. Budgets for the interventions were determined based on the minimum expected return on investment, and the probability of the desired outcome. Budgets for interventions to control heifer mastitis were highly dependent on the decision maker's willingness to pay, and hence minimum expected return on investment. Understanding the requirements of decision makers and their rational spending limits would be useful for the development of specific interventions for particular farms to control heifer mastitis, and other
Statistical inference in behavior analysis: Experimental control is better
Perone, Michael
1999-01-01
Statistical inference promises automatic, objective, reliable assessments of data, independent of the skills or biases of the investigator, whereas the single-subject methods favored by behavior analysts often are said to rely too much on the investigator's subjective impressions, particularly in the visual analysis of data. In fact, conventional statistical methods are difficult to apply correctly, even by experts, and the underlying logic of null-hypothesis testing has drawn criticism since its inception. By comparison, single-subject methods foster direct, continuous interaction between investigator and subject and development of strong forms of experimental control that obviate the need for statistical inference. Treatment effects are demonstrated in experimental designs that incorporate replication within and between subjects, and the visual analysis of data is adequate when integrated into such designs. Thus, single-subject methods are ideal for shaping—and maintaining—the kind of experimental practices that will ensure the continued success of behavior analysis. PMID:22478328
The HONEYPOT randomized controlled trial statistical analysis plan.
Pascoe, Elaine Mary; Lo, Serigne; Scaria, Anish; Badve, Sunil V; Beller, Elaine Mary; Cass, Alan; Hawley, Carmel Mary; Johnson, David W
2013-01-01
The HONEYPOT study is a multicenter, open-label, blinded-outcome, randomized controlled trial designed to determine whether, compared with standard topical application of mupirocin for nasal staphylococcal carriage, exit-site application of antibacterial honey reduces the rate of catheter-associated infections in peritoneal dialysis patients. To make public the pre-specified statistical analysis principles to be adhered to and the procedures to be performed by statisticians who will analyze the data for the HONEYPOT trial. Statisticians and clinical investigators who were blinded to treatment allocation and treatment-related study results and who will remain blinded until the central database is locked for final data extraction and analysis determined the statistical methods and procedures to be used for analysis and wrote the statistical analysis plan. The plan describes basic analysis principles, methods for dealing with a range of commonly encountered data analysis issues, and the specific statistical procedures for analyzing the primary, secondary, and safety outcomes. A statistical analysis plan containing the pre-specified principles, methods, and procedures to be adhered to in the analysis of the data from the HONEYPOT trial was developed in accordance with international guidelines. The structure and content of the plan provide sufficient detail to meet the guidelines on statistical principles for clinical trials produced by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. Making public the pre-specified statistical analysis plan for the HONEYPOT trial minimizes the potential for bias in the analysis of trial data and the interpretation and reporting of trial results.
The HONEYPOT Randomized Controlled Trial Statistical Analysis Plan
Pascoe, Elaine Mary; Lo, Serigne; Scaria, Anish; Badve, Sunil V.; Beller, Elaine Mary; Cass, Alan; Hawley, Carmel Mary; Johnson, David W.
2013-01-01
♦ Background: The HONEYPOT study is a multicenter, open-label, blinded-outcome, randomized controlled trial designed to determine whether, compared with standard topical application of mupirocin for nasal staphylococcal carriage, exit-site application of antibacterial honey reduces the rate of catheter-associated infections in peritoneal dialysis patients. ♦ Objective: To make public the pre-specified statistical analysis principles to be adhered to and the procedures to be performed by statisticians who will analyze the data for the HONEYPOT trial. ♦ Methods: Statisticians and clinical investigators who were blinded to treatment allocation and treatment-related study results and who will remain blinded until the central database is locked for final data extraction and analysis determined the statistical methods and procedures to be used for analysis and wrote the statistical analysis plan. The plan describes basic analysis principles, methods for dealing with a range of commonly encountered data analysis issues, and the specific statistical procedures for analyzing the primary, secondary, and safety outcomes. ♦ Results: A statistical analysis plan containing the pre-specified principles, methods, and procedures to be adhered to in the analysis of the data from the HONEYPOT trial was developed in accordance with international guidelines. The structure and content of the plan provide sufficient detail to meet the guidelines on statistical principles for clinical trials produced by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. ♦ Conclusions: Making public the pre-specified statistical analysis plan for the HONEYPOT trial minimizes the potential for bias in the analysis of trial data and the interpretation and reporting of trial results. PMID:23843589
Statistical transformation and the interpretation of inpatient glucose control data.
Saulnier, George E; Castro, Janna C; Cook, Curtiss B
2014-03-01
To introduce a statistical method of assessing hospital-based non-intensive care unit (non-ICU) inpatient glucose control. Point-of-care blood glucose (POC-BG) data from hospital non-ICUs were extracted for January 1 through December 31, 2011. Glucose data distribution was examined before and after Box-Cox transformations and compared to normality. Different subsets of data were used to establish upper and lower control limits, and exponentially weighted moving average (EWMA) control charts were constructed from June, July, and October data as examples to determine if out-of-control events were identified differently in nontransformed versus transformed data. A total of 36,381 POC-BG values were analyzed. In all 3 monthly test samples, glucose distributions in nontransformed data were skewed but approached a normal distribution once transformed. Interpretation of out-of-control events from EWMA control chart analyses also revealed differences. In the June test data, an out-of-control process was identified at sample 53 with nontransformed data, whereas the transformed data remained in control for the duration of the observed period. Analysis of July data demonstrated an out-of-control process sooner in the transformed (sample 55) than nontransformed (sample 111) data, whereas for October, transformed data remained in control longer than nontransformed data. Statistical transformations increase the normal behavior of inpatient non-ICU glycemic data sets. The decision to transform glucose data could influence the interpretation and conclusions about the status of inpatient glycemic control. Further study is required to determine whether transformed versus nontransformed data influence clinical decisions or evaluation of interventions.
Statistical physics of human beings in games: Controlled experiments
NASA Astrophysics Data System (ADS)
Liang, Yuan; Huang, Ji-Ping
2014-07-01
It is important to know whether the laws or phenomena in statistical physics for natural systems with non-adaptive agents still hold for social human systems with adaptive agents, because this implies whether it is possible to study or understand social human systems by using statistical physics originating from natural systems. For this purpose, we review the role of human adaptability in four kinds of specific human behaviors, namely, normal behavior, herd behavior, contrarian behavior, and hedge behavior. The approach is based on controlled experiments in the framework of market-directed resource-allocation games. The role of the controlled experiments could be at least two-fold: adopting the real human decision-making process so that the system under consideration could reflect the performance of genuine human beings; making it possible to obtain macroscopic physical properties of a human system by tuning a particular factor of the system, thus directly revealing cause and effect. As a result, both computer simulations and theoretical analyses help to show a few counterparts of some laws or phenomena in statistical physics for social human systems: two-phase phenomena or phase transitions, entropy-related phenomena, and a non-equilibrium steady state. This review highlights the role of human adaptability in these counterparts, and makes it possible to study or understand some particular social human systems by means of statistical physics coming from natural systems.
Callahan, Charles D; Griffen, David L
2003-08-01
Emergency medicine faces unique challenges in the effort to improve efficiency and effectiveness. Increased patient volumes, decreased emergency department (ED) supply, and an increased emphasis on the ED as a diagnostic center have contributed to poor customer satisfaction and process failures such as diversion/bypass. Statistical process control (SPC) techniques developed in industry offer an empirically based means to understand our work processes and manage by fact. Emphasizing that meaningful quality improvement can occur only when it is exercised by "front-line" providers, this primer presents robust yet accessible SPC concepts and techniques for use in today's ED.
CRN5EXP: Expert system for statistical quality control
NASA Technical Reports Server (NTRS)
Hentea, Mariana
1991-01-01
The purpose of the Expert System CRN5EXP is to assist in checking the quality of the coils at two very important mills: Hot Rolling and Cold Rolling in a steel plant. The system interprets the statistical quality control charts, diagnoses and predicts the quality of the steel. Measurements of process control variables are recorded in a database and sample statistics such as the mean and the range are computed and plotted on a control chart. The chart is analyzed through patterns using the C Language Integrated Production System (CLIPS) and a forward chaining technique to reach a conclusion about the causes of defects and to take management measures for the improvement of the quality control techniques. The Expert System combines the certainty factors associated with the process control variables to predict the quality of the steel. The paper presents the approach to extract data from the database, the reason to combine certainty factors, the architecture and the use of the Expert System. However, the interpretation of control charts patterns requires the human expert's knowledge and lends to Expert Systems rules.
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
A statistical combustion phase control approach of SI engines
NASA Astrophysics Data System (ADS)
Gao, Jinwu; Wu, Yuhu; Shen, Tielong
2017-02-01
In order to maximize the performance of internal combustion engine, combustion phase is usually controlled to track its desired reference. However, suffering from the cyclic variability of combustion, it is difficulty but meaningful to control mean of combustion phase and constrain its variance. As a combustion phase indicator, the location of peak pressure (LPP) is utilized for real-time combustion phase control in this research. The purpose of the proposed method is to ensure the mean of LPP statistically tracks its reference and constrains the standard deviation of LPP distribution. To achieve this, LPP is first calculated based on the cylinder pressure sensor, and its characteristics are analyzed at the steady-state operating condition, then the distribution of LPP is examined online using hypothesis test criterion. On the basis of the presented statistical algorithm, current mean of LPP is applied in the feedback channel for designing spark advance adjustment law, and the stability of closed-loop system is theoretically ensured according to a steady statistical model. Finally, the proposed strategy is verified on a spark ignition gasoline engine.
NASA Technical Reports Server (NTRS)
He, Yuning
2015-01-01
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
Statistical Quality Control of Moisture Data in GEOS DAS
NASA Technical Reports Server (NTRS)
Dee, D. P.; Rukhovets, L.; Todling, R.
1999-01-01
A new statistical quality control algorithm was recently implemented in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The final step in the algorithm consists of an adaptive buddy check that either accepts or rejects outlier observations based on a local statistical analysis of nearby data. A basic assumption in any such test is that the observed field is spatially coherent, in the sense that nearby data can be expected to confirm each other. However, the buddy check resulted in excessive rejection of moisture data, especially during the Northern Hemisphere summer. The analysis moisture variable in GEOS DAS is water vapor mixing ratio. Observational evidence shows that the distribution of mixing ratio errors is far from normal. Furthermore, spatial correlations among mixing ratio errors are highly anisotropic and difficult to identify. Both factors contribute to the poor performance of the statistical quality control algorithm. To alleviate the problem, we applied the buddy check to relative humidity data instead. This variable explicitly depends on temperature and therefore exhibits a much greater spatial coherence. As a result, reject rates of moisture data are much more reasonable and homogeneous in time and space.
Statistical Quality Control of Moisture Data in GEOS DAS
NASA Technical Reports Server (NTRS)
Dee, D. P.; Rukhovets, L.; Todling, R.
1999-01-01
A new statistical quality control algorithm was recently implemented in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The final step in the algorithm consists of an adaptive buddy check that either accepts or rejects outlier observations based on a local statistical analysis of nearby data. A basic assumption in any such test is that the observed field is spatially coherent, in the sense that nearby data can be expected to confirm each other. However, the buddy check resulted in excessive rejection of moisture data, especially during the Northern Hemisphere summer. The analysis moisture variable in GEOS DAS is water vapor mixing ratio. Observational evidence shows that the distribution of mixing ratio errors is far from normal. Furthermore, spatial correlations among mixing ratio errors are highly anisotropic and difficult to identify. Both factors contribute to the poor performance of the statistical quality control algorithm. To alleviate the problem, we applied the buddy check to relative humidity data instead. This variable explicitly depends on temperature and therefore exhibits a much greater spatial coherence. As a result, reject rates of moisture data are much more reasonable and homogeneous in time and space.
Ma, Jinhui; Thabane, Lehana; Kaczorowski, Janusz; Chambers, Larry; Dolovich, Lisa; Karwalajtys, Tina; Levitt, Cheryl
2009-06-16
Cluster randomized trials (CRTs) are increasingly used to assess the effectiveness of interventions to improve health outcomes or prevent diseases. However, the efficiency and consistency of using different analytical methods in the analysis of binary outcome have received little attention. We described and compared various statistical approaches in the analysis of CRTs using the Community Hypertension Assessment Trial (CHAT) as an example. The CHAT study was a cluster randomized controlled trial aimed at investigating the effectiveness of pharmacy-based blood pressure clinics led by peer health educators, with feedback to family physicians (CHAT intervention) against Usual Practice model (Control), on the monitoring and management of BP among older adults. We compared three cluster-level and six individual-level statistical analysis methods in the analysis of binary outcomes from the CHAT study. The three cluster-level analysis methods were: i) un-weighted linear regression, ii) weighted linear regression, and iii) random-effects meta-regression. The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression. We also investigated the robustness of the estimates after the adjustment for the cluster and individual level covariates. Among all the statistical methods assessed, the Bayesian random-effects logistic regression method yielded the widest 95% interval estimate for the odds ratio and consequently led to the most conservative conclusion. However, the results remained robust under all methods - showing sufficient evidence in support of the hypothesis of no effect for the CHAT intervention against Usual Practice control model for management of blood pressure among seniors in primary care. The individual-level standard logistic regression is the
2009-01-01
Background Cluster randomized trials (CRTs) are increasingly used to assess the effectiveness of interventions to improve health outcomes or prevent diseases. However, the efficiency and consistency of using different analytical methods in the analysis of binary outcome have received little attention. We described and compared various statistical approaches in the analysis of CRTs using the Community Hypertension Assessment Trial (CHAT) as an example. The CHAT study was a cluster randomized controlled trial aimed at investigating the effectiveness of pharmacy-based blood pressure clinics led by peer health educators, with feedback to family physicians (CHAT intervention) against Usual Practice model (Control), on the monitoring and management of BP among older adults. Methods We compared three cluster-level and six individual-level statistical analysis methods in the analysis of binary outcomes from the CHAT study. The three cluster-level analysis methods were: i) un-weighted linear regression, ii) weighted linear regression, and iii) random-effects meta-regression. The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression. We also investigated the robustness of the estimates after the adjustment for the cluster and individual level covariates. Results Among all the statistical methods assessed, the Bayesian random-effects logistic regression method yielded the widest 95% interval estimate for the odds ratio and consequently led to the most conservative conclusion. However, the results remained robust under all methods – showing sufficient evidence in support of the hypothesis of no effect for the CHAT intervention against Usual Practice control model for management of blood pressure among seniors in primary care. The individual-level standard
NASA Technical Reports Server (NTRS)
da Silva, Arlindo M.; Norris, Peter M.
2013-01-01
Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.
NASA Technical Reports Server (NTRS)
Norris, Peter M.; da Silva, Arlindo M.
2016-01-01
Part 1 of this series presented a Monte Carlo Bayesian method for constraining a complex statistical model of global circulation model (GCM) sub-gridcolumn moisture variability using high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data, thereby permitting parameter estimation and cloud data assimilation for large-scale models. This article performs some basic testing of this new approach, verifying that it does indeed reduce mean and standard deviation biases significantly with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud-top pressure and that it also improves the simulated rotational-Raman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the Ozone Monitoring Instrument (OMI). Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows non-gradient-based jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast, where the background state has a clear swath. This article also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in passive-radiometer-retrieved cloud observables on cloud vertical structure, beyond cloud-top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification from Riishojgaard provides some help in this respect, by
McAloon, Conor G; Doherty, Michael L; Whyte, Paul; O'Grady, Luke; More, Simon J; Messam, Locksley L McV; Good, Margaret; Mullowney, Peter; Strain, Sam; Green, Martin J
2016-06-01
Bovine paratuberculosis is a disease characterised by chronic granulomatous enteritis which manifests clinically as a protein-losing enteropathy causing diarrhoea, hypoproteinaemia, emaciation and, eventually death. Some evidence exists to suggest a possible zoonotic link and a national voluntary Johne's Disease Control Programme was initiated by Animal Health Ireland in 2013. The objective of this study was to estimate herd-level true prevalence (HTP) and animal-level true prevalence (ATP) of paratuberculosis in Irish herds enrolled in the national voluntary JD control programme during 2013-14. Two datasets were used in this study. The first dataset had been collected in Ireland during 2005 (5822 animals from 119 herds), and was used to construct model priors. Model priors were updated with a primary (2013-14) dataset which included test records from 99,101 animals in 1039 dairy herds and was generated as part of the national voluntary JD control programme. The posterior estimate of HTP from the final Bayesian model was 0.23-0.34 with a 95% probability. Across all herds, the median ATP was found to be 0.032 (0.009, 0.145). This study represents the first use of Bayesian methodology to estimate the prevalence of paratuberculosis in Irish dairy herds. The HTP estimate was higher than previous Irish estimates but still lower than estimates from other major dairy producing countries.
Statistical process control of a Kalman filter model.
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A
2014-09-26
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
Statistical Process Control of a Kalman Filter Model
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A.
2014-01-01
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations. PMID:25264959
The Bayesian Inventory Problem
1984-05-01
Bayesian Approach to Demand Estimation and Inventory Provisioning," Naval Research Logistics Quarterly. Vol 20, 1973, (p607-624). 4 DeGroot , Morris H...page is blank APPENDIX A SUFFICIENT STATISTICS A convenient reference for moat of this material is DeGroot (41. Su-pose that we are sampling from a
Monitoring Pharmacy Expert System Performance Using Statistical Process Control Methodology
Doherty, Joshua A.; Reichley, Richard M.; Noirot, Laura A.; Resetar, Ervina; Hodge, Michael R.; Sutter, Robert D.; Dunagan, Wm Claiborne; Bailey, Thomas C.
2003-01-01
Automated expert systems provide a reliable and effective way to improve patient safety in a hospital environment. Their ability to analyze large amounts of data without fatigue is a decided advantage over clinicians who perform the same tasks. As dependence on expert systems increase and the systems become more complex, it is important to closely monitor their performance. Failure to generate alerts can jeopardize the health and safety of patients, while generating excessive false positives can cause valid alerts to be dismissed as noise. In this study, statistical process control charts were used to monitor an expert system, and the strengths and weaknesses of this technology are presented. PMID:14728163
NASA Astrophysics Data System (ADS)
Kumjian, M. R.; Prat, O. P.; van Lier-Walqui, M.; Morrison, H.; Martinkus, C.
2016-12-01
Radar polarimetry is a useful tool for understanding cloud and precipitation microphysics. Such observations, however informative, are only capable of showing signatures that are associated with microphysical processes, rather than quantitative estimates of the actual process rates. Thus, accurate models of observed features are needed to explore relationships between microphysics and radar observables. Current microphysics schemes are ill-suited for this because they have errors caused by structural assumptions and poorly constrained parameters. We present an alternative approach wherein observations provide a fundamental physical and statistical underpinning for construction of a new microphysical parameterization. The Bayesian Observationally-constrained Statistical-physical Scheme (BOSS; van Lier-Walqui et al., this meeting) is a novel approach that facilitates using radar polarimetry for development and constraint of rain microphysics. We use a 1D bin model of rain processes and dual-pol radar forward oeprator to generate a large library of simulations covering a variety of column-top drop size distributions (DSDs), environmental profiles, and process rate formulations (e.g., breakup and coalescence kernels, fallspeed relations). The resulting library is used to investigate the BOSS's ability to capture microphysical behavior in widely varying conditions, and to constrain the scheme's parameters, serving as a first-guess prior for further constraint by real observations. Typical forward operators use model output to determine a DSD that is subsequently discretized; scattering calculations are applied to these individual particles. Given that widely used bulk schemes do not explicitly predict DSDs, a priori assumptions about the DSD functional form are required. Because BOSS predicts DSD moments and does not assume a DSD shape, we develop a polarimetric forward operator in terms of DSD moments using the library of bin simulations. This eliminates structural
Raso, Giovanna; Vounatsou, Penelope; McManus, Donald P; N'Goran, Eliézer K; Utzinger, Jürg
2007-11-01
Models that accurately estimate the age-specific infection prevalence of Schistosoma mansoni can be useful for schistosomiasis control programmes, particularly with regard to whether mass drug administration or selected treatment should be employed. We developed a Bayesian formulation of an immigration-death model that has been previously proposed, which used maximum likelihood inference for estimating the age-specific S. mansoni prevalence in a dataset from Egypt. For comparative purposes, we first applied the Bayesian formulation of the immigration-death model to the dataset from Egypt. We further analysed data obtained from a cross-sectional parasitological survey that determined the infection prevalence of S. mansoni among 447 individuals in a village in Côte d'Ivoire. Three consecutive stool samples were collected from each participant and analysed by the Kato-Katz technique. In the Côte d'Ivoire study, the observed S. mansoni infection prevalence was 41.6% and varied with age. The immigration-death model was able to correctly predict 50% of the observed age group-specific point prevalences. The model presented here can be utilized to estimate S. mansoni community infection prevalences, which in turn helps in the strategic planning of schistosomiasis control.
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…
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…
Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use
Stewart, Jennifer L.; Zhang, Shunan; Tapert, Susan F.; Yu, Angela J.; Paulus, Martin P.
2015-01-01
Bayesian ideal observer models quantify individuals’ context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18–24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to ‘stop’ or to ‘go’ are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. PMID:26336910
Bhadra, Dhiman; Daniels, Michael J.; Kim, Sungduk; Ghosh, Malay; Mukherjee, Bhramar
2014-01-01
In a typical case-control study, exposure information is collected at a single time-point for the cases and controls. However, case-control studies are often embedded in existing cohort studies containing a wealth of longitudinal exposure history on the participants. Recent medical studies have indicated that incorporating past exposure history, or a constructed summary measure of cumulative exposure derived from the past exposure history, when available, may lead to more precise and clinically meaningful estimates of the disease risk. In this paper, we propose a flexible Bayesian semiparametric approach to model the longitudinal exposure profiles of the cases and controls and then use measures of cumulative exposure based on a weighted integral of this trajectory in the final disease risk model. The estimation is done via a joint likelihood. In the construction of the cumulative exposure summary, we introduce an influence function, a smooth function of time to characterize the association pattern of the exposure profile on the disease status with different time windows potentially having differential influence/weights. This enables us to analyze how the present disease status of a subject is influenced by his/her past exposure history conditional on the current ones. The joint likelihood formulation allows us to properly account for uncertainties associated with both stages of the estimation process in an integrated manner. Analysis is carried out in a hierarchical Bayesian framework using Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms. The proposed methodology is motivated by, and applied to a case-control study of prostate cancer where longitudinal biomarker information is available for the cases and controls. PMID:22313248
Structure Learning in Bayesian Sensorimotor Integration
Genewein, Tim; Hez, Eduard; Razzaghpanah, Zeynab; Braun, Daniel A.
2015-01-01
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration. PMID:26305797
Bayesian demography 250 years after Bayes
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
Bayesian demography 250 years after Bayes.
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.
BIE: Bayesian Inference Engine
NASA Astrophysics Data System (ADS)
Weinberg, Martin D.
2013-12-01
The Bayesian Inference Engine (BIE) is an object-oriented library of tools written in C++ designed explicitly to enable Bayesian update and model comparison for astronomical problems. To facilitate "what if" exploration, BIE provides a command line interface (written with Bison and Flex) to run input scripts. The output of the code is a simulation of the Bayesian posterior distribution from which summary statistics e.g. by taking moments, or determine confidence intervals and so forth, can be determined. All of these quantities are fundamentally integrals and the Markov Chain approach produces variates heta distributed according to P( heta|D) so moments are trivially obtained by summing of the ensemble of variates.
The influence of control group reproduction on the statistical ...
Because of various Congressional mandates to protect the environment from endocrine disrupting chemicals (EDCs), the United States Environmental Protection Agency (USEPA) initiated the Endocrine Disruptor Screening Program. In the context of this framework, the Office of Research and Development within the USEPA developed the Medaka Extended One Generation Reproduction Test (MEOGRT) to characterize the endocrine action of a suspected EDC. One important endpoint of the MEOGRT is fecundity of breeding pairs of medaka. Power analyses were conducted to determine the number of replicates needed in proposed test designs and to determine the effects that varying reproductive parameters (e.g. mean fecundity, variance, and days with no egg production) will have on the statistical power of the test. A software tool, the MEOGRT Reproduction Power Analysis Tool, was developed to expedite these power analyses by both calculating estimates of the needed reproductive parameters (e.g. population mean and variance) and performing the power analysis under user specified scenarios. The manuscript illustrates how the reproductive performance of the control medaka that are used in a MEOGRT influence statistical power, and therefore the successful implementation of the protocol. Example scenarios, based upon medaka reproduction data collected at MED, are discussed that bolster the recommendation that facilities planning to implement the MEOGRT should have a culture of medaka with hi
Statistically Controlling for Confounding Constructs Is Harder than You Think
Westfall, Jacob; Yarkoni, Tal
2016-01-01
Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity. PMID:27031707
LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS
Lipton, D.L.; Wong, H.K.T.
1984-02-01
An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.
Green, M.J.; Browne, W.J.; Green, L.E.; Bradley, A.J.; Leach, K.A.; Breen, J.E.; Medley, G.F.
2009-01-01
The fundamental objective for health research is to determine whether changes should be made to clinical decisions. Decisions made by veterinary surgeons in the light of new research evidence are known to be influenced by their prior beliefs, especially their initial opinions about the plausibility of possible results. In this paper, clinical trial results for a bovine mastitis control plan were evaluated within a Bayesian context, to incorporate a community of prior distributions that represented a spectrum of clinical prior beliefs. The aim was to quantify the effect of veterinary surgeons’ initial viewpoints on the interpretation of the trial results. A Bayesian analysis was conducted using Markov chain Monte Carlo procedures. Stochastic models included a financial cost attributed to a change in clinical mastitis following implementation of the control plan. Prior distributions were incorporated that covered a realistic range of possible clinical viewpoints, including scepticism, enthusiasm and uncertainty. Posterior distributions revealed important differences in the financial gain that clinicians with different starting viewpoints would anticipate from the mastitis control plan, given the actual research results. For example, a severe sceptic would ascribe a probability of 0.50 for a return of <£5 per cow in an average herd that implemented the plan, whereas an enthusiast would ascribe this probability for a return of >£20 per cow. Simulations using increased trial sizes indicated that if the original study was four times as large, an initial sceptic would be more convinced about the efficacy of the control plan but would still anticipate less financial return than an initial enthusiast would anticipate after the original study. In conclusion, it is possible to estimate how clinicians’ prior beliefs influence their interpretation of research evidence. Further research on the extent to which different interpretations of evidence result in changes to
Howard B. Stauffer; Cynthia J. Zabel; Jeffrey R. Dunk
2005-01-01
We compared a set of competing logistic regression habitat selection models for Northern Spotted Owls (Strix occidentalis caurina) in California. The habitat selection models were estimated, compared, evaluated, and tested using multiple sample datasets collected on federal forestlands in northern California. We used Bayesian methods in interpreting...
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…
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…
Improved statistical method for temperature and salinity quality control
NASA Astrophysics Data System (ADS)
Gourrion, Jérôme; Szekely, Tanguy
2017-04-01
Climate research and Ocean monitoring benefit from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of an automatic quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to late 2015, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has already been implemented in the latest version of the delayed-time CMEMS in-situ dataset and will be deployed soon in the equivalent near-real time products.
Villani, N; Gérard, K; Marchesi, V; Huger, S; François, P; Noël, A
2010-06-01
The first purpose of this study was to illustrate the contribution of statistical process control for a better security in intensity modulated radiotherapy (IMRT) treatments. This improvement is possible by controlling the dose delivery process, characterized by pretreatment quality control results. So, it is necessary to put under control portal dosimetry measurements (currently, the ionisation chamber measurements were already monitored by statistical process control thanks to statistical process control tools). The second objective was to state whether it is possible to substitute ionisation chamber with portal dosimetry in order to optimize time devoted to pretreatment quality control. At Alexis-Vautrin center, pretreatment quality controls in IMRT for prostate and head and neck treatments were performed for each beam of each patient. These controls were made with an ionisation chamber, which is the reference detector for the absolute dose measurement, and with portal dosimetry for the verification of dose distribution. Statistical process control is a statistical analysis method, coming from industry, used to control and improve the studied process quality. It uses graphic tools as control maps to follow-up process, warning the operator in case of failure, and quantitative tools to evaluate the process toward its ability to respect guidelines: this is the capability study. The study was performed on 450 head and neck beams and on 100 prostate beams. Control charts, showing drifts, both slow and weak, and also both strong and fast, of mean and standard deviation have been established and have shown special cause introduced (manual shift of the leaf gap of the multileaf collimator). Correlation between dose measured at one point, given with the EPID and the ionisation chamber has been evaluated at more than 97% and disagreement cases between the two measurements were identified. The study allowed to demonstrate the feasibility to reduce the time devoted to
Bayesian B-spline mapping for dynamic quantitative traits.
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.
Sobas, Frédéric; Tsiamyrtzis, Panagiotis; Benattar, Norbert; Lienhart, Anne; Négrier, Claude
2014-09-01
An ideal medical biology internal quality control (IQC) plan should both monitor the laboratory methods efficiently and implement the relevant clinical-biological specifications. However, many laboratories continue to use the 12s quality control rule without considering the high risk of false rejection and without considering the relationship of analytical performance to quality requirements. Alternatively, one can move to the Bayesian arena, enabling probabilistic quantification of the information coming in, on a daily basis from the laboratory's IQC tests, and taking into account the laboratory's medical and economic contexts. Using the example of one-stage clotting factor VIII assay, the present study compares frequentist (12s quality control rule) and Bayesian IQC management with respect to prescriber requirements, process start-up phase issues, and abnormal scenarios in IQC results. To achieve comparable confidence, the traditional 12s quality control rule requires more data than the Bayesian approach in order to detect an increase in the random or systematic error of the method. Moreover, the Bayesian IQC management approach explicitly implements respect of prescriber requirements in terms of calculating the probability that the variable in question lies in a given predefined interval: for example, the factor VIII concentration required after knee surgery in a hemophilia patient.
Tidal controls on earthquake size-frequency statistics
NASA Astrophysics Data System (ADS)
Ide, S.; Yabe, S.; Tanaka, Y.
2016-12-01
The possibility that tidal stresses can trigger earthquakes is a long-standing issue in seismology. Except in some special cases, a causal relationship between seismicity and the phase of tidal stress has been rejected on the basis of studies using many small events. However, recently discovered deep tectonic tremors are highly sensitive to tidal stress levels, with the relationship being governed by a nonlinear law according to which the tremor rate increases exponentially with increasing stress; thus, slow deformation (and the probability of earthquakes) may be enhanced during periods of large tidal stress. Here, we show the influence of tidal stress on seismicity by calculating histories of tidal shear stress during the 2-week period before earthquakes. Very large earthquakes tend to occur near the time of maximum tidal stress, but this tendency is not obvious for small earthquakes. Rather, we found that tidal stress controls the earthquake size-frequency statistics; i.e., the fraction of large events increases (i.e. the b-value of the Gutenberg-Richter relation decreases) as the tidal shear stress increases. This correlation is apparent in data from the global catalog and in relatively homogeneous regional catalogues of earthquakes in Japan. The relationship is also reasonable, considering the well-known relationship between stress and the b-value. Our findings indicate that the probability of a tiny rock failure expanding to a gigantic rupture increases with increasing tidal stress levels. This finding has clear implications for probabilistic earthquake forecasting.
Impact angle control of interplanetary shock geoeffectiveness: A statistical study
NASA Astrophysics Data System (ADS)
Oliveira, Denny M.; Raeder, Joachim
2015-06-01
We present a survey of interplanetary (IP) shocks using Wind and ACE satellite data from January 1995 to December 2013 to study how IP shock geoeffectiveness is controlled by IP shock impact angles. A shock list covering one and a half solar cycle is compiled. The yearly number of IP shocks is found to correlate well with the monthly sunspot number. We use data from SuperMAG, a large chain with more than 300 geomagnetic stations, to study geoeffectiveness triggered by IP shocks. The SuperMAG SML index, an enhanced version of the familiar AL index, is used in our statistical analysis. The jumps of the SML index triggered by IP shock impacts on the Earth's magnetosphere are investigated in terms of IP shock orientation and speed. We find that, in general, strong (high speed) and almost frontal (small impact angle) shocks are more geoeffective than inclined shocks with low speed. The strongest correlation (correlation coefficient R = 0.78) occurs for fixed IP shock speed and for varied IP shock impact angle. We attribute this result, predicted previously with simulations, to the fact that frontal shocks compress the magnetosphere symmetrically from all sides, which is a favorable condition for the release of magnetic energy stored in the magnetotail, which in turn can produce moderate to strong auroral substorms, which are then observed by ground-based magnetometers.
Bayesian Inference: with ecological applications
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.
Gryspeirt, Aiko; Gubbins, Simon
2013-09-01
Current strategies to control classical scrapie remove animals at risk of scrapie rather than those known to be infected with the scrapie agent. Advances in diagnostic tests, however, suggest that a more targeted approach involving the application of a rapid live test may be feasible in future. Here we consider the use of two diagnostic tests: recto-anal mucosa-associated lymphatic tissue (RAMALT) biopsies; and a blood-based assay. To assess their impact we developed a stochastic age- and prion protein (PrP) genotype-structured model for the dynamics of scrapie within a sheep flock. Parameters were estimated in a Bayesian framework to facilitate integration of a number of disparate datasets and to allow parameter uncertainty to be incorporated in model predictions. In small flocks a control strategy based on removal of clinical cases was sufficient to control disease and more stringent measures (including the use of a live diagnostic test) did not significantly reduce outbreak size or duration. In medium or large flocks strategies in which a large proportion of animals are tested with either live diagnostic test significantly reduced outbreak size, but not always duration, compared with removal of clinical cases. However, the current Compulsory Scrapie Flocks Scheme (CSFS) significantly reduced outbreak size and duration compared with both removal of clinical cases and all strategies using a live diagnostic test. Accordingly, under the assumptions made in the present study there is little benefit from implementing a control strategy which makes use of a live diagnostic test.
An Extended Bayesian-FBP Algorithm.
Zeng, Gengsheng L; Divkovic, Zeljko
2016-02-01
Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximum a Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with L1-norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.
Bayesian approach to non-inferiority trials for normal means.
Gamalo, M Amper; Wu, Rui; Tiwari, Ram C
2016-02-01
Regulatory framework recommends that novel statistical methodology for analyzing trial results parallels the frequentist strategy, e.g. the new method must protect type-I error and arrive at a similar conclusion. Keeping these in mind, we construct a Bayesian approach for non-inferiority trials with normal response. A non-informative prior is assumed for the mean response of the experimental treatment and Jeffrey's prior for its corresponding variance when it is unknown. The posteriors of the mean response and variance of the treatment in historical trials are then assumed as priors for its corresponding parameters in the current trial, where that treatment serves as the active control. From these priors, a Bayesian decision criterion is derived to determine whether the experimental treatment is non-inferior to the active control. This criterion is evaluated and compared with the frequentist method using simulation studies. Results show that both Bayesian and frequentist approaches perform alike, but the Bayesian approach has a higher power when the variances are unknown. Both methods also arrive at the same conclusion of non-inferiority when applied on two real datasets. A major advantage of the proposed Bayesian approach lies in its ability to provide posterior probabilities for varying effect sizes of the experimental treatment over the active control.
Zhang, Jing; Cole, Stephen R; Richardson, David B; Chu, Haitao
2013-11-10
In case-control studies, exposure assessments are almost always error-prone. In the absence of a gold standard, two or more assessment approaches are often used to classify people with respect to exposure. Each imperfect assessment tool may lead to misclassification of exposure assignment; the exposure misclassification may be differential with respect to case status or not; and, the errors in exposure classification under the different approaches may be independent (conditional upon the true exposure status) or not. Although methods have been proposed to study diagnostic accuracy in the absence of a gold standard, these methods are infrequently used in case-control studies to correct exposure misclassification that is simultaneously differential and dependent. In this paper, we proposed a Bayesian method to estimate the measurement-error corrected exposure-disease association, accounting for both differential and dependent misclassification. The performance of the proposed method is investigated using simulations, which show that the proposed approach works well, as well as an application to a case-control study assessing the association between asbestos exposure and mesothelioma.
NASA Technical Reports Server (NTRS)
Gupta, Pramod; Guenther, Kurt; Hodgkinson, John; Jacklin, Stephen; Richard, Michael; Schumann, Johann; Soares, Fola
2005-01-01
Modern exploration missions require modern control systems-control systems that can handle catastrophic changes in the system's behavior, compensate for slow deterioration in sustained operations, and support fast system ID. Adaptive controllers, based upon Neural Networks have these capabilities, but they can only be used safely if proper verification & validation (V&V) can be done. In this paper we present our V & V approach and simulation result within NASA's Intelligent Flight Control Systems (IFCS).
Word learning as Bayesian inference.
Xu, Fei; Tenenbaum, Joshua B
2007-04-01
The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning. (c) 2007 APA, all rights reserved.
77 FR 46096 - Statistical Process Controls for Blood Establishments; Public Workshop
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-02
...: Statistical process control is the application of statistical methods to the monitoring, or quality control... monitors manufacturing procedures, validation summaries, and quality control data prior to licensure and... at implementation and then monitor these processes on a regular basis, using quality control...
A Goal-Directed Bayesian Framework for Categorization
Rigoli, Francesco; Pezzulo, Giovanni; Dolan, Raymond; Friston, Karl
2017-01-01
Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis. PMID:28382008
Wholesale Warehouse Inventory Control with Statistical Demand Information.
1980-12-01
following pages. Harvey M. Wagner Principal investigator Richard Ehrhardt Co-Principal Investigator I|.4J I]I . . MacCormick , A. (1974), Statistical...Analysis, Uni- versity of North Carolina at Chapel Hill, 109 pp. Schultz, C. R., R. Ehrhardt, and A. MacCormick (1977), Forecasting Operating...b t-b) t=l of the time series. Previous forecasting studies [ MacCormick (1974), Estey and Kaufman (1975), Ehrhardt (1976), and Kaufman (1977)] have
Liu, Yueyi I; Kamaya, Aya; Desser, Terry S; Rubin, Daniel L
2009-03-01
It is challenging to distinguish benign from malignant thyroid nodules on high resolution ultrasound. Many ultrasound features have been studied individually as predictors for thyroid malignancy, none with a high degree of accuracy, and there is no consistent vocabulary used to describe the features. Our hypothesis is that a standard vocabulary will advance accuracy. We performed a systemic literature review and identified all the sonographic features that have been well studied in thyroid cancers. We built a controlled vocabulary for describing sonographic features and to enable us to unify data in the literature on the predictive power of each feature. We used this terminology to build a Bayesian network to predict thyroid malignancy. Our Bayesian network performed similar to or slightly better than experienced radiologists. Controlled terminology for describing thyroid radiology findings could be useful to characterize thyroid nodules and could enable decision support applications.
Ball, Greg; Piller, Linda B
2011-09-01
If the primary objective of a trial is to learn about the ability of a new treatment to help future patients without sacrificing the safe and effective treatment of the current patients, then a Bayesian design with frequent assessments of the accumulating data should be considered. Unfortunately, Bayesian analyses typically do not have standard approaches, and because of the subjectivity of prior probabilities and the possibility for introducing bias, statisticians have developed other methods for statistical inference that only depend on deductive probabilities. However, these frequentist probabilities are just theories about how certain relative frequencies will develop over time. They have no real meaning in a single experiment. Designed to work well in the long run, p-values become hard to explain for individual experiments. Fortunately, the controversy surrounding Bayes' theorem comes, not from the representation of evidence, but from the use of probabilities to measure belief. A prior distribution is not necessary. The likelihood function contains all of the information in a trial relevant for making inferences about the parameters. Monitoring clinical trials is a dynamic process which requires flexibility to respond to unforeseen developments. Likelihood ratios allow the data to speak for themselves, without regard for the probability of observing weak or misleading evidence, and decisions to stop, or continue, a trial can be made at any time, with all of the available information. A likelihood based method is needed.
NASA Technical Reports Server (NTRS)
Gupta, Pramod; Jacklin, Stephen; Schumann, Johann; Guenther, Kurt; Richard, Michael; Soares, Fola
2005-01-01
Modem aircraft, UAVs, and robotic spacecraft pose substantial requirements on controllers in the light of ever increasing demands for reusability, affordability, and reliability. The individual systems (which are often nonlinear) must be controlled safely and reliably in environments where it is virtually impossible to analyze-ahead of time- all the important and possible scenarios and environmental factors. For example, system components (e.g., gyros, bearings of reaction wheels, valves) may deteriorate or break during autonomous UAV operation or long-lasting space missions, leading to a sudden, drastic change in vehicle performance. Manual repair or replacement is not an option in such cases. Instead, the system must be able to cope with equipment failure and deterioration. Controllability of the system must be retained as good as possible or re-established as fast as possible with a minimum of deactivation or shutdown of the system being controlled. In such situations the control engineer has to employ adaptive control systems that automatically sense and correct themselves whenever drastic disturbances and/or severe changes in the plant or environment occur.
Statistical process control applications in the metal working industry
Powers, H.G.
1985-01-01
Purpose of this paper is to describe those activities considered to be essential for continual improvements in the state of control over processes and to present basic concepts for structuring those activities. An effective process control function provides for improvements in both product quality and productivity. Quality increases because improved control over the process results in greater product uniformity. Productivity improves because costs related to removal of defective product, rework, repair, scrap, and customer dissatisfaction are minimized or eliminated.
Prior approval: the growth of Bayesian methods in psychology.
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.
Bayesian computation via empirical likelihood
Mengersen, Kerrie L.; Pudlo, Pierre; Robert, Christian P.
2013-01-01
Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models. PMID:23297233
Statistical process control (SPC) for coordinate measurement machines
Escher, R.N.
2000-01-04
The application of process capability analysis, using designed experiments, and gage capability studies as they apply to coordinate measurement machine (CMM) uncertainty analysis and control will be demonstrated. The use of control standards in designed experiments, and the use of range charts and moving range charts to separate measurement error into it's discrete components will be discussed. The method used to monitor and analyze the components of repeatability and reproducibility will be presented with specific emphasis on how to use control charts to determine and monitor CMM performance and capability, and stay within your uncertainty assumptions.
A new prior for bayesian anomaly detection: application to biosurveillance.
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
A Total Quality-Control Plan with Right-Sized Statistical Quality-Control.
Westgard, James O
2017-03-01
A new Clinical Laboratory Improvement Amendments option for risk-based quality-control (QC) plans became effective in January, 2016. Called an Individualized QC Plan, this option requires the laboratory to perform a risk assessment, develop a QC plan, and implement a QC program to monitor ongoing performance of the QC plan. Difficulties in performing a risk assessment may limit validity of an Individualized QC Plan. A better alternative is to develop a Total QC Plan including a right-sized statistical QC procedure to detect medically important errors. Westgard Sigma Rules provides a simple way to select the right control rules and the right number of control measurements. Copyright © 2016 Elsevier Inc. All rights reserved.
Statistical methodologies for the control of dynamic remapping
NASA Technical Reports Server (NTRS)
Saltz, J. H.; Nicol, D. M.
1986-01-01
Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.
Statistical analysis of static shape control in space structures
NASA Technical Reports Server (NTRS)
Burdisso, Ricardo A.; Haftka, Raphael T.
1990-01-01
The article addresses the problem of efficient analysis of the statistics of initial and corrected shape distortions in space structures. Two approaches for improving efficiency are considered. One is an adjoint technique for calculating distortion shapes: the second is a modal expansion of distortion shapes in terms of pseudo-vibration modes. The two techniques are applied to the problem of optimizing actuator locations on a 55 m radiometer antenna. The adjoint analysis technique is used with a discrete-variable optimization method. The modal approximation technique is coupled with a standard conjugate-gradient continuous optimization method. The agreement between the two sets of results is good, validating both the approximate analysis and optimality of the results.
Bayesian Analysis of Individual Level Personality Dynamics.
Cripps, Edward; Wood, Robert E; Beckmann, Nadin; Lau, John; Beckmann, Jens F; Cripps, Sally Ann
2016-01-01
A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine whether the patterns of within-person responses on a 12-trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999). ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiraling. While Bayesian techniques have many potential advantages for the analyses of processes at the level of the individual, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.
Bayesian Analysis of Individual Level Personality Dynamics
Cripps, Edward; Wood, Robert E.; Beckmann, Nadin; Lau, John; Beckmann, Jens F.; Cripps, Sally Ann
2016-01-01
A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine whether the patterns of within-person responses on a 12-trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999). ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiraling. While Bayesian techniques have many potential advantages for the analyses of processes at the level of the individual, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques. PMID:27486415
Methods of Statistical Control for Groundwater Quality Indicators
NASA Astrophysics Data System (ADS)
Yankovich, E.; Nevidimova, O.; Yankovich, K.
2016-06-01
The article describes the results of conducted groundwater quality control. Controlled quality indicators included the following microelements - barium, manganese, iron, mercury, iodine, chromium, strontium, etc. Quality control charts - X-bar chart and R chart - were built. For the upper and the lower threshold limits, maximum permissible concentration of components in water and the lower limit of their biologically significant concentration, respectively, were selected. The charts analysis has shown that the levels of microelements content in water at the area of study are stable. Most elements in the underground water are contained in concentrations, significant for human organisms consuming the water. For example, such elements as Ba, Mn, Fe have concentrations that exceed maximum permissible levels for drinking water.
Liu, Jin; Chen, Feng; Zhu, Feng-Cai; Bai, Jian-Ling; Li, Jing-Xin; Yu, Hao; Liu, Pei; Zeng, Ping
2015-01-01
In the evaluation of vaccine seroresponse rates and adverse reaction rates, extreme test results often occur, with substantial adverse event rates of 0% and/or seroresponse rates of 100%, which has produced several data challenges. Few studies have used both the Bayesian and frequentist methods on the same sets of data that contain extreme test cases to evaluate vaccine safety and immunogenicity. In this study, Bayesian methods were introduced, and the comparison with frequentist methods was made based on practical cases from randomized controlled vaccine trials and a simulation experiment to examine the rationality of the Bayesian methods. The results demonstrated that the Bayesian non-informative method obtained lower limits (for extreme cases of 100%) and upper limits (for extreme cases of zero), which were similar to the limits that were identified with the frequentist method. The frequentist rate estimates and corresponding confidence intervals (CIs) for extreme cases of 0 or 100% always equaled and included 0 or 100%, respectively, whereas the Bayesian estimations varied depending on the sample size, with none equaling zero or 100%. The Bayesian method obtained more reasonable interval estimates of the rates with extreme data compared with the frequentist method, whereas the frequentist method objectively expressed the outcomes of clinical vaccine trials. The two types of statistical results are complementary, and it is proposed that the Bayesian and frequentist methods should be combined to more comprehensively evaluate clinical vaccine trials.
Woldegebriel, Michael; Zomer, Paul; Mol, Hans G J; Vivó-Truyols, Gabriel
2016-08-02
In this work, we introduce an automated, efficient, and elegant model to combine all pieces of evidence (e.g., expected retention times, peak shapes, isotope distributions, fragment-to-parent ratio) obtained from liquid chromatography-tandem mass spectrometry (LC-MS/MS/MS) data for screening purposes. Combining all these pieces of evidence requires a careful assessment of the uncertainties in the analytical system as well as all possible outcomes. To-date, the majority of the existing algorithms are highly dependent on user input parameters. Additionally, the screening process is tackled as a deterministic problem. In this work we present a Bayesian framework to deal with the combination of all these pieces of evidence. Contrary to conventional algorithms, the information is treated in a probabilistic way, and a final probability assessment of the presence/absence of a compound feature is computed. Additionally, all the necessary parameters except the chromatographic band broadening for the method are learned from the data in training and learning phase of the algorithm, avoiding the introduction of a large number of user-defined parameters. The proposed method was validated with a large data set and has shown improved sensitivity and specificity in comparison to a threshold-based commercial software package.
Bayesian Comparison of Two Regression Lines.
ERIC Educational Resources Information Center
Tsutakawa, Robert K.
1978-01-01
A Bayesian solution is presented for the Johnson-Neyman problem (whether or not the distance between two regression lines is statistically significant over a finite interval of the independent variable). (Author/CTM)
Bayesian stable isotope mixing models
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...
Bayesian stable isotope mixing models
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...
Bayesian Integrated Microbial Forensics
Jarman, Kristin H.; Kreuzer-Martin, Helen W.; Wunschel, David S.; Valentine, Nancy B.; Cliff, John B.; Petersen, Catherine E.; Colburn, Heather A.; Wahl, Karen L.
2008-06-01
In the aftermath of the 2001 anthrax letters, researchers have been exploring ways to predict the production environment of unknown source microorganisms. Different mass spectral techniques are being developed to characterize components of a microbe’s culture medium including water, carbon and nitrogen sources, metal ions added, and the presence of agar. Individually, each technique has the potential to identify one or two ingredients in a culture medium recipe. However, by integrating data from multiple mass spectral techniques, a more complete characterization is possible. We present a Bayesian statistical approach to integrated microbial forensics and illustrate its application on spores grown in different culture media.
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research
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
Using Statistical Process Control to Make Data-Based Clinical Decisions.
ERIC Educational Resources Information Center
Pfadt, Al; Wheeler, Donald J.
1995-01-01
Statistical process control (SPC), which employs simple statistical tools and problem-solving techniques such as histograms, control charts, flow charts, and Pareto charts to implement continual product improvement procedures, can be incorporated into human service organizations. Examples illustrate use of SPC procedures to analyze behavioral data…
Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings
ERIC Educational Resources Information Center
Omar, M. Hafidz
2010-01-01
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation…
Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings
ERIC Educational Resources Information Center
Omar, M. Hafidz
2010-01-01
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation…
Active Control for Statistically Stationary Turbulent PremixedFlame Simulations
Bell, J.B.; Day, M.S.; Grcar, J.F.; Lijewski, M.J.
2005-08-30
The speed of propagation of a premixed turbulent flame correlates with the intensity of the turbulence encountered by the flame. One consequence of this property is that premixed flames in both laboratory experiments and practical combustors require some type of stabilization mechanism to prevent blow-off and flashback. The stabilization devices often introduce a level of geometric complexity that is prohibitive for detailed computational studies of turbulent flame dynamics. Furthermore, the stabilization introduces additional fluid mechanical complexity into the overall combustion process that can complicate the analysis of fundamental flame properties. To circumvent these difficulties we introduce a feedback control algorithm that allows us to computationally stabilize a turbulent premixed flame in a simple geometric configuration. For the simulations, we specify turbulent inflow conditions and dynamically adjust the integrated fueling rate to control the mean location of the flame in the domain. We outline the numerical procedure, and illustrate the behavior of the control algorithm on methane flames at various equivalence ratios in two dimensions. The simulation data are used to study the local variation in the speed of propagation due to flame surface curvature.
NASA Astrophysics Data System (ADS)
Olivares, G.; Teferle, F. N.
2013-12-01
Geodetic time series provide information which helps to constrain theoretical models of geophysical processes. It is well established that such time series, for example from GPS, superconducting gravity or mean sea level (MSL), contain time-correlated noise which is usually assumed to be a combination of a long-term stochastic process (characterized by a power-law spectrum) and random noise. Therefore, when fitting a model to geodetic time series it is essential to also estimate the stochastic parameters beside the deterministic ones. Often the stochastic parameters include the power amplitudes of both time-correlated and random noise, as well as, the spectral index of the power-law process. To date, the most widely used method for obtaining these parameter estimates is based on maximum likelihood estimation (MLE). We present an integration method, the Bayesian Monte Carlo Markov Chain (MCMC) method, which, by using Markov chains, provides a sample of the posteriori distribution of all parameters and, thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. This algorithm automatically optimizes the Markov chain step size and estimates the convergence state by spectral analysis of the chain. We assess the MCMC method through comparison with MLE, using the recently released GPS position time series from JPL and apply it also to the MSL time series from the Revised Local Reference data base of the PSMSL. Although the parameter estimates for both methods are fairly equivalent, they suggest that the MCMC method has some advantages over MLE, for example, without further computations it provides the spectral index uncertainty, is computationally stable and detects multimodality.
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
NASA Astrophysics Data System (ADS)
Karmakar, Mampi; Maiti, Saumen; Singh, Amrita; Ojha, Maheswar; Maity, Bhabani Sankar
2017-07-01
Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for
Bayesian survival analysis in clinical trials: What methods are used in practice?
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
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.
Bayesian anatomy of galaxy structure
NASA Astrophysics Data System (ADS)
Yoon, Ilsang
In this thesis I develop Bayesian approach to model galaxy surface brightness and apply it to a bulge-disc decomposition analysis of galaxies in near-infrared band, from Two Micron All Sky Survey (2MASS). The thesis has three main parts. First part is a technical development of Bayesian galaxy image decomposition package GALPHAT based on Markov chain Monte Carlo algorithm. I implement a fast and accurate galaxy model image generation algorithm to reduce computation time and make Bayesian approach feasible for real science analysis using large ensemble of galaxies. I perform a benchmark test of G ALPHAT and demonstrate significant improvement in parameter estimation with a correct statistical confidence. Second part is a performance test for full Bayesian application to galaxy bulge-disc decomposition analysis including not only the parameter estimation but also the model comparison to classify different galaxy population. The test demonstrates that GALPHAT has enough statistical power to make a reliable model inference using galaxy photometric survey data. Bayesian prior update is also tested for parameter estimation and Bayes factor model comparison and it shows that informative prior significantly improves the model inference in every aspects. Last part is a Bayesian bulge-disc decomposition analysis using 2MASS Ks-band selected samples. I characterise the luminosity distributions in spheroids, bulges and discs separately in the local Universe and study the galaxy morphology correlation, by full utilizing the ensemble parameter posterior of the entire galaxy samples. It shows that to avoid a biased inference, the parameter covariance and model degeneracy has to be carefully characterized by the full probability distribution.
La Russa, D
2015-06-15
Purpose: The purpose of this project is to develop a robust method of parameter estimation for a Poisson-based TCP model using Bayesian inference. Methods: Bayesian inference was performed using the PyMC3 probabilistic programming framework written in Python. A Poisson-based TCP regression model that accounts for clonogen proliferation was fit to observed rates of local relapse as a function of equivalent dose in 2 Gy fractions for a population of 623 stage-I non-small-cell lung cancer patients. The Slice Markov Chain Monte Carlo sampling algorithm was used to sample the posterior distributions, and was initiated using the maximum of the posterior distributions found by optimization. The calculation of TCP with each sample step required integration over the free parameter α, which was performed using an adaptive 24-point Gauss-Legendre quadrature. Convergence was verified via inspection of the trace plot and posterior distribution for each of the fit parameters, as well as with comparisons of the most probable parameter values with their respective maximum likelihood estimates. Results: Posterior distributions for α, the standard deviation of α (σ), the average tumour cell-doubling time (Td), and the repopulation delay time (Tk), were generated assuming α/β = 10 Gy, and a fixed clonogen density of 10{sup 7} cm−{sup 3}. Posterior predictive plots generated from samples from these posterior distributions are in excellent agreement with the observed rates of local relapse used in the Bayesian inference. The most probable values of the model parameters also agree well with maximum likelihood estimates. Conclusion: A robust method of performing Bayesian inference of TCP data using a complex TCP model has been established.
NASA Astrophysics Data System (ADS)
Isakson, Steve Wesley
2001-12-01
Well-known principles of physics explain why resolution restrictions occur in images produced by optical diffraction-limited systems. The limitations involved are present in all diffraction-limited imaging systems, including acoustical and microwave. In most circumstances, however, prior knowledge about the object and the imaging system can lead to resolution improvements. In this dissertation I outline a method to incorporate prior information into the process of reconstructing images to superresolve the object beyond the above limitations. This dissertation research develops the details of this methodology. The approach can provide the most-probable global solution employing a finite number of steps in both far-field and near-field images. In addition, in order to overcome the effects of noise present in any imaging system, this technique provides a weighted image that quantifies the likelihood of various imaging solutions. By utilizing Bayesian probability, the procedure is capable of incorporating prior information about both the object and the noise to overcome the resolution limitation present in many imaging systems. Finally I will present an imaging system capable of detecting the evanescent waves missing from far-field systems, thus improving the resolution further.
Bayesian robust principal component analysis.
Ding, Xinghao; He, Lihan; Carin, Lawrence
2011-12-01
A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observed matrix is a superposition of the two. The matrix is assumed noisy, with unknown and possibly non-stationary noise statistics. The Bayesian framework infers an approximate representation for the noise statistics while simultaneously inferring the low-rank and sparse-outlier contributions; the model is robust to a broad range of noise levels, without having to change model hyperparameter settings. In addition, the Bayesian framework allows exploitation of additional structure in the matrix. For example, in video applications each row (or column) corresponds to a video frame, and we introduce a Markov dependency between consecutive rows in the matrix (corresponding to consecutive frames in the video). The properties of this Markov process are also inferred based on the observed matrix, while simultaneously denoising and recovering the low-rank and sparse components. We compare the Bayesian model to a state-of-the-art optimization-based implementation of robust PCA; considering several examples, we demonstrate competitive performance of the proposed model.
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…
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…
Able, Charles M.; Bright, Megan; Frizzell, Bart
2013-03-01
Purpose: Statistical process control (SPC) is a quality control method used to ensure that a process is well controlled and operates with little variation. This study determined whether SPC was a viable technique for evaluating the proper operation of a high-dose-rate (HDR) brachytherapy treatment delivery system. Methods and Materials: A surrogate prostate patient was developed using Vyse ordnance gelatin. A total of 10 metal oxide semiconductor field-effect transistors (MOSFETs) were placed from prostate base to apex. Computed tomography guidance was used to accurately position the first detector in each train at the base. The plan consisted of 12 needles with 129 dwell positions delivering a prescribed peripheral dose of 200 cGy. Sixteen accurate treatment trials were delivered as planned. Subsequently, a number of treatments were delivered with errors introduced, including wrong patient, wrong source calibration, wrong connection sequence, single needle displaced inferiorly 5 mm, and entire implant displaced 2 mm and 4 mm inferiorly. Two process behavior charts (PBC), an individual and a moving range chart, were developed for each dosimeter location. Results: There were 4 false positives resulting from 160 measurements from 16 accurately delivered treatments. For the inaccurately delivered treatments, the PBC indicated that measurements made at the periphery and apex (regions of high-dose gradient) were much more sensitive to treatment delivery errors. All errors introduced were correctly identified by either the individual or the moving range PBC in the apex region. Measurements at the urethra and base were less sensitive to errors. Conclusions: SPC is a viable method for assessing the quality of HDR treatment delivery. Further development is necessary to determine the most effective dose sampling, to ensure reproducible evaluation of treatment delivery accuracy.
Able, Charles M; Bright, Megan; Frizzell, Bart
2013-03-01
Statistical process control (SPC) is a quality control method used to ensure that a process is well controlled and operates with little variation. This study determined whether SPC was a viable technique for evaluating the proper operation of a high-dose-rate (HDR) brachytherapy treatment delivery system. A surrogate prostate patient was developed using Vyse ordnance gelatin. A total of 10 metal oxide semiconductor field-effect transistors (MOSFETs) were placed from prostate base to apex. Computed tomography guidance was used to accurately position the first detector in each train at the base. The plan consisted of 12 needles with 129 dwell positions delivering a prescribed peripheral dose of 200 cGy. Sixteen accurate treatment trials were delivered as planned. Subsequently, a number of treatments were delivered with errors introduced, including wrong patient, wrong source calibration, wrong connection sequence, single needle displaced inferiorly 5 mm, and entire implant displaced 2 mm and 4 mm inferiorly. Two process behavior charts (PBC), an individual and a moving range chart, were developed for each dosimeter location. There were 4 false positives resulting from 160 measurements from 16 accurately delivered treatments. For the inaccurately delivered treatments, the PBC indicated that measurements made at the periphery and apex (regions of high-dose gradient) were much more sensitive to treatment delivery errors. All errors introduced were correctly identified by either the individual or the moving range PBC in the apex region. Measurements at the urethra and base were less sensitive to errors. SPC is a viable method for assessing the quality of HDR treatment delivery. Further development is necessary to determine the most effective dose sampling, to ensure reproducible evaluation of treatment delivery accuracy. Copyright © 2013 Elsevier Inc. All rights reserved.
Létourneau, Daniel McNiven, Andrea; Keller, Harald; Wang, An; Amin, Md Nurul; Pearce, Jim; Norrlinger, Bernhard; Jaffray, David A.
2014-12-15
Purpose: High-quality radiation therapy using highly conformal dose distributions and image-guided techniques requires optimum machine delivery performance. In this work, a monitoring system for multileaf collimator (MLC) performance, integrating semiautomated MLC quality control (QC) tests and statistical process control tools, was developed. The MLC performance monitoring system was used for almost a year on two commercially available MLC models. Control charts were used to establish MLC performance and assess test frequency required to achieve a given level of performance. MLC-related interlocks and servicing events were recorded during the monitoring period and were investigated as indicators of MLC performance variations. Methods: The QC test developed as part of the MLC performance monitoring system uses 2D megavoltage images (acquired using an electronic portal imaging device) of 23 fields to determine the location of the leaves with respect to the radiation isocenter. The precision of the MLC performance monitoring QC test and the MLC itself was assessed by detecting the MLC leaf positions on 127 megavoltage images of a static field. After initial calibration, the MLC performance monitoring QC test was performed 3–4 times/week over a period of 10–11 months to monitor positional accuracy of individual leaves for two different MLC models. Analysis of test results was performed using individuals control charts per leaf with control limits computed based on the measurements as well as two sets of specifications of ±0.5 and ±1 mm. Out-of-specification and out-of-control leaves were automatically flagged by the monitoring system and reviewed monthly by physicists. MLC-related interlocks reported by the linear accelerator and servicing events were recorded to help identify potential causes of nonrandom MLC leaf positioning variations. Results: The precision of the MLC performance monitoring QC test and the MLC itself was within ±0.22 mm for most MLC leaves
NASA Astrophysics Data System (ADS)
Gomes, Guilherme J. C.; Vrugt, Jasper A.; Vargas, Eurípedes A.
2016-04-01
The depth to bedrock controls a myriad of processes by influencing subsurface flow paths, erosion rates, soil moisture, and water uptake by plant roots. As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This essay is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high-resolution topographic data, numerical modeling, and Bayesian analysis. Our DTB model builds on the bottom-up control on fresh-bedrock topography hypothesis of Rempe and Dietrich (2014) and includes a mass movement and bedrock-valley morphology term to extent the usefulness and general applicability of the model. We reconcile the DTB model with field observations using Bayesian analysis with the DREAM algorithm. We investigate explicitly the benefits of using spatially distributed parameter values to account implicitly, and in a relatively simple way, for rock mass heterogeneities that are very difficult, if not impossible, to characterize adequately in the field. We illustrate our method using an artificial data set of bedrock depth observations and then evaluate our DTB model with real-world data collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. The posterior mean DTB simulation is shown to be in good agreement with the measured data. The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety.
Is probabilistic bias analysis approximately Bayesian?
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
Phan, Kevin; Xie, Ashleigh; Kumar, Narendra; Wong, Sophia; Medi, Caroline; La Meir, Mark; Yan, Tristan D
2015-08-01
Simplified maze procedures involving radiofrequency, cryoenergy and microwave energy sources have been increasingly utilized for surgical treatment of atrial fibrillation as an alternative to the traditional cut-and-sew approach. In the absence of direct comparisons, a Bayesian network meta-analysis is another alternative to assess the relative effect of different treatments, using indirect evidence. A Bayesian meta-analysis of indirect evidence was performed using 16 published randomized trials identified from 6 databases. Rank probability analysis was used to rank each intervention in terms of their probability of having the best outcome. Sinus rhythm prevalence beyond the 12-month follow-up was similar between the cut-and-sew, microwave and radiofrequency approaches, which were all ranked better than cryoablation (respectively, 39, 36, and 25 vs 1%). The cut-and-sew maze was ranked worst in terms of mortality outcomes compared with microwave, radiofrequency and cryoenergy (2 vs 19, 34, and 24%, respectively). The cut-and-sew maze procedure was associated with significantly lower stroke rates compared with microwave ablation [odds ratio <0.01; 95% confidence interval 0.00, 0.82], and ranked the best in terms of pacemaker requirements compared with microwave, radiofrequency and cryoenergy (81 vs 14, and 1, <0.01% respectively). Bayesian rank probability analysis shows that the cut-and-sew approach is associated with the best outcomes in terms of sinus rhythm prevalence and stroke outcomes, and remains the gold standard approach for AF treatment. Given the limitations of indirect comparison analysis, these results should be viewed with caution and not over-interpreted. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
Bayesian Confirmation and Interpretation.
ERIC Educational Resources Information Center
Ellett, Frederick S., Jr.
1984-01-01
The author briefly characterizes two ways to confirm the empirical part of educational theories: the hypothetico-deductive method and the Bayesian method. It is argued that the Bayesian approach can be justified. (JMK)
Bayesian Confirmation and Interpretation.
ERIC Educational Resources Information Center
Ellett, Frederick S., Jr.
1984-01-01
The author briefly characterizes two ways to confirm the empirical part of educational theories: the hypothetico-deductive method and the Bayesian method. It is argued that the Bayesian approach can be justified. (JMK)
Advanced statistical process control: controlling sub-0.18-μm lithography and other processes
NASA Astrophysics Data System (ADS)
Zeidler, Amit; Veenstra, Klaas-Jelle; Zavecz, Terrence E.
2001-08-01
access of the analysis to include the external variables involved in CMP, deposition etc. We then applied yield analysis methods to identify the significant lithography-external process variables from the history of lots, subsequently adding the identified process variable to the signatures database and to the PPC calculations. With these improvements, the authors anticipate a 50% improvement of the process window. This improvement results in a significant reduction of rework and improved yield depending on process demands and equipment configuration. A statistical theory that explains the PPC is then presented. This theory can be used to simulate a general PPC application. In conclusion, the PPC concept is not lithography or semiconductors limited. In fact it is applicable for any production process that is signature biased (chemical industry, car industry, .). Requirements for the PPC are large data collection, a controllable process that is not too expensive to tune the process for every lot, and the ability to employ feedback calculations. PPC is a major change in the process management approach and therefor will first be employed where the need is high and the return on investment is very fast. The best industry to start with is the semiconductors and the most likely process area to start with is lithography.
Bayesian Logical Data Analysis for the Physical Sciences
NASA Astrophysics Data System (ADS)
Gregory, Phil
2010-05-01
Preface; Acknowledgements; 1. Role of probability theory in science; 2. Probability theory as extended logic; 3. The how-to of Bayesian inference; 4. Assigning probabilities; 5. Frequentist statistical inference; 6. What is a statistic?; 7. Frequentist hypothesis testing; 8. Maximum entropy probabilities; 9. Bayesian inference (Gaussian errors); 10. Linear model fitting (Gaussian errors); 11. Nonlinear model fitting; 12. Markov Chain Monte Carlo; 13. Bayesian spectral analysis; 14. Bayesian inference (Poisson sampling); Appendix A. Singular value decomposition; Appendix B. Discrete Fourier transforms; Appendix C. Difference in two samples; Appendix D. Poisson ON/OFF details; Appendix E. Multivariate Gaussian from maximum entropy; References; Index.
ERIC Educational Resources Information Center
Hantula, Donald A.
1995-01-01
Clinical applications of statistical process control (SPC) in human service organizations are considered. SPC is seen as providing a standard set of criteria that serves as a common interface for data-based decision making, which may bring decision making under the control of established contingencies rather than the immediate contingencies of…
An Automated Statistical Process Control Study of Inline Mixing Using Spectrophotometric Detection
ERIC Educational Resources Information Center
Dickey, Michael D.; Stewart, Michael D.; Willson, C. Grant
2006-01-01
An experiment is described, which is designed for a junior-level chemical engineering "fundamentals of measurements and data analysis" course, where students are introduced to the concept of statistical process control (SPC) through a simple inline mixing experiment. The students learn how to create and analyze control charts in an effort to…
An Automated Statistical Process Control Study of Inline Mixing Using Spectrophotometric Detection
ERIC Educational Resources Information Center
Dickey, Michael D.; Stewart, Michael D.; Willson, C. Grant
2006-01-01
An experiment is described, which is designed for a junior-level chemical engineering "fundamentals of measurements and data analysis" course, where students are introduced to the concept of statistical process control (SPC) through a simple inline mixing experiment. The students learn how to create and analyze control charts in an effort to…
Reducing lumber thickness variation using real-time statistical process control
Thomas M. Young; Brian H. Bond; Jan Wiedenbeck
2002-01-01
A technology feasibility study for reducing lumber thickness variation was conducted from April 2001 until March 2002 at two sawmills located in the southern U.S. A real-time statistical process control (SPC) system was developed that featured Wonderware human machine interface technology (HMI) with distributed real-time control charts for all sawing centers and...
ERIC Educational Resources Information Center
Widiana, I. Wayan; Jampel, I. Nyoman
2016-01-01
This study aimed to find out the effect of learning model and form of assessment toward inferential statistical achievement after controlling numeric thinking skills. This study was quasi experimental study with 130 students as the sample. The data analysis used ANCOVA. After controlling numeric thinking skills, the result of this study show that:…
A practical statistical quality control scheme for the industrial hygiene chemistry laboratory.
Burkart, J A; Eggenberger, L M; Nelson, J H; Nicholson, P R
1984-06-01
A computerized statistical quality control system has been developed for use in the industrial hygiene chemistry laboratory. The system is practical and sufficiently flexible to allow for multiple analytes, concentrations, replicate sizes and sample types. The computerized system provides an immediate evaluation of the quality of analytical results and produces automatically simple but informative accuracy and precision quality control charts.
Liu, Na; Li, Jun; Li, Bao-Guo
2014-11-01
The study of quality control of Chinese medicine has always been the hot and the difficulty spot of the development of traditional Chinese medicine (TCM), which is also one of the key problems restricting the modernization and internationalization of Chinese medicine. Multivariate statistical analysis is an analytical method which is suitable for the analysis of characteristics of TCM. It has been used widely in the study of quality control of TCM. Multivariate Statistical analysis was used for multivariate indicators and variables that appeared in the study of quality control and had certain correlation between each other, to find out the hidden law or the relationship between the data can be found,.which could apply to serve the decision-making and realize the effective quality evaluation of TCM. In this paper, the application of multivariate statistical analysis in the quality control of Chinese medicine was summarized, which could provided the basis for its further study.
Bayesian analysis for kaon photoproduction
Marsainy, T. Mart, T.
2014-09-25
We have investigated contribution of the nucleon resonances in the kaon photoproduction process by using an established statistical decision making method, i.e. the Bayesian method. This method does not only evaluate the model over its entire parameter space, but also takes the prior information and experimental data into account. The result indicates that certain resonances have larger probabilities to contribute to the process.
Bayesian classification theory
NASA Technical Reports Server (NTRS)
Hanson, Robin; Stutz, John; Cheeseman, Peter
1991-01-01
The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.
Bayesian structural equation modeling in sport and exercise psychology.
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.
Down, P M; Bradley, A J; Breen, J E; Browne, W J; Kypraios, T; Green, M J
2016-10-01
Importance of the dry period with respect to mastitis control is now well established although the precise interventions that reduce the risk of acquiring intramammary infections during this time are not clearly understood. There are very few intervention studies that have measured the clinical efficacy of specific mastitis interventions within a cost-effectiveness framework so there remains a large degree of uncertainty about the impact of a specific intervention and its costeffectiveness. The aim of this study was to use a Bayesian framework to investigate the cost-effectiveness of mastitis controls during the dry period. Data were assimilated from 77 UK dairy farms that participated in a British national mastitis control programme during 2009-2012 in which the majority of intramammary infections were acquired during the dry period. The data consisted of clinical mastitis (CM) and somatic cell count (SCC) records, herd management practices and details of interventions that were implemented by the farmer as part of the control plan. The outcomes used to measure the effectiveness of the interventions were i) changes in the incidence rate of clinical mastitis during the first 30days after calving and ii) the rate at which cows gained new infections during the dry period (measured by SCC changes across the dry period from <200,000cells/ml to >200,000cells/ml). A Bayesian one-step microsimulation model was constructed such that posterior predictions from the model incorporated uncertainty in all parameters. The incremental net benefit was calculated across 10,000 Markov chain Monte Carlo iterations, to estimate the cost-benefit (and associated uncertainty) of each mastitis intervention. Interventions identified as being cost-effective in most circumstances included selecting dry-cow therapy at the cow level, dry-cow rations formulated by a qualified nutritionist, use of individual calving pens, first milking cows within 24h of calving and spreading bedding evenly in
Statistical issues in quality control of proteomic analyses: good experimental design and planning.
Cairns, David A
2011-03-01
Quality control is becoming increasingly important in proteomic investigations as experiments become more multivariate and quantitative. Quality control applies to all stages of an investigation and statistics can play a key role. In this review, the role of statistical ideas in the design and planning of an investigation is described. This involves the design of unbiased experiments using key concepts from statistical experimental design, the understanding of the biological and analytical variation in a system using variance components analysis and the determination of a required sample size to perform a statistically powerful investigation. These concepts are described through simple examples and an example data set from a 2-D DIGE pilot experiment. Each of these concepts can prove useful in producing better and more reproducible data. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Use of a programmable desk-top calculator for the statistical quality control of radioimmunoassays.
Cernosek, S F; Gutierrez-Cernosek, R M
1978-07-01
We have developed an interactive statistical quality-control system for the small- to medium-sized radioimmunoassay laboratory, which can be used in a programmable desk-top calculator instead of the medium- or large-scale computer systems usually required. The design of this quality-control system is modeled after the suggestions of Rodbard and has three components. The first component evaluates the relationship between the measured response variable of the radioimmunoassay and the precision (or variance) of these measurements. This derived relationship is then used in the second component of the system as the basis for the weighting function used to calculate an interative, weighted, least squares regression of the logit-log transformation of the dose-response curve. The third component uses the quality-control parameters statistically calculated from the linearized dose-response curve to monitor whether the assay is "in-control". The calculator tabulates the means and confidence limits for the various parameters and can plot the statistical quality-control charts. The major benefit of this statistical quality-control system is that it allows the real-time computation and plotting of quality-control data with a programmable desk-top calculator.
Federal Register 2010, 2011, 2012, 2013, 2014
2010-02-08
... thoughts on the appropriate use of Bayesian statistical methods in the design and analysis of medical... outlines FDA's current thinking on the use of Bayesian statistical methods in medical device clinical... guidance represents the agency's current thinking on ``Guidance for the Use of Bayesian Statistics...
A Primer on Bayesian Analysis for Experimental Psychopathologists.
Krypotos, Angelos-Miltiadis; Blanken, Tessa F; Arnaudova, Inna; Matzke, Dora; Beckers, Tom
2017-01-01
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.
Bayesian approach to noninferiority trials for proportions.
Gamalo, Mark A; Wu, Rui; Tiwari, Ram C
2011-09-01
Noninferiority trials are unique because they are dependent upon historical information in order to make meaningful interpretation of their results. Hence, a direct application of the Bayesian paradigm in sequential learning becomes apparently useful in the analysis. This paper describes a Bayesian procedure for testing noninferiority in two-arm studies with a binary primary endpoint that allows the incorporation of historical data on an active control via the use of informative priors. In particular, the posteriors of the response in historical trials are assumed as priors for its corresponding parameters in the current trial, where that treatment serves as the active control. The Bayesian procedure includes a fully Bayesian method and two normal approximation methods on the prior and/or on the posterior distributions. Then a common Bayesian decision criterion is used but with two prespecified cutoff levels, one for the approximation methods and the other for the fully Bayesian method, to determine whether the experimental treatment is noninferior to the active control. This criterion is evaluated and compared with the frequentist method using simulation studies in keeping with regulatory framework that new methods must protect type I error and arrive at a similar conclusion with existing standard strategies. Results show that both methods arrive at comparable conclusions of noninferiority when applied to a modified real data set. The advantage of the proposed Bayesian approach lies in its ability to provide posterior probabilities for effect sizes of the experimental treatment over the active control.
Ungvári, Ildikó; Hullám, Gábor; Antal, Péter; Kiszel, Petra Sz; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba
2012-01-01
Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA). This method uses bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated.With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2-1.8); p = 3×10(-4)). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics.In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.
NASA Technical Reports Server (NTRS)
Shewhart, Mark
1991-01-01
Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.
Chmielewski, R; Abbas, A; Yeung, I
2012-07-01
The objective of this work was to create a comprehensive online tool to evaluate and review the performance of quality assurance measurements that assess beam output and profile constancy as soon as they are acquired using statistical process control. As part of routine quality assurance: output, flatness and symmetry measurements are acquired daily and weekly with DQA3 and the Matrix and symmetry and flatness are acquired on a monthly basis with Profiler2. An individuals control chart and a moving range control chart was plotted for each set of data. Upper and lower control limits were calculated using measurements acquired during a several month period when the linear accelerators were operating optimally. The existing action levels, established according to TG142 and CAPCA guidelines were compared with the calculated statistical control limits. Tighter tolerance limits were recommended for output, symmetry and flatness Matrix measurements and DQA3 flatness measurements. © 2012 American Association of Physicists in Medicine.
Saulnier, George E; Castro, Janna C; Cook, Curtiss B
2014-05-01
Glucose control can be problematic in critically ill patients. We evaluated the impact of statistical transformation on interpretation of intensive care unit inpatient glucose control data. Point-of-care blood glucose (POC-BG) data derived from patients in the intensive care unit for 2011 was obtained. Box-Cox transformation of POC-BG measurements was performed, and distribution of data was determined before and after transformation. Different data subsets were used to establish statistical upper and lower control limits. Exponentially weighted moving average (EWMA) control charts constructed from April, October, and November data determined whether out-of-control events could be identified differently in transformed versus nontransformed data. A total of 8679 POC-BG values were analyzed. POC-BG distributions in nontransformed data were skewed but approached normality after transformation. EWMA control charts revealed differences in projected detection of out-of-control events. In April, an out-of-control process resulting in the lower control limit being exceeded was identified at sample 116 in nontransformed data but not in transformed data. October transformed data detected an out-of-control process exceeding the upper control limit at sample 27 that was not detected in nontransformed data. Nontransformed November results remained in control, but transformation identified an out-of-control event less than 10 samples into the observation period. Using statistical methods to assess population-based glucose control in the intensive care unit could alter conclusions about the effectiveness of care processes for managing hyperglycemia. Further study is required to determine whether transformed versus nontransformed data change clinical decisions about the interpretation of care or intervention results. © 2014 Diabetes Technology Society.
Saulnier, George E.; Castro, Janna C.
2014-01-01
Glucose control can be problematic in critically ill patients. We evaluated the impact of statistical transformation on interpretation of intensive care unit inpatient glucose control data. Point-of-care blood glucose (POC-BG) data derived from patients in the intensive care unit for 2011 was obtained. Box–Cox transformation of POC-BG measurements was performed, and distribution of data was determined before and after transformation. Different data subsets were used to establish statistical upper and lower control limits. Exponentially weighted moving average (EWMA) control charts constructed from April, October, and November data determined whether out-of-control events could be identified differently in transformed versus nontransformed data. A total of 8679 POC-BG values were analyzed. POC-BG distributions in nontransformed data were skewed but approached normality after transformation. EWMA control charts revealed differences in projected detection of out-of-control events. In April, an out-of-control process resulting in the lower control limit being exceeded was identified at sample 116 in nontransformed data but not in transformed data. October transformed data detected an out-of-control process exceeding the upper control limit at sample 27 that was not detected in nontransformed data. Nontransformed November results remained in control, but transformation identified an out-of-control event less than 10 samples into the observation period. Using statistical methods to assess population-based glucose control in the intensive care unit could alter conclusions about the effectiveness of care processes for managing hyperglycemia. Further study is required to determine whether transformed versus nontransformed data change clinical decisions about the interpretation of care or intervention results. PMID:24876620
NASA Astrophysics Data System (ADS)
Kim, Seongryong; Tkalčić, Hrvoje; Rhie, Junkee; Chen, Youlin
2016-08-01
Intraplate volcanism adjacent to active continental margins is not simply explained by plate tectonics or plume interaction. Recent volcanoes in northeast (NE) Asia, including NE China and the Korean Peninsula, are characterized by heterogeneous tectonic structures and geochemical compositions. Here we apply a transdimensional Bayesian tomography to estimate high-resolution images of group and phase velocity variations (with periods between 8 and 70 s). The method provides robust estimations of velocity maps, and the reliability of results is tested through carefully designed synthetic recovery experiments. Our maps reveal two sublithospheric low-velocity anomalies that connect back-arc regions (in Japan and Ryukyu Trench) with current margins of continental lithosphere where the volcanoes are distributed. Combined with evidences from previous geochemical and geophysical studies, we argue that the volcanoes are related to the low-velocity structures associated with back-arc processes and preexisting continental lithosphere.
Matched case-control studies: a review of reported statistical methodology.
Niven, Daniel J; Berthiaume, Luc R; Fick, Gordon H; Laupland, Kevin B
2012-01-01
Case-control studies are a common and efficient means of studying rare diseases or illnesses with long latency periods. Matching of cases and controls is frequently employed to control the effects of known potential confounding variables. The analysis of matched data requires specific statistical methods. The objective of this study was to determine the proportion of published, peer-reviewed matched case-control studies that used statistical methods appropriate for matched data. Using a comprehensive set of search criteria we identified 37 matched case-control studies for detailed analysis. Among these 37 articles, only 16 studies were analyzed with proper statistical techniques (43%). Studies that were properly analyzed were more likely to have included case patients with cancer and cardiovascular disease compared to those that did not use proper statistics (10/16 or 63%, versus 5/21 or 24%, P = 0.02). They were also more likely to have matched multiple controls for each case (14/16 or 88%, versus 13/21 or 62%, P = 0.08). In addition, studies with properly analyzed data were more likely to have been published in a journal with an impact factor listed in the top 100 according to the Journal Citation Reports index (12/16 or 69%, versus 1/21 or 5%, P ≤ 0.0001). The findings of this study raise concern that the majority of matched case-control studies report results that are derived from improper statistical analyses. This may lead to errors in estimating the relationship between a disease and exposure, as well as the incorrect adaptation of emerging medical literature.
Matched case-control studies: a review of reported statistical methodology
Niven, Daniel J; Berthiaume, Luc R; Fick, Gordon H; Laupland, Kevin B
2012-01-01
Background Case-control studies are a common and efficient means of studying rare diseases or illnesses with long latency periods. Matching of cases and controls is frequently employed to control the effects of known potential confounding variables. The analysis of matched data requires specific statistical methods. Methods The objective of this study was to determine the proportion of published, peer-reviewed matched case-control studies that used statistical methods appropriate for matched data. Using a comprehensive set of search criteria we identified 37 matched case-control studies for detailed analysis. Results Among these 37 articles, only 16 studies were analyzed with proper statistical techniques (43%). Studies that were properly analyzed were more likely to have included case patients with cancer and cardiovascular disease compared to those that did not use proper statistics (10/16 or 63%, versus 5/21 or 24%, P = 0.02). They were also more likely to have matched multiple controls for each case (14/16 or 88%, versus 13/21 or 62%, P = 0.08). In addition, studies with properly analyzed data were more likely to have been published in a journal with an impact factor listed in the top 100 according to the Journal Citation Reports index (12/16 or 69%, versus 1/21 or 5%, P ≤ 0.0001). Conclusion The findings of this study raise concern that the majority of matched case-control studies report results that are derived from improper statistical analyses. This may lead to errors in estimating the relationship between a disease and exposure, as well as the incorrect adaptation of emerging medical literature. PMID:22570570
Detecting Exoplanets using Bayesian Object Detection
NASA Astrophysics Data System (ADS)
Feroz, Farhan
2015-08-01
Detecting objects from noisy data-sets is common practice in astrophysics. Object detection presents a particular challenge in terms of statistical inference, not only because of its multi-modal nature but also because it combines both the parameter estimation (for characterizing objects) and model selection problems (in order to quantify the detection). Bayesian inference provides a mathematically rigorous solution to this problem by calculating marginal posterior probabilities of models with different number of sources, but the use of this method in astrophysics has been hampered by the computational cost of evaluating the Bayesian evidence. Nonetheless, Bayesian model selection has the potential to improve the interpretation of existing observational data. I will discuss several Bayesian approaches to object detection problems, both in terms of their theoretical framework and also the practical details about carrying out the computation. I will also describe some recent applications of these methods in the detection of exoplanets.
NASA Astrophysics Data System (ADS)
Granderson, Jessica Ann
2007-12-01
The need for sustainable, efficient energy systems is the motivation that drove this research, which targeted the design of an intelligent commercial lighting system. Lighting in commercial buildings consumes approximately 13% of all the electricity generated in the US. Advanced lighting controls1 intended for use in commercial office spaces have proven to save up to 45% in electricity consumption. However, they currently comprise only a fraction of the market share, resulting in a missed opportunity to conserve energy. The research goals driving this dissertation relate directly to barriers hindering widespread adoption---increase user satisfaction, and provide increased energy savings through more sophisticated control. To satisfy these goals an influence diagram was developed to perform daylighting actuation. This algorithm was designed to balance the potentially conflicting lighting preferences of building occupants, with the efficiency desires of building facilities management. A supervisory control policy was designed to implement load shedding under a demand response tariff. Such tariffs offer incentives for customers to reduce their consumption during periods of peak demand, trough price reductions. In developing the value function occupant user testing was conducted to determine that computer and paper tasks require different illuminance levels, and that user preferences are sufficiently consistent to attain statistical significance. Approximately ten facilities managers were also interviewed and surveyed to isolate their lighting preferences with respect to measures of lighting quality and energy savings. Results from both simulation and physical implementation and user testing indicate that the intelligent controller can increase occupant satisfaction, efficiency, cost savings, and management satisfaction, with respect to existing commercial daylighting systems. Several important contributions were realized by satisfying the research goals. A general
ERIC Educational Resources Information Center
Averitt, Sallie D.
This instructor guide, which was developed for use in a manufacturing firm's advanced technical preparation program, contains the materials required to present a learning module that is designed to prepare trainees for the program's statistical process control module by improving their basic math skills and instructing them in basic calculator…
ERIC Educational Resources Information Center
Billings, Paul H.
This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 6-hour introductory module on statistical process control (SPC), designed to develop competencies in the following skill areas: (1) identification of the three classes of SPC use; (2) understanding a process and how it works; (3)…
Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes
Jorge F. Perez-Quezada; Nicanor Z. Saliendra; William E. Emmerich; Emilio A. Laca
2007-01-01
The process of quality control of micrometeorological and carbon dioxide (CO2) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO2 flux...
Optimal Mass Transport for Statistical Estimation, Image Analysis, Information Geometry, and Control
2017-01-10
years has increasingly impacted a large number of other fields (probability theory, partial differential equations, physics, meteorology ). We have...in recent years has increasingly impacted a large number of other fields (probability theory, partial differential equations, physics, meteorology ...econometrics, fluid dynamics, automatic control, transportation, statistical physics, shape optimization, expert systems, and meteorology [52, 68]. The problem
ERIC Educational Resources Information Center
Briere, John; Elliott, Diana M.
1993-01-01
Responds to article in which Nash et al. reported on effects of controlling for family environment when studying sexual abuse sequelae. Considers findings in terms of theoretical and statistical constraints placed on analysis of covariance and other partializing procedures. Questions use of covariate techniques to test hypotheses about causal role…
ERIC Educational Resources Information Center
Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark; Menzies, Ross; Packman, Ann
2013-01-01
Purpose: Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method: Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech…
Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)
NASA Technical Reports Server (NTRS)
Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)
1999-01-01
This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.
Total Quality Management: Statistics and Graphics II-Control Charts. AIR 1992 Annual Forum Paper.
ERIC Educational Resources Information Center
Cherland, Ryan M.
An examination was conducted of the control chart as a quality improvement statistical method often used by Total Quality Management (TQM) practitioners in higher education. The examination used an example based on actual requests for information gathered for the Director of Human Resources at a medical center at a midwestern university. The…
Monitoring Book Reshelving in Libraries Using Statistical Sampling and Control Charts.
ERIC Educational Resources Information Center
Edwardy, Jeffrey M.; Pontius, Jeffrey S.
2001-01-01
Discussion of shelf reading as a method to maintain library books in their proper location focuses on a statistical approach to determine when shelf reading is necessary. Uses sampling to obtain data on misshelved books over time and develop a control chart to assess when shelf reading is necessary. (Author/LRW)
Project T.E.A.M. (Technical Education Advancement Modules). Advanced Statistical Process Control.
ERIC Educational Resources Information Center
Dunlap, Dale
This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 20-hour advanced statistical process control (SPC) and quality improvement course designed to develop the following competencies: (1) understanding quality systems; (2) knowing the process; (3) solving quality problems; and (4)…
ERIC Educational Resources Information Center
Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark; Menzies, Ross; Packman, Ann
2013-01-01
Purpose: Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method: Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech…
ERIC Educational Resources Information Center
Logue, Alexandra W.; Watanabe-Rose, Mari
2014-01-01
This study used a randomized controlled trial to determine whether students, assessed by their community colleges as needing an elementary algebra (remedial) mathematics course, could instead succeed at least as well in a college-level, credit-bearing introductory statistics course with extra support (a weekly workshop). Researchers randomly…
Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)
NASA Technical Reports Server (NTRS)
Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)
1999-01-01
This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.
ERIC Educational Resources Information Center
Briere, John; Elliott, Diana M.
1993-01-01
Responds to article in which Nash et al. reported on effects of controlling for family environment when studying sexual abuse sequelae. Considers findings in terms of theoretical and statistical constraints placed on analysis of covariance and other partializing procedures. Questions use of covariate techniques to test hypotheses about causal role…
Bayesian Inference on Proportional Elections
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Implementation of Statistical Process Control for Proteomic Experiments via LC MS/MS
Bereman, Michael S.; Johnson, Richard; Bollinger, James; Boss, Yuval; Shulman, Nick; MacLean, Brendan; Hoofnagle, Andrew N.; MacCoss, Michael J.
2014-01-01
Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution); and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies. PMID:24496601
NASA Astrophysics Data System (ADS)
Belt, John Q.; Rice, Gary K.
2002-02-01
There are four major quality control measures that can apply to geochemical petroleum exploration data: statistical quality control charts, data reproducibility-Juran approach, ethane composition index, and hydrocarbon cross plots. Statistical quality control, or SQC, charts reflect the quality-performance of the analytical process composed of equipment, instrumentation, and operator technique. An unstable process is detected through assignable causes using SQC charts. Knowing data variability is paramount to tying geochemical data over time for in-fill samples and/or project extensions. The Juran approach is a statistical tool used to help determine the ability of a process to maintain itself within the limits of set specifications for reproducing geochemical data. Ethane composition index, or ECI, is a statistical calculation based on near-surface, light hydrocarbon measurements that help differentiate thermogenic, petroleum sources at depth. The ECI data are integrated with subsurface geological information, and/or seismic survey data to determine lower-risk drilling locations. Hydrocarbon cross plots are visual correlation techniques that compare two hydrocarbons within a similar hydrocarbon suite (e.g., ethane versus propane, benzene versus toluene, or 2-ring aromatics versus 3-ring aromatics). Cross plots help determine contamination, multiple petroleum sources, and low-quality data versus high-quality data indigenous to different geochemical exploration tools. When integrated with geomorphology, subsurface geology, and seismic survey data high-quality geochemical data provides beneficial information for developing a petroleum exploration model. High-quality data is the key to the successful application of geochemistry in petroleum exploration modeling. The ability to produce high-quality, geochemical data requires the application of quality control measures reflective of a well managed ISO 9000 quality system. Statistical quality control charts, Juran
Howard Stauffer; Nadav Nur
2005-01-01
The papers included in the Advances in Statistics section of the Partners in Flight (PIF) 2002 Proceedings represent a small sample of statistical topics of current importance to Partners In Flight research scientists: hierarchical modeling, estimation of detection probabilities, and Bayesian applications. Sauer et al. (this volume) examines a hierarchical model...
Abdel Wahab, Moataza M; Nofal, Laila M; Guirguis, Wafaa W; Mahdy, Nehad H
2004-01-01
Quality control is the application of statistical techniques to a process in an effort to identify and minimize both random and non-random sources of variation. The present study aimed at the application of Statistical Process Control (SPC) to analyze the referrals by General Practitioners (GP) at Health Insurance Organization (HIO) clinics in Alexandria. Retrospective analysis of records and cross sectional interview to 180 GPs were done. Using the control charts (p chart), the present study confirmed the presence of substantial variation in referral rates from GPs to specialists; more than 60% of variation was of the special cause, which revealed that the process of referral in Alexandria (HIO) was completely out of statistical control. Control charts for referrals by GPs classified by different GP characteristics or organizational factors revealed much variation, which suggested that the variation was at the level of individual GPs. Furthermore, the p chart for each GP separately; which yielded a fewer number of points out of control (outliers), with an average of 4 points. For 26 GPs, there was no points out of control, those GPs were slightly older than those having points out of control. Otherwise, there was no significant difference between them. The revised p chart for those 26 GPs together yielded a centerline of 9.7%, upper control limit of 12.0% and lower control limit of 7.4%. Those limits were in good agreement with the limits specified by HIO; they can be suggested to be the new specification limits after some training programs.
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…
A Bayesian Analysis of Finite Mixtures in the LISREL Model.
ERIC Educational Resources Information Center
Zhu, Hong-Tu; Lee, Sik-Yum
2001-01-01
Proposes a Bayesian framework for estimating finite mixtures of the LISREL model. The model augments the observed data of the manifest variables with the latent variables and allocation variables and uses the Gibbs sampler to obtain the Bayesian solution. Discusses other associated statistical inferences. (SLD)
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…
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…
NASA Astrophysics Data System (ADS)
Hobson, Michael P.; Jaffe, Andrew H.; Liddle, Andrew R.; Mukherjee, Pia; Parkinson, David
2009-12-01
Preface; Part I. Methods: 1. Foundations and algorithms John Skilling; 2. Simple applications of Bayesian methods D. S. Sivia and Steve Rawlings; 3. Parameter estimation using Monte Carlo sampling Antony Lewis and Sarah Bridle; 4. Model selection and multi-model interference Andrew R. Liddle, Pia Mukherjee and David Parkinson; 5. Bayesian experimental design and model selection forecasting Roberto Trotta, Martin Kunz, Pia Mukherjee and David Parkinson; 6. Signal separation in cosmology M. P. Hobson, M. A. J. Ashdown and V. Stolyarov; Part II. Applications: 7. Bayesian source extraction M. P. Hobson, Graça Rocha and R. Savage; 8. Flux measurement Daniel Mortlock; 9. Gravitational wave astronomy Neil Cornish; 10. Bayesian analysis of cosmic microwave background data Andrew H. Jaffe; 11. Bayesian multilevel modelling of cosmological populations Thomas J. Loredo and Martin A. Hendry; 12. A Bayesian approach to galaxy evolution studies Stefano Andreon; 13. Photometric redshift estimation: methods and applications Ofer Lahav, Filipe B. Abdalla and Manda Banerji; Index.
NASA Astrophysics Data System (ADS)
Hobson, Michael P.; Jaffe, Andrew H.; Liddle, Andrew R.; Mukherjee, Pia; Parkinson, David
2014-02-01
Preface; Part I. Methods: 1. Foundations and algorithms John Skilling; 2. Simple applications of Bayesian methods D. S. Sivia and Steve Rawlings; 3. Parameter estimation using Monte Carlo sampling Antony Lewis and Sarah Bridle; 4. Model selection and multi-model interference Andrew R. Liddle, Pia Mukherjee and David Parkinson; 5. Bayesian experimental design and model selection forecasting Roberto Trotta, Martin Kunz, Pia Mukherjee and David Parkinson; 6. Signal separation in cosmology M. P. Hobson, M. A. J. Ashdown and V. Stolyarov; Part II. Applications: 7. Bayesian source extraction M. P. Hobson, Graça Rocha and R. Savage; 8. Flux measurement Daniel Mortlock; 9. Gravitational wave astronomy Neil Cornish; 10. Bayesian analysis of cosmic microwave background data Andrew H. Jaffe; 11. Bayesian multilevel modelling of cosmological populations Thomas J. Loredo and Martin A. Hendry; 12. A Bayesian approach to galaxy evolution studies Stefano Andreon; 13. Photometric redshift estimation: methods and applications Ofer Lahav, Filipe B. Abdalla and Manda Banerji; Index.
Three case studies in the Bayesian analysis of cognitive models.
Lee, Michael D
2008-02-01
Bayesian statistical inference offers a principled and comprehensive approach for relating psychological models to data. This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. In each case, the model is recast as a probabilistic graphical model and is evaluated in relation to a previously considered data set. In each case, it is shown that Bayesian inference is able to provide answers to important theoretical and empirical questions easily and coherently. The generality of the Bayesian approach and its potential for the understanding of models and data in psychology are discussed.
Shanahan, K.L.
1990-09-01
A suite of RS/1 procedures for Shewhart control charting in chemical laboratories is described. The suite uses the RS series product QCA (Quality Control Analysis) for chart construction and analysis. The suite prompts users for data in a user friendly fashion and adds the data to or creates the control charts. All activities are time stamped. Facilities for generating monthly or contiguous time segment summary charts are included. The suite is currently in use at Westinghouse Savannah River Company.
Bayesian sperm competition estimates.
Jones, Beatrix; Clark, Andrew G
2003-01-01
We introduce a Bayesian method for estimating parameters for a model of multiple mating and sperm displacement from genotype counts of brood-structured data. The model is initially targeted for Drosophila melanogaster, but is easily adapted to other organisms. The method is appropriate for use with field studies where the number of mates and the genotypes of the mates cannot be controlled, but where unlinked markers have been collected for a set of females and a sample of their offspring. Advantages over previous approaches include full use of multilocus information and the ability to cope appropriately with missing data and ambiguities about which alleles are maternally vs. paternally inherited. The advantages of including X-linked markers are also demonstrated. PMID:12663555
Li, Jin-Na; Er, Meng-Joo; Tan, Yen-Kheng; Yu, Hai-Bin; Zeng, Peng
2015-09-01
This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.
Tsiamyrtzis, Panagiotis; Sobas, Frédéric; Négrier, Claude
2015-07-01
The present study seeks to demonstrate the feasibility of avoiding the preliminary phase, which is mandatory in all conventional approaches for internal quality control (IQC) management. Apart from savings on the resources consumed by the preliminary phase, the alternative approach described here is able to detect any analytic problems during the startup and provide a foundation for subsequent conventional assessment. A new dynamically updated predictive control chart (PCC) is used. Being Bayesian in concept, it utilizes available prior information. The manufacturer's prior quality control target value, the manufacturer's maximum acceptable interassay coefficient of variation value and the interassay standard deviation value defined during method validation in each laboratory, allow online IQC management. An Excel template, downloadable from journal website, allows easy implementation of this alternative approach in any laboratory. In the practical case of prothrombin percentage measurement, PCC gave no false alarms with respect to the 1ks rule (with same 5% false-alarm probability on a single control sample) during an overlap phase between two IQC batches. Moreover, PCCs were as effective as the 1ks rule in detecting increases in both random and systematic error after the minimal preliminary phase required by medical biology guidelines. PCCs can improve efficiency in medical biology laboratories.
Empirical Bayesian significance measure of neuronal spike response.
Oba, Shigeyuki; Nakae, Ken; Ikegaya, Yuji; Aki, Shunsuke; Yoshimoto, Junichiro; Ishii, Shin
2016-05-21
Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information. In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors. The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a
An introduction to using Bayesian linear regression with clinical data.
Baldwin, Scott A; Larson, Michael J
2016-12-31
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.
NASA Technical Reports Server (NTRS)
Manning, Robert M.
1990-01-01
A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.
NASA Technical Reports Server (NTRS)
Manning, Robert M.
1990-01-01
A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.
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.
Chen, Zhongxue; Liu, Qingzhong; Nadarajah, Saralees
2012-04-15
As an epigenetic alteration, DNA methylation plays an important role in epigenetic controls of gene transcription. Recent advances in genome-wide scan of DNA methylation provide great opportunities in studying the impact of DNA methylation on many human diseases including various types of cancer. Due to the unique feature of this type of data, applicable statistical methods are limited and new sophisticated approaches are desirable. In this article, we propose a new statistical test to detect differentially methylated loci for case control methylation data generated by Illumina arrays. This new method utilizes the important finding that DNA methylation is highly correlated with age. The proposed method estimates the overall P-value by combining the P-values from independent individual tests each for one age group. Through real data application and simulation study, we show that the proposed test is robust and usually more powerful than other methods.
Bayesian second law of thermodynamics
NASA Astrophysics Data System (ADS)
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as Δ H (ρm,ρ ) + F |m≥0 , where Δ H (ρm,ρ ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρm and
F |m is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Bayesian second law of thermodynamics.
Bartolotta, Anthony; Carroll, Sean M; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as ΔH(ρ_{m},ρ)+〈Q〉_{F|m}≥0, where ΔH(ρ_{m},ρ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρ_{m} and 〈Q〉_{F|m} is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Whose statistical reasoning is facilitated by a causal structure intervention?
McNair, Simon; Feeney, Aidan
2015-02-01
People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430-450, 2007) proposed that a causal Bayesian framework accounts for peoples' errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.
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…
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…
The use of statistical process control to improve the accuracy of turning
NASA Astrophysics Data System (ADS)
Pisarciuc, Cristian
2016-11-01
The present work deals with the turning process improvement using means of statistical process control. The approach on improvement is related to the fact that several methods are used in order to achieve quality defined by technical specifications. The experimental data is collected during identical and successive manufacturing processes of turning of an electrical motor shaft. The initial process presents some difficulties because many machined parts are nonconforming as a consequence of reduced precision of turning. The article is using data collected in turning process, presented through histograms and control charts, to improve the accuracy in order to reduce scrap.
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.
Bayesian Inference of Galaxy Morphology
NASA Astrophysics Data System (ADS)
Yoon, Ilsang; Weinberg, M.; Katz, N.
2011-01-01
Reliable inference on galaxy morphology from quantitative analysis of ensemble galaxy images is challenging but essential ingredient in studying galaxy formation and evolution, utilizing current and forthcoming large scale surveys. To put galaxy image decomposition problem in broader context of statistical inference problem and derive a rigorous statistical confidence levels of the inference, I developed a novel galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes) that exploits recent developments in Bayesian computation to provide full posterior probability distributions and reliable confidence intervals for all parameters. I will highlight the significant improvements in galaxy image decomposition using GALPHAT, over the conventional model fitting algorithms and introduce the GALPHAT potential to infer the statistical distribution of galaxy morphological structures, using ensemble posteriors of galaxy morphological parameters from the entire galaxy population that one studies.
Neural network classification - A Bayesian interpretation
NASA Technical Reports Server (NTRS)
Wan, Eric A.
1990-01-01
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
Automatic Thesaurus Construction Using Bayesian Networks.
ERIC Educational Resources Information Center
Park, Young C.; Choi, Key-Sun
1996-01-01
Discusses automatic thesaurus construction and characterizes the statistical behavior of terms by using an inference network. Highlights include low-frequency terms and data sparseness, Bayesian networks, collocation maps and term similarity, constructing a thesaurus from a collocation map, and experiments with test collections. (Author/LRW)
Approximation for Bayesian Ability Estimation.
1987-02-18
posterior pdfs of ande are given by p(-[Y) p(F) F P((y lei’ j)P )d. SiiJ i (4) a r~d p(e Iy) - p(t0) 1 J i P(Yij ei, (5) As shown in Tsutakawa and Lin...inverse A Hessian of the log of (27) with respect to , evaulatedat a Then, under regularity conditions, the marginal posterior pdf of O is...two-way contingency tables. Journal of Educational Statistics, 11, 33-56. Lindley, D.V. (1980). Approximate Bayesian methods. Trabajos Estadistica , 31
Software For Multivariate Bayesian Classification
NASA Technical Reports Server (NTRS)
Saul, Ronald; Laird, Philip; Shelton, Robert
1996-01-01
PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.
The role of control groups in mutagenicity studies: matching biological and statistical relevance.
Hauschke, Dieter; Hothorn, Torsten; Schäfer, Juliane
2003-06-01
The statistical test of the conventional hypothesis of "no treatment effect" is commonly used in the evaluation of mutagenicity experiments. Failing to reject the hypothesis often leads to the conclusion in favour of safety. The major drawback of this indirect approach is that what is controlled by a prespecified level alpha is the probability of erroneously concluding hazard (producer risk). However, the primary concern of safety assessment is the control of the consumer risk, i.e. limiting the probability of erroneously concluding that a product is safe. In order to restrict this risk, safety has to be formulated as the alternative, and hazard, i.e. the opposite, has to be formulated as the hypothesis. The direct safety approach is examined for the case when the corresponding threshold value is expressed either as a fraction of the population mean for the negative control, or as a fraction of the difference between the positive and negative controls.
Theoretical evaluation of the detectability of random lesions in bayesian emission reconstruction
Qi, Jinyi
2003-05-01
Detecting cancerous lesion is an important task in positron emission tomography (PET). Bayesian methods based on the maximum a posteriori principle (also called penalized maximum likelihood methods) have been developed to deal with the low signal to noise ratio in the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the prior parameters in Bayesian reconstruction control the resolution and noise trade-off and hence affect detectability of lesions in reconstructed images. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Most research has been based on Monte Carlo simulations, which are very time consuming. Building on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers, here we develop a theoretical approach for fast computation of lesion detectability in Bayesian reconstruction. The results can be used to choose the optimum hyperparameter for the maximum lesion detectability. New in this work is the use of theoretical expressions that explicitly model the statistical variation of the lesion and background without assuming that the object variation is (locally) stationary. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predications and the Monte Carlo results.
Statistical Inference: The Big Picture.
Kass, Robert E
2011-02-01
Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction.
NASA Technical Reports Server (NTRS)
Navard, Sharon E.
1989-01-01
In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.
NASA Astrophysics Data System (ADS)
Wang, Ping; Dai, Xin-Gang
2016-09-01
The term "APEC Blue" has been created to describe the clear sky days since the Asia-Pacific Economic Cooperation (APEC) summit held in Beijing during November 5-11, 2014. The duration of the APEC Blue is detected from November 1 to November 14 (hereafter Blue Window) by moving t test in statistics. Observations show that APEC Blue corresponds to low air pollution with respect to PM2.5, PM10, SO2, and NO2 under strict emission-control measures (ECMs) implemented in Beijing and surrounding areas. Quantitative assessment shows that ECM is more effective on reducing aerosols than the chemical constituents. Statistical investigation has revealed that the window also resulted from intensified wind variability, as well as weakened static stability of atmosphere (SSA). The wind and ECMs played key roles in reducing air pollution during November 1-7 and 11-13, and strict ECMs and weak SSA become dominant during November 7-10 under weak wind environment. Moving correlation manifests that the emission reduction for aerosols can increase the apparent wind cleanup effect, leading to significant negative correlations of them, and the period-wise changes in emission rate can be well identified by multi-scale correlations basing on wavelet decomposition. In short, this case study manifests statistically how human interference modified air quality in the mega city through controlling local and surrounding emissions in association with meteorological condition.
NASA Astrophysics Data System (ADS)
Byrd, Gene G.; Byrd, Dana
2017-06-01
The two main purposes of this paper on improving Ay101 courses are presentations of (1) some very effective single changes and (2) a method to improve teaching by making just single changes which are evaluated statistically versus a control group class. We show how simple statistical comparison can be done even with Excel in Windows. Of course, other more sophisticated and powerful methods could be used if available. One of several examples to be discussed on our poster is our modification of an online introductory astronomy lab course evaluated by the multiple choice final exam. We composed questions related to the learning objectives of the course modules (LOQs). Students could “talk to themselves” by discursively answering these for extra credit prior to the final. Results were compared to an otherwise identical previous unmodified class. Modified classes showed statistically much better final exam average scores (78% vs. 66%). This modification helped those students who most need help. Students in the lower third of the class preferentially answered the LOQs to improve their scores and the class average on the exam. These results also show the effectiveness of relevant extra credit work. Other examples will be discussed as specific examples of evaluating improvement by making one change and then testing it versus a control. Essentially, this is an evolutionary approach in which single favorable “mutations” are retained and the unfavorable removed. The temptation to make more than one change each time must be resisted!
Valid statistical inference methods for a case-control study with missing data.
Tian, Guo-Liang; Zhang, Chi; Jiang, Xuejun
2016-05-19
The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.
Cheung, Yvonne Y; Jung, Boyoun; Sohn, Jae Ho; Ogrinc, Greg
2012-01-01
Quality improvement (QI) projects are an integral part of today's radiology practice, helping identify opportunities for improving outcomes by refining work processes. QI projects are typically driven by outcome measures, but the data can be difficult to interpret: The numbers tend to fluctuate even before a process is altered, and after a QI intervention takes place, it may be even more difficult to determine the cause of such vacillations. Control chart analysis helps the QI project team identify variations that should be targeted for intervention and avoid tampering in processes in which variation is random or harmless. Statistical control charts make it possible to distinguish among random variation or noise in the data, outlying tendencies that should be targeted for future intervention, and changes that signify the success of previous intervention. The data on control charts are plotted over time and integrated with various graphic devices that represent statistical reasoning (eg, control limits) to allow visualization of the intensity and overall effect-negative or positive-of variability. Even when variability has no substantial negative effect, appropriate intervention based on the results of control chart analysis can help increase the efficiency of a process by optimizing the central tendency of the outcome measure. Different types of control charts may be used to analyze the same outcome dataset: For example, paired charts of individual values (x) and the moving range (mR) allow robust and reliable analyses of most types of data from radiology QI projects. Many spreadsheet programs and templates are available for use in creating x-mR charts and other types of control charts.
Pedestrian dynamics via Bayesian networks
NASA Astrophysics Data System (ADS)
Venkat, Ibrahim; Khader, Ahamad Tajudin; Subramanian, K. G.
2014-06-01
Studies on pedestrian dynamics have vital applications in crowd control management relevant to organizing safer large scale gatherings including pilgrimages. Reasoning pedestrian motion via computational intelligence techniques could be posed as a potential research problem within the realms of Artificial Intelligence. In this contribution, we propose a "Bayesian Network Model for Pedestrian Dynamics" (BNMPD) to reason the vast uncertainty imposed by pedestrian motion. With reference to key findings from literature which include simulation studies, we systematically identify: What are the various factors that could contribute to the prediction of crowd flow status? The proposed model unifies these factors in a cohesive manner using Bayesian Networks (BNs) and serves as a sophisticated probabilistic tool to simulate vital cause and effect relationships entailed in the pedestrian domain.
Does quality control of death certificates in hospitals have an impact on cause of death statistics?
Alfsen, G Cecilie; Lyckander, Lars Gustav
2013-04-09
The effects of inaccurate death certificates on cause of death statistics are uncertain. Since 2008, Akershus University Hospital has systematically corrected all death certificates. The effects of these corrections on the total cause of death statistics from the hospital were studied. ICD-10 codes for the underlying cause of death on the original and the corrected death certificates issued by Akershus University Hospital were retrieved from the Cause of Death Registry for the period 1 May 2008-31 December 2009, once the Cause of Death Registry had processed the death certificates with the aid of the computer program ACME (Automatic Classification of Medical Entities). Altogether 1,001 deaths were investigated (547 men and 454 women). A total of 223 death certificates were corrected. This entailed changing the underlying cause of death in 176 cases. Death certificates for women were corrected most frequently. In 121 cases, the changes entailed a change of disease chapter in ICD-10. The corrections caused a significant reduction in the number of unspecific diagnoses, such as sepsis, cardiac arrest, pneumonia with no further specification, renal failure and fractures without any specific cause. There was a significant exchange of individuals within all the large diagnostic groups, with the exception of cancer. Because of the balancing effect of exchanges within and between the disease chapters, this generated only minor effects on general statistics on causes of death. The continuous correction of death certificates in the hospital was important for adjustments at the individual level and as a quality control of cause of death statistics, but had only minor effects on the general statistics from the hospital.
Current Challenges in Bayesian Model Choice
NASA Astrophysics Data System (ADS)
Clyde, M. A.; Berger, J. O.; Bullard, F.; Ford, E. B.; Jefferys, W. H.; Luo, R.; Paulo, R.; Loredo, T.
2007-11-01
Model selection (and the related issue of model uncertainty) arises in many astronomical problems, and, in particular, has been one of the focal areas of the Exoplanet working group under the SAMSI (Statistics and Applied Mathematical Sciences Institute) Astrostatistcs Exoplanet program. We provide an overview of the Bayesian approach to model selection and highlight the challenges involved in implementing Bayesian model choice in four stylized problems. We review some of the current methods used by statisticians and astronomers and present recent developments in the area. We discuss the applicability, computational challenges, and performance of suggested methods and conclude with recommendations and open questions.
Moving beyond qualitative evaluations of Bayesian models of cognition.
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.
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…
Using statistical process control to make data-based clinical decisions.
Pfadt, A; Wheeler, D J
1995-01-01
Applied behavior analysis is based on an investigation of variability due to interrelationships among antecedents, behavior, and consequences. This permits testable hypotheses about the causes of behavior as well as for the course of treatment to be evaluated empirically. Such information provides corrective feedback for making data-based clinical decisions. This paper considers how a different approach to the analysis of variability based on the writings of Walter Shewart and W. Edwards Deming in the area of industrial quality control helps to achieve similar objectives. Statistical process control (SPC) was developed to implement a process of continual product improvement while achieving compliance with production standards and other requirements for promoting customer satisfaction. SPC involves the use of simple statistical tools, such as histograms and control charts, as well as problem-solving techniques, such as flow charts, cause-and-effect diagrams, and Pareto charts, to implement Deming's management philosophy. These data-analytic procedures can be incorporated into a human service organization to help to achieve its stated objectives in a manner that leads to continuous improvement in the functioning of the clients who are its customers. Examples are provided to illustrate how SPC procedures can be used to analyze behavioral data. Issues related to the application of these tools for making data-based clinical decisions and for creating an organizational climate that promotes their routine use in applied settings are also considered.
Bilingualism and inhibitory control influence statistical learning of novel word forms.
Bartolotti, James; Marian, Viorica; Schroeder, Scott R; Shook, Anthony
2011-01-01
We examined the influence of bilingual experience and inhibitory control on the ability to learn a novel language. Using a statistical learning paradigm, participants learned words in two novel languages that were based on the International Morse Code. First, participants listened to a continuous stream of words in a Morse code language to test their ability to segment words from continuous speech. Since Morse code does not overlap in form with natural languages, interference from known languages was minimized. Next, participants listened to another Morse code language composed of new words that conflicted with the first Morse code language. Interference in this second language was high due to conflict between languages and due to the presence of two colliding cues (compressed pauses between words and statistical regularities) that competed to define word boundaries. Results suggest that bilingual experience can improve word learning when interference from other languages is low, while inhibitory control ability can improve word learning when interference from other languages is high. We conclude that the ability to extract novel words from continuous speech is a skill that is affected both by linguistic factors, such as bilingual experience, and by cognitive abilities, such as inhibitory control.
Bilingualism and Inhibitory Control Influence Statistical Learning of Novel Word Forms
Bartolotti, James; Marian, Viorica; Schroeder, Scott R.; Shook, Anthony
2011-01-01
We examined the influence of bilingual experience and inhibitory control on the ability to learn a novel language. Using a statistical learning paradigm, participants learned words in two novel languages that were based on the International Morse Code. First, participants listened to a continuous stream of words in a Morse code language to test their ability to segment words from continuous speech. Since Morse code does not overlap in form with natural languages, interference from known languages was minimized. Next, participants listened to another Morse code language composed of new words that conflicted with the first Morse code language. Interference in this second language was high due to conflict between languages and due to the presence of two colliding cues (compressed pauses between words and statistical regularities) that competed to define word boundaries. Results suggest that bilingual experience can improve word learning when interference from other languages is low, while inhibitory control ability can improve word learning when interference from other languages is high. We conclude that the ability to extract novel words from continuous speech is a skill that is affected both by linguistic factors, such as bilingual experience, and by cognitive abilities, such as inhibitory control. PMID:22131981
Using statistical process control to make data-based clinical decisions.
Pfadt, A; Wheeler, D J
1995-01-01
Applied behavior analysis is based on an investigation of variability due to interrelationships among antecedents, behavior, and consequences. This permits testable hypotheses about the causes of behavior as well as for the course of treatment to be evaluated empirically. Such information provides corrective feedback for making data-based clinical decisions. This paper considers how a different approach to the analysis of variability based on the writings of Walter Shewart and W. Edwards Deming in the area of industrial quality control helps to achieve similar objectives. Statistical process control (SPC) was developed to implement a process of continual product improvement while achieving compliance with production standards and other requirements for promoting customer satisfaction. SPC involves the use of simple statistical tools, such as histograms and control charts, as well as problem-solving techniques, such as flow charts, cause-and-effect diagrams, and Pareto charts, to implement Deming's management philosophy. These data-analytic procedures can be incorporated into a human service organization to help to achieve its stated objectives in a manner that leads to continuous improvement in the functioning of the clients who are its customers. Examples are provided to illustrate how SPC procedures can be used to analyze behavioral data. Issues related to the application of these tools for making data-based clinical decisions and for creating an organizational climate that promotes their routine use in applied settings are also considered. PMID:7592154
Peters, Rosalind M; Gjini, Klevest; Templin, Thomas N; Boutros, Nash N
2014-05-30
We present a methodology to statistically discriminate among univariate and multivariate indices to improve accuracy in differentiating schizophrenia patients from healthy controls. Electroencephalogram data from 71 subjects (37 controls/34 patients) were analyzed. Data included P300 event-related response amplitudes and latencies as well as amplitudes and sensory gating indices derived from the P50, N100, and P200 auditory-evoked responses resulting in 20 indices analyzed. Receiver operator characteristic (ROC) curve analyses identified significant univariate indices; these underwent principal component analysis (PCA). Logistic regression of PCA components created a multivariate composite used in the final ROC. Eleven univariate ROCs were significant with area under the curve (AUC) >0.50. PCA of these indices resulted in a three-factor solution accounting for 76.96% of the variance. The first factor was defined primarily by P200 and P300 amplitudes, the second by P50 ratio and difference scores, and the third by P300 latency. ROC analysis using the logistic regression composite resulted in an AUC of 0.793 (0.06), p<0.001 (CI=0.685-0.901). A composite score of 0.456 had a sensitivity of 0.829 (correctly identifying schizophrenia patients) and a specificity of 0.703 (correctly identifying healthy controls). Results demonstrated the usefulness of combined statistical techniques in creating a multivariate composite that improves diagnostic accuracy. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Bayesian Estimation of Thermonuclear Reaction Rates
NASA Astrophysics Data System (ADS)
Iliadis, C.; Anderson, K. S.; Coc, A.; Timmes, F. X.; Starrfield, S.
2016-11-01
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 present 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)3He, 3He(3He,2p)4He, and 3He(α,γ)7Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
Al-Hussein, Fahad A.
2009-01-01
Objective: To use statistical control charts in a series of audits to improve the acceptance and consistant use of guidelines, and reduce the variations in prescription processing in primary health care. Methods: A series of audits were done at the main satellite of King Saud Housing Family and Community Medicine Center, National Guard Health Affairs, Riyadh, where three general practitioners and six pharmacists provide outpatient care to about 3000 residents. Audits were carried out every fortnight to calculate the proportion of prescriptions that did not conform to the given guidelines of prescribing and dispensing. Simple random samples of thirty were chosen from a sampling frame of all prescriptions given in the two previous weeks. Thirty six audits were carried out from September 2004 to February 2006. P-charts were constructed around a parametric specification of non-conformities not exceeding 25%. Results: Of the 1081 prescriptions, the most frequent non-conformity was failure to write generic names (35.5%), followed by the failure to record patient's weight (16.4%), pharmacist's name (14.3%), duration of therapy (9.1%), and the use of inappropriate abbreviations (6.0%). Initially, 100% of prescriptions did not conform to the guidelines, but within a period of three months, this came down to 40%. Conclusions: A process of audits in the context of statistical process control is necessary for any improvement in the implementation of guidelines in primary care. Statistical process control charts are an effective means of visual feedback to the care providers. PMID:23012184
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2017-04-12
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.
Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao
2016-12-01
To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented.
Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control.
Westgard, James O; Westgard, Sten A
2017-03-01
Six sigma concepts provide a quality management system (QMS) with many useful tools for managing quality in medical laboratories. This Six Sigma QMS is driven by the quality required for the intended use of a test. The most useful form for this quality requirement is the allowable total error. Calculation of a sigma-metric provides the best predictor of risk for an analytical examination process, as well as a design parameter for selecting the statistical quality control (SQC) procedure necessary to detect medically important errors. Simple point estimates of sigma at medical decision concentrations are sufficient for laboratory applications. Copyright © 2016 Elsevier Inc. All rights reserved.
The Hybrid Synthetic Microdata Platform: A Method for Statistical Disclosure Control
van den Heuvel, Edwin R.; Swertz, Morris A.
2015-01-01
Owners of biobanks are in an unfortunate position: on the one hand, they need to protect the privacy of their participants, whereas on the other, their usefulness relies on the disclosure of the data they hold. Existing methods for Statistical Disclosure Control attempt to find a balance between utility and confidentiality, but come at a cost for the analysts of the data. We outline an alternative perspective to the balance between confidentiality and utility. By combining the generation of synthetic data with the automated execution of data analyses, biobank owners can guarantee the privacy of their participants, yet allow the analysts to work in an unrestricted manner. PMID:26035007
Monitoring Actuarial Present Values of Term Life Insurance By a Statistical Process Control Chart
NASA Astrophysics Data System (ADS)
Hafidz Omar, M.
2015-06-01
Tracking performance of life insurance or similar insurance policy using standard statistical process control chart is complex because of many factors. In this work, we present the difficulty in doing so. However, with some modifications of the SPC charting framework, the difficulty can be manageable to the actuaries. So, we propose monitoring a simpler but natural actuarial quantity that is typically found in recursion formulas of reserves, profit testing, as well as present values. We shared some simulation results for the monitoring process. Additionally, some advantages of doing so is discussed.
Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark; Menzies, Ross; Packman, Ann
2013-12-01
Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech during different speaking activities over a 12-hr day. Results for each participant were plotted on control charts. All participants showed marked variation around mean stuttering frequency. However, there was no pattern of that variation consistent across the 10 participants. According to control charts, the %SS scores of half the participants were indicative of unpredictable, out-of-control systems, and the %SS scores of the other half of the participants were not. Self-rated stuttering severity and communication satisfaction correlated significantly and intuitively with the number of times participants exceeded their upper control chart limits. Control charts are a useful method to study how %SS scores might be applied to the study of stuttering variability during research and clinical practice. However, the method presents some practical problems, and the authors discuss how those problems can be solved. Solving those problems would enable researchers and clinicians to better plan, conduct, and evaluate stuttering treatments.
Menghi, Enrico; Marcocci, Francesco; Bianchini, David
2016-01-01
The purpose of this study was to retrospectively evaluate the results from a Helical TomoTherapy Hi-Art treatment system relating to quality controls based on daily static and dynamic output checks using statistical process control methods. Individual value X-charts, exponentially weighted moving average charts, and process capability and acceptability indices were used to monitor the treatment system performance. Daily output values measured from January 2014 to January 2015 were considered. The results obtained showed that, although the process was in control, there was an out-of-control situation in the principal maintenance intervention for the treatment system. In particular, process capability indices showed a decreasing percentage of points in control which was, however, acceptable according to AAPM TG148 guidelines. Our findings underline the importance of restricting the acceptable range of daily output checks and suggest a future line of investigation for a detailed process control of daily output checks for the Helical TomoTherapy Hi-Art treatment system. PMID:26848962
Adaptive Decoding for Brain-Machine Interfaces through Bayesian Parameter Updates
Li, Zheng; O’Doherty, Joseph E.; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.
2011-01-01
Brain machine interfaces (BMIs) transform activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To assure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder’s output to periodically update the decoder’s neuronal tuning model in a Bayesian linear regression. We use two previously-known statistical formulations of Bayesian linear regression: (1) a joint formulation which allows fast and exact inference, and (2) a factorized formulation which allows the addition and temporary omission of neurons from updates, but requires approximate variational inference. To evaluate these methods, we performed off-line reconstructions and closed-loop experiments with Rhesus monkeys implanted cortically with micro-wire electrodes. Off-line reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of 3 monkeys while they controlled a cursor using a hand-held joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control
NASA Astrophysics Data System (ADS)
Khan, Shahjahan
Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden "jewels" in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model
NASA Astrophysics Data System (ADS)
Khan, Shahjahan
Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden “jewels” in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model
Bayesian Spectroscopy and Target Tracking
Cunningham, C
2001-05-01
Statistical analysis gives a paradigm for detection and tracking of weak-signature sources that are moving among a network of detectors. The detector platforms compute and exchange information with near-neighbors in the form of Bayesian probabilities for possible sources. This can shown to be an optimal scheme for the use of detector information and communication resources. Here, we apply that paradigm to the detection and discrimination of radiation sources using multi-channel gamma-ray spectra. We present algorithms for the reduction of detector data to probability estimates and the fusion of estimates among multiple detectors. A primary result is the development of a goodness-of-fit metric, similar to {chi}{sup 2}, for template matching that is statistically valid for spectral channels with low expected counts. Discrimination of a target source from other false sources and detection of imprecisely known spectra are the main applications considered. We use simulated NaI spectral data to demonstrate the Bayesian algorithm compare it to other techniques. Results of simulations of a network of spectrometers are presented, showing its capability to distinguish intended targets from nuisance sources.
McCaa, Robert; Ruggles, Steven; Sobek, Matt
2016-01-01
In the last decade, a revolution has occurred in access to census microdata for social and behavioral research. More than 325 million person records (55 countries, 159 samples) representing two-thirds of the world’s population are now readily available to bona fide researchers from the IPUMS-International website: www.ipums.org/international hosted by the Minnesota Population Center. Confidentialized extracts are disseminated on a restricted access basis at no cost to bona fide researchers. Over the next five years, from the microdata already entrusted by National Statistical Office-owners, the database will encompass more than 80 percent of the world’s population (85 countries, ~100 additional datasets) with priority given to samples from the 2010 round of censuses. A profile of the most frequently used samples and variables is described from 64,248 requests for microdata extracts. The development of privacy protection standards by National Statistical Offices, international organizations and academic experts is fundamental to eliciting world-wide cooperation and, thus, to the success of the IPUMS initiative. This paper summarizes the legal, administrative and technical underpinnings of the project, including statistical disclosure controls, as well as the conclusions of a lengthy on-site review by the former Australian Statistician, Mr. Dennis Trewin.
When mechanism matters: Bayesian forecasting using models of ecological diffusion
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.
Tsai, Miao-Yu
2015-03-01
The problem of variable selection in the generalized linear-mixed models (GLMMs) is pervasive in statistical practice. For the purpose of variable selection, many methodologies for determining the best subset of explanatory variables currently exist according to the model complexity and differences between applications. In this paper, we develop a "higher posterior probability model with bootstrap" (HPMB) approach to select explanatory variables without fitting all possible GLMMs involving a small or moderate number of explanatory variables. Furthermore, to save computational load, we propose an efficient approximation approach with Laplace's method and Taylor's expansion to approximate intractable integrals in GLMMs. Simulation studies and an application of HapMap data provide evidence that this selection approach is computationally feasible and reliable for exploring true candidate genes and gene-gene associations, after adjusting for complex structures among clusters. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Statistical Methods for Astronomy
NASA Astrophysics Data System (ADS)
Feigelson, Eric D.; Babu, G. Jogesh
Statistical methodology, with deep roots in probability theory, providesquantitative procedures for extracting scientific knowledge from astronomical dataand for testing astrophysical theory. In recent decades, statistics has enormouslyincreased in scope and sophistication. After a historical perspective, this reviewoutlines concepts of mathematical statistics, elements of probability theory,hypothesis tests, and point estimation. Least squares, maximum likelihood, andBayesian approaches to statistical inference are outlined. Resampling methods,particularly the bootstrap, provide valuable procedures when distributionsfunctions of statistics are not known. Several approaches to model selection andgoodness of fit are considered.
Flood, David; Douglas, Kate; Goldberg, Vera; Martinez, Boris; Garcia, Pablo; Arbour, MaryCatherine; Rohloff, Peter
2017-05-09
Quality improvement (QI) is a key strategy for improving diabetes care in low- and middle-income countries (LMICs). This study reports on a diabetes QI project in rural Guatemala whose primary aim was to improve glycemic control of a panel of adult diabetes patients. Formative research suggested multiple areas for programmatic improvement in ambulatory diabetes care. This project utilized the Model for Improvement and Agile Global Health, our organization's complementary healthcare implementation framework. A bundle of improvement activities were implemented at the home, clinic and institutional level. Control charts of mean hemoglobin A1C (HbA1C) and proportion of patients meeting target HbA1C showed improvement as special cause variation was identified 3 months after the intervention began. Control charts for secondary process measures offered insights into the value of different components of the intervention. Intensity of home-based diabetes education emerged as an important driver of panel glycemic control. Diabetes QI work is feasible in resource-limited settings in LMICs and can improve glycemic control. Statistical process control charts are a promising methodology for use with panels or registries of diabetes patients.
The Bayesian t-test and beyond.
Gönen, Mithat
2010-01-01
In this chapter we will explore Bayesian alternatives to the t-test. We saw in Chapter 1 how t-test can be used to test whether the expected outcomes of the two groups are equal or not. In Chapter 3 we saw how to make inferences from a Bayesian perspective in principle. In this chapter we will put these together to develop a Bayesian procedure for a t-test. This procedure depends on the data only through the t-statistic. It requires prior inputs and we will discuss how to assign them. We will use an example from a microarray study as to demonstrate the practical issues. The microarray study is an important application for the Bayesian t-test as it naturally brings up the question of simultaneous t-tests. It turns out that the Bayesian procedure can easily be extended to carry several t-tests on the same data set, provided some attention is paid to the concept of the correlation between tests.
Efficient inference for hybrid dynamic Bayesian networks
NASA Astrophysics Data System (ADS)
Chang, Kuo Chu; Chen, Hongda
2005-07-01
This paper is a revision of a paper presented at the SPIE conference on Signal Processing, Senior Fusion, and Target Recognition XII, Aug. 2004, Orlando, Florida. The paper presented there appears (unrefereed) in SPIE Proceedings Vol. 5429. Bayesian networks for static as well as for dynamic cases have been the subject of a great deal of theoretical analysis and practical inference-algorithm development in the research community of artificial intelligence, machine learning, and pattern recognition. After summarizing the well-known theory of discrete and continuous Bayesian networks, we introduce an efficient reasoning scheme into hybrid Bayesian networks. In addition to illustrating the similarities between the dynamic Bayesian networks and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBNs). The proposed method is based on the separation of the dynamic and static nodes, and subsequent hypercubic partitions via the decision tree algorithm. Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the trade-offs of computational complexity and accuracy performance, compared to other exact and approximate methods for applications with uncertainty in a dynamic system.
Computationally efficient Bayesian inference for inverse problems.
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
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
Hantula, D A
1995-01-01
This paper explores some of the implications the statistical process control (SPC) methodology described by Pfadt and Wheeler (1995) may have for analyzing more complex performances and contingencies in human services or health care environments at an organizational level. Service delivery usually occurs in an organizational system that is characterized by functional structures, high levels of professionalism, subunit optimization, and organizational suboptimization. By providing a standard set of criteria and decision rules, SPC may provide a common interface for data-based decision making, may bring decision making under the control of the contigencies that are established by these rules rather than the immediate contingencies of data fluctuation, and may attenuate escalation of failing treatments. SPC is culturally consistent with behavior analysis, sharing an emphasis on data-based decisions, measurement over time, and graphic analysis of data, as well as a systemic view of organizations. PMID:7592155
Using a statistical process control chart during the quality assessment of cancer registry data.
Myles, Zachary M; German, Robert R; Wilson, Reda J; Wu, Manxia
2011-01-01
Statistical process control (SPC) charts may be used to detect acute variations in the data while simultaneously evaluating unforeseen aberrations that may warrant further investigation by the data user. Using cancer stage data captured by the Summary Stage 2000 (SS2000) variable, we sought to present a brief report highlighting the utility of the SPC chart during the quality assessment of cancer registry data. Using a county-level caseload for the diagnosis period of 2001-2004 (n=25,648), we found the overall variation of the SS2000 variable to be in control during diagnosis years of 2001 and 2002, exceeded the lower control limit (LCL) in 2003, and exceeded the upper control limit (UCL) in 2004; in situ/localized stages were in control throughout the diagnosis period, regional stage exceeded UCL in 2004, and distant stage exceeded the LCL in 2001 and the UCL in 2004. Our application of the SPC chart with cancer registry data illustrates that the SPC chart may serve as a readily available and timely tool for identifying areas of concern during the data collection and quality assessment of central cancer registry data.
Register Control of Roll-to-Roll Printing System Based on Statistical Approach
NASA Astrophysics Data System (ADS)
Kim, Chung Hwan; You, Ha-Il; Jo, Jeongdai
2013-05-01
One of the most important requirements when using roll-to-roll printing equipment for multilayer printing is register control. Because multilayer printing requires a printing accuracy of several microns to several tens of microns, depending on the devices and their sizes, precise register control is required. In general, the register errors vary with time, even for one revolution of the plate cylinder. Therefore, more information about the register errors in one revolution of the plate cylinder is required for more precise register control, which is achieved by using multiple register marks in a single revolution of the plate cylinder. By using a larger number of register marks, we can define the value of the register error as a statistical value rather than a single one. The register errors measured from an actual roll-to-roll printing system consist of a linearly varying term, a static offset term, and small fluctuations. The register errors resulting from the linearly varying term and the offset term are compensated for by the velocity and phase control of the plate cylinders, based on the calculated slope and offset of the register errors, which are obtained by the curve-fitting of the data set of register errors. We show that even with the slope and offset compensation of the register errors, a register control performance of within 20 µm can be achieved.
NASA Technical Reports Server (NTRS)
Oravec, Heather Ann; Daniels, Christopher C.
2014-01-01
The National Aeronautics and Space Administration has been developing a novel docking system to meet the requirements of future exploration missions to low-Earth orbit and beyond. A dynamic gas pressure seal is located at the main interface between the active and passive mating components of the new docking system. This seal is designed to operate in the harsh space environment, but is also to perform within strict loading requirements while maintaining an acceptable level of leak rate. In this study, a candidate silicone elastomer seal was designed, and multiple subscale test articles were manufactured for evaluation purposes. The force required to fully compress each test article at room temperature was quantified and found to be below the maximum allowable load for the docking system. However, a significant amount of scatter was observed in the test results. Due to the stochastic nature of the mechanical performance of this candidate docking seal, a statistical process control technique was implemented to isolate unusual compression behavior from typical mechanical performance. The results of this statistical analysis indicated a lack of process control, suggesting a variation in the manufacturing phase of the process. Further investigation revealed that changes in the manufacturing molding process had occurred which may have influenced the mechanical performance of the seal. This knowledge improves the chance of this and future space seals to satisfy or exceed design specifications.
Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments.
Welch, Rene; Chung, Dongjun; Grass, Jeffrey; Landick, Robert; Keles, Sündüz
2017-09-06
ChIP-exo/nexus experiments rely on innovative modifications of the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a novel statistical quality control pipeline and accompanying R/Bioconductor package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics computed over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from Escherichia coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments
Welch, Rene; Chung, Dongjun; Grass, Jeffrey; Landick, Robert
2017-01-01
Abstract ChIP-exo/nexus experiments rely on innovative modifications of the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a novel statistical quality control pipeline and accompanying R/Bioconductor package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics computed over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from Escherichia coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data. PMID:28911122
Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.
Silva, Ana F T; Sarraguça, Mafalda Cruz; Ribeiro, Paulo R; Santos, Adenilson O; De Beer, Thomas; Lopes, João Almeida
2017-03-30
Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T(2) statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS.
Efficient inference algorithms for hybrid dynamic Bayesian networks (HDBN)
NASA Astrophysics Data System (ADS)
Chang, KuoChu; Chen, Hongda
2004-08-01
Bayesian networks for the static as well as for the dynamic cases have been the subject of a great deal of theoretical analysis and practical inference approximations in the research community of artificial intelligence, machine learning and pattern recognition. After exploring the quite well known theory of discrete and continuous Bayesian networks, we introduce an almost instant reasoning scheme to the hybrid Bayesian networks. In addition to illustrate the similarities of the dynamic Bayesian networks (DBN) and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBN). The proposed method is based on the separations of the dynamic and static nodes, and following hypercubic partitions via the Decision tree algorithm (DT). Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the tradeoffs of computational complexities and accuracy performance when compared to Junction tree and Gaussian mixture models on the task of classifications.
Bayesian microsaccade detection
Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji
2017-01-01
Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483
Isetta, Valentina; Negrín, Miguel A; Monasterio, Carmen; Masa, Juan F; Feu, Nuria; Álvarez, Ainhoa; Campos-Rodriguez, Francisco; Ruiz, Concepción; Abad, Jorge; Vázquez-Polo, Francisco J; Farré, Ramon; Galdeano, Marina; Lloberes, Patricia; Embid, Cristina; de la Peña, Mónica; Puertas, Javier; Dalmases, Mireia; Salord, Neus; Corral, Jaime; Jurado, Bernabé; León, Carmen; Egea, Carlos; Muñoz, Aida; Parra, Olga; Cambrodi, Roser; Martel-Escobar, María; Arqué, Meritxell; Montserrat, Josep M
2015-11-01
Compliance with continuous positive airway pressure (CPAP) therapy is essential in patients with obstructive sleep apnoea (OSA), but adequate control is not always possible. This is clinically important because CPAP can reverse the morbidity and mortality associated with OSA. Telemedicine, with support provided via a web platform and video conferences, could represent a cost-effective alternative to standard care management. To assess the telemedicine impact on treatment compliance, cost-effectiveness and improvement in quality of life (QoL) when compared with traditional face-to-face follow-up. A randomised controlled trial was performed to compare a telemedicine-based CPAP follow-up strategy with standard face-to-face management. Consecutive OSA patients requiring CPAP treatment, with sufficient internet skills and who agreed to participate, were enrolled. They were followed-up at 1, 3 and 6 months and answered surveys about sleep, CPAP side effects and lifestyle. We compared CPAP compliance, cost-effectiveness and QoL between the beginning and the end of the study. A Bayesian cost-effectiveness analysis with non-informative priors was performed. We randomised 139 patients. At 6 months, we found similar levels of CPAP compliance, and improved daytime sleepiness, QoL, side effects and degree of satisfaction in both groups. Despite requiring more visits, the telemedicine group was more cost-effective: costs were lower and differences in effectiveness were not relevant. A telemedicine-based strategy for the follow-up of CPAP treatment in patients with OSA was as effective as standard hospital-based care in terms of CPAP compliance and symptom improvement, with comparable side effects and satisfaction rates. The telemedicine-based strategy had lower total costs due to savings on transport and less lost productivity (indirect costs). NCT01716676. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go
Govindarajan, R; Llueguera, E; Melero, A; Molero, J; Soler, N; Rueda, C; Paradinas, C
2010-01-01
Statistical Process Control (SPC) was applied to monitor patient set-up in radiotherapy and, when the measured set-up error values indicated a loss of process stability, its root cause was identified and eliminated to prevent set-up errors. Set up errors were measured for medial-lateral (ml), cranial-caudal (cc) and anterior-posterior (ap) dimensions and then the upper control limits were calculated. Once the control limits were known and the range variability was acceptable, treatment set-up errors were monitored using sub-groups of 3 patients, three times each shift. These values were plotted on a control chart in real time. Control limit values showed that the existing variation was acceptable. Set-up errors, measured and plotted on a X chart, helped monitor the set-up process stability and, if and when the stability was lost, treatment was interrupted, the particular cause responsible for the non-random pattern was identified and corrective action was taken before proceeding with the treatment. SPC protocol focuses on controlling the variability due to assignable cause instead of focusing on patient-to-patient variability which normally does not exist. Compared to weekly sampling of set-up error in each and every patient, which may only ensure that just those sampled sessions were set-up correctly, the SPC method enables set-up error prevention in all treatment sessions for all patients and, at the same time, reduces the control costs. Copyright © 2009 SECA. Published by Elsevier Espana. All rights reserved.
Dechartres, Agnes; Bond, Elizabeth G; Scheer, Jordan; Riveros, Carolina; Atal, Ignacio; Ravaud, Philippe
2016-11-30
Publication bias and other reporting bias have been well documented for journal articles, but no study has evaluated the nature of results posted at ClinicalTrials.gov. We aimed to assess how many randomized controlled trials (RCTs) with results posted at ClinicalTrials.gov report statistically significant results and whether the proportion of trials with significant results differs when no treatment effect estimate or p-value is posted. We searched ClinicalTrials.gov in June 2015 for all studies with results posted. We included completed RCTs with a superiority hypothesis and considered results for the first primary outcome with results posted. For each trial, we assessed whether a treatment effect estimate and/or p-value was reported at ClinicalTrials.gov and if yes, whether results were statistically significant. If no treatment effect estimate or p-value was reported, we calculated the treatment effect and corresponding p-value using results per arm posted at ClinicalTrials.gov when sufficient data were reported. From the 17,536 studies with results posted at ClinicalTrials.gov, we identified 2823 completed phase 3 or 4 randomized trials with a superiority hypothesis. Of these, 1400 (50%) reported a treatment effect estimate and/or p-value. Results were statistically significant for 844 trials (60%), with a median p-value of 0.01 (Q1-Q3: 0.001-0.26). For the 1423 trials with no treatment effect estimate or p-value posted, we could calculate the treatment effect and corresponding p-value using results reported per arm for 929 (65%). For 494 trials (35%), p-values could not be calculated mainly because of insufficient reporting, censored data, or repeated measurements over time. For the 929 trials we could calculate p-values, we found statistically significant results for 342 (37%), with a median p-value of 0.19 (Q1-Q3: 0.005-0.59). Half of the trials with results posted at ClinicalTrials.gov reported a treatment effect estimate and/or p-value, with significant
A Case Study of Ion Implant In-Line Statistical Process Control
NASA Astrophysics Data System (ADS)
Zhao, Zhiyong; Ramczyk, Kenneth; Hall, Darcy; Wang, Linda
2005-09-01
Ion implantation is one of the most critical processes in the front-end-of-line for ULSI manufacturing. With more complexity in device layout, the fab cycle time can only be expected to be longer. To ensure yield and consistent device performance it is very beneficial to have a Statistical Process Control (SPC) practice that can detect tool issues to prevent excursions. Also, implanters may abort a process due to run-time issues. It requires human intervention to dispose of the lot. Since device wafers have a fixed flow plan and can only do anneal at certain points in the manufacturing process, it is not practical to use four-point probe to check such implants. Pattern recognition option on some of the metrology tools, such as ThermaWave (TWave), allows user to check an open area on device wafers for implant information. The two cited reasons prompted this work to look into the sensitivity of TWave with different implant processes and the possibility of setting up an SPC practice in a high-volume manufacturing fab. In this work, the authors compare the test wafer result with that of device wafers with variations in implant conditions such as dose, implant angle, energy, etc. The intention of this work is to correlate analytical measurement such as sheet resistance (Rs) and Secondary Ion Mass Spectrometry (SIMS) with device data such as electrical testing and sort yield. For a ± 1.5% TWave control limit with the tested implant processes in this work, this translates to about 0.5° in implant angle control or 2% to 8% dose change, respectively. It is understood that the dose sensitivity is not good since the tested processes are deep layer implants. Based on the statistics calculation, we assess the experimental error bar is within 1% of the measured values.
Multivariate statistical process control in product quality review assessment - A case study.
Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A
2017-08-07
According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T(2) 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.
Hippert, Henrique S; Taylor, James W
2010-04-01
Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation.
Bayesian analysis on meta-analysis of case-control studies accounting for within-study correlation.
Chen, Yong; Chu, Haitao; Luo, Sheng; Nie, Lei; Chen, Sining
2015-12-01
In retrospective studies, odds ratio is often used as the measure of association. Under independent beta prior assumption, the exact posterior distribution of odds ratio given a single 2 × 2 table has been derived in the literature. However, independence between risks within the same study may be an oversimplified assumption because cases and controls in the same study are likely to share some common factors and thus to be correlated. Furthermore, in a meta-analysis of case-control studies, investigators usually have multiple 2 × 2 tables. In this article, we first extend the published results on a single 2 × 2 table to allow within study prior correlation while retaining the advantage of closed-form posterior formula, and then extend the results to multiple 2 × 2 tables and regression setting. The hyperparameters, including within study correlation, are estimated via an empirical Bayes approach. The overall odds ratio and the exact posterior distribution of the study-specific odds ratio are inferred based on the estimated hyperparameters. We conduct simulation studies to verify our exact posterior distribution formulas and investigate the finite sample properties of the inference for the overall odds ratio. The results are illustrated through a twin study for genetic heritability and a meta-analysis for the association between the N-acetyltransferase 2 (NAT2) acetylation status and colorectal cancer.
Bayesian Analysis on Meta-analysis of Case-control Studies Accounting for Within-study Correlation
Chen, Yong; Chu, Haitao; Luo, Sheng; Nie, Lei; Chen, Sining
2013-01-01
In retrospective studies, odds ratio is often used as the measure of association. Under independent beta prior assumption, the exact posterior distribution of odds ratio given a single 2 × 2 table has been derived in the literature. However, independence between risks within the same study may be an oversimplified assumption because cases and controls in the same study are likely to share some common factors and thus to be correlated. Furthermore, in a meta-analysis of case-control studies, investigators usually have multiple 2×2 tables. In this paper, we first extend the published results on a single 2×2 table to allow within study prior correlation while retaining the advantage of closed form posterior formula, and then extend the results to multiple 2 × 2 tables and regression setting. The hyperparameters, including within study correlation, are estimated via an empirical Bayes approach. The overall odds ratio and the exact posterior distribution of the study-specific odds ratio are inferred based on the estimated hyperparameters. We conduct simulation studies to verify our exact posterior distribution formulas and investigate the finite sample properties of the inference for the overall odds ratio. The results are illustrated through a twin study for genetic heritability and a meta-analysis for the association between the N-acetyltransferase 2 (NAT2) acetylation status and colorectal cancer. PMID:22143403
Bayesian Analysis of Savings from Retrofit Projects
Im, Piljae
2012-01-01
Estimates of savings from retrofit projects depend on statistical models, but because of the complicated analysis required to determine the uncertainty of the estimates, savings uncertainty is not often considered. Numerous simplified methods have been proposed to determine savings uncertainty, but in all but the simplest cases, these methods provide approximate results only. The objective of this paper is to show that Bayesian inference provides a consistent framework for estimating savings and savings uncertainty in retrofit projects. We review the mathematical background of Bayesian inference and Bayesian regression, and present two examples of estimating savings and savings uncertainty in retrofit projects. The first is a simple case where both baseline and post-retrofit monthly natural gas use can be modeled as a linear function of monthly heating degree days. The Efficiency Valuation Organization (EVO 2007) defines two methods of determining savings in such cases: reporting period savings, which is an estimate of the savings during the post-retrofit period; and normalized savings, which is an estimate of the savings that would be obtained during a typical year at the project site. For reporting period savings, classical statistical analysis provides exact analytic results for both savings and savings uncertainty in this case. We use Bayesian analysis to calculate reporting period savings and savings uncertainty and show that the results are identical to the analytical results. For normalized savings, the literature contains no exact expression for the uncertainty of normalized savings; we use Bayesian inference to calculate this quantity for the first time, and compare it with the result of an approximate formula that has been proposed. The second example concerns a problem where the baseline data exhibit nonlinearity and serial autocorrelation, both of which are common in real-world retrofit projects. No analytical solutions exist to determine savings or
ERIC Educational Resources Information Center
van Krimpen-Stoop, Edith M. L. A.; Meijer, Rob R.
Person-fit research in the context of paper-and-pencil tests is reviewed, and some specific problems regarding person fit in the context of computerized adaptive testing (CAT) are discussed. Some new methods are proposed to investigate person fit in a CAT environment. These statistics are based on Statistical Process Control (SPC) theory. A…
Statistical disclosure control architectures for patient records in biomedical information systems.
Elliot, M; Purdam, K; Smith, D
2008-02-01
Patient record data are potentially highly sensitive and their secondary use raises both ethical and data protection issues. Disclosure of patient data could cause serious difficulties for the medical profession and be potentially damaging for individual patients and clinicians. Yet at the same time patient records are a hugely valuable resource in terms of clinical research and patient treatment. A secure, remote access system for such data would therefore provide numerous benefits. In this paper we outline the statistical disclosure risks posed by patient record data in the context of establishing a grid based medical data repository. We review good practice in existing patient databases, outline a scenario model for assessing risk and suggest a new model for statistical disclosure control of patient data. The architecture and the research method we have described have general relevance for any remote data access system where maximizing both data utility and security is a priority, and has specific relevance to medical data and bioinformatics. It can straightforwardly be integrated into data access and analysis tools.
NASA Astrophysics Data System (ADS)
Line, C. E. R.; Hobbs, R. W.; Hudson, J. A.; Snyder, D. B.
1998-01-01
Statistical parameters describing heterogeneity in the Proterozoic basement of the Baltic Shield were estimated from controlled-source seismic data, using a statistical inversion based on the theory of wave propagation through random media (WPRM), derived from the parabolic wave approximation. Synthetic plane-wave seismograms generated from models of random media show consistency with WPRM theory for forward propagation in the weak-scattering regime, whilst for two-way propagation a discrepancy exists that is due to contamination of the primary wave by backscattered energy. Inverse modelling of the real seismic data suggests that the upper crust to depths of ~ 15 km can be characterized, subject to the range of spatial resolution of the method, by a medium with an exponential spatial autocorrelation function, an rms velocity fluctuation of 1.5 +/- 0.5 per cent and a correlation length of 150 +/- 50 m. Further inversions show that scattering is predominantly occurring in the uppermost ~ 2 km of crust, where rms velocity fluctuation is 3 - 6 per cent. Although values of correlation distance are well constrained by these inversions, there is a trade-off between thickness of scattering layer and rms velocity perturbation estimates, with both being relatively poorly resolved. The higher near-surface heterogeneity is interpreted to arise from fractures in the basement rocks that close under lithostatic pressure for depths greater than 2 - 3 km.
FISHBONE,L.G.
1999-07-25
While substantial work has been performed in the Russian MPC&A Program, much more needs to be done at Russian nuclear facilities to complete four necessary steps. These are (1) periodically measuring the physical inventory of nuclear material, (2) continuously measuring the flows of nuclear material, (3) using the results to close the material balance, particularly at bulk processing facilities, and (4) statistically evaluating any apparent loss of nuclear material. The periodic closing of material balances provides an objective test of the facility's system of nuclear material protection, control and accounting. The statistical evaluation using the uncertainties associated with individual measurement systems involved in the calculation of the material balance provides a fair standard for concluding whether the apparent loss of nuclear material means a diversion or whether the facility's accounting system needs improvement. In particular, if unattractive flow material at a facility is not measured well, the accounting system cannot readily detect the loss of attractive material if the latter substantially derives from the former.
Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan
2016-05-01
The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016.
SU-D-201-04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control
Meyers, SM; Balderson, MJ; Letourneau, D
2016-06-15
Purpose: to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools. Methods: Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field-of-view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out-of-control and out-of-specification events were determined. Results: Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out-of-control events, the mean in-control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy. Conclusion: The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on-going to determine if Agility performance can be optimized further using high-frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image
Poster - Thur Eve - 29: Detecting changes in IMRT QA using statistical process control.
Drever, L; Salomons, G
2012-07-01
Statistical process control (SPC) methods were used to analyze 239 measurement based individual IMRT QA events. The selected IMRT QA events were all head and neck (H&N) cases with 70Gy in 35 fractions, and all prostate cases with 76Gy in 38 fractions planned between March 2009 and 2012. The results were used to determine if the tolerance limits currently being used for IMRT QA were able to indicate if the process was under control. The SPC calculations were repeated for IMRT QA of the same type of cases that were planned after the treatment planning system was upgraded from Eclipse version 8.1.18 to version 10.0.39. The initial tolerance limits were found to be acceptable for two of the three metrics tested prior to the upgrade. After the upgrade to the treatment planning system the SPC analysis found that the a priori limits were no longer capable of indicating control for 2 of the 3 metrics analyzed. The changes in the IMRT QA results were clearly identified using SPC, indicating that it is a useful tool for finding changes in the IMRT QA process. Routine application of SPC to IMRT QA results would help to distinguish unintentional trends and changes from the random variation in the IMRT QA results for individual plans. © 2012 American Association of Physicists in Medicine.
Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka
2017-04-09
Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.
Sassenhagen, Jona; Alday, Phillip M
2016-11-01
Experimental research on behavior and cognition frequently rests on stimulus or subject selection where not all characteristics can be fully controlled, even when attempting strict matching. For example, when contrasting patients to controls, variables such as intelligence or socioeconomic status are often correlated with patient status. Similarly, when presenting word stimuli, variables such as word frequency are often correlated with primary variables of interest. One procedure very commonly employed to control for such nuisance effects is conducting inferential tests on confounding stimulus or subject characteristics. For example, if word length is not significantly different for two stimulus sets, they are considered as matched for word length. Such a test has high error rates and is conceptually misguided. It reflects a common misunderstanding of statistical tests: interpreting significance not to refer to inference about a particular population parameter, but about 1. the sample in question, 2. the practical relevance of a sample difference (so that a nonsignificant test is taken to indicate evidence for the absence of relevant differences). We show inferential testing for assessing nuisance effects to be inappropriate both pragmatically and philosophically, present a survey showing its high prevalence, and briefly discuss an alternative in the form of regression including nuisance variables.
Lee, Young Ho; Song, Gwan Gyu
2016-05-01
The aim of this study was to assess the relative efficacy and tolerability of duloxetine, pregabalin, and milnacipran at the recommended doses in patients with fibromyalgia. Randomized controlled trials (RCTs) examining the efficacy and safety of duloxetine 60 mg, pregabalin 300 mg, pregabalin 150 mg, milnacipran 200 mg, and milnacipran 100 mg compared to placebo in patients with fibromyalgia were included in this Bayesian network meta-analysis. Nine RCTs including 5140 patients met the inclusion criteria. The proportion of patients with >30 % improvement from baseline in pain was significantly higher in the duloxetine 60 mg, pregabalin 300 mg, milnacipran 100 mg, and milnacipran 200 mg groups than in the placebo group [pairwise odds ratio (OR) 2.33, 95 % credible interval (CrI) 1.50-3.67; OR 1.68, 95 % CrI 1.25-2.28; OR 1.62, 95 % CrI 1.16-2.25; and OR 1.61; 95 % CrI 1.15-2.24, respectively]. Ranking probability based on the surface under the cumulative ranking curve (SUCRA) indicated that duloxetine 60 mg had the highest probability of being the best treatment for achieving the response level (SUCRA = 0.9431), followed by pregabalin 300 mg (SUCRA = 0.6300), milnacipran 100 mg (SUCRA = 0.5680), milnacipran 200 mg (SUCRA = 0.5617), pregabalin 150 mg (SUCRA = 0.2392), and placebo (SUCRA = 0.0580). The risk of withdrawal due to adverse events was lower in the placebo group than in the pregabalin 300 mg, duloxetine 60 mg, milnacipran 100 mg, and milnacipran 200 mg groups. However, there was no significant difference in the efficacy and tolerability between the medications at the recommended doses. Duloxetine 60 mg, pregabalin 300 mg, milnacipran 100 mg, and milnacipran 200 mg were more efficacious than placebo. However, there was no significant difference in the efficacy and tolerability between the medications at the recommended doses.
Statistical Analysis of Crossed Undulator for Polarization Control in a SASE FEL
Ding, Yuantao; Huang, Zhirong; /SLAC
2008-02-01
There is a growing interest in producing intense, coherent x-ray radiation with an adjustable and arbitrary polarization state. In this paper, we study the crossed undulator scheme (K.-J. Kim, Nucl. Instrum. Methods A 445, 329 (2000)) for rapid polarization control in a self-amplified spontaneous emission (SASE) free electron laser (FEL). Because a SASE source is a temporally chaotic light, we perform a statistical analysis on the state of polarization using FEL theory and simulations. We show that by adding a small phase shifter and a short (about 1.3 times the FEL power gain length), 90{sup o} rotated planar undulator after the main SASE planar undulator, one can obtain circularly polarized light--with over 80% polarization--near the FEL saturation.
NASA Astrophysics Data System (ADS)
Villeta, M.; Sanz-Lobera, A.; González, C.; Sebastián, M. A.
2009-11-01
The implantation of Statistical Process Control, SPC designated in short, requires the use of measurement systems. The inherent variability of these systems influences on the reliability of measurement results obtained, and as a consequence of it, influences on the SPC results. This paper investigates about the influence of the uncertainty of measurement on the analysis of process capability. It looks for reducing the effect of measurement uncertainty, to approach the capability that the productive process really has. In this work processes centered at a nominal value as well as off-center processes are raised, and a criterion is proposed that allows validate the adequacy of the dimensional measurement systems used in a SPC implantation.
Dodoo-Schittko, Frank; Rosengarth, Katharina; Doenitz, Christian; Greenlee, Mark W
2012-09-01
There is a discrepancy between the brain regions revealed by functional neuroimaging techniques and those brain regions where a loss of function, either by lesion or by electrocortical stimulation, induces language disorders. To differentiate between essential and non-essential language-related processes, we investigated the effects of linguistic control tasks and different analysis methods for functional MRI data. Twelve subjects solved two linguistic generation tasks: (1) a verb generation task and (2) an antonym generation task (each with a linguistic control task on the phonological level) as well as two decision tasks of semantic congruency (each with a cognitive high-level control task). Differential contrasts and conjunction analyses were carried out on the single-subject level and an individual lateralization index (LI) was computed. On the group level we determined the percent signal change in the left inferior frontal gyrus (IFG: BA 44 and BA 45). The conjunction analysis of multiple language tasks led to significantly greater absolute LIs than the LIs based on the single task versus fixation contrasts. A further significant increase of the magnitude of the LIs could be achieved by using the phonological control conditions. Although the decision tasks appear to be more robust to changes in the statistical threshold, the combined generation tasks had an advantage over the decision tasks both for assessing language dominance and locating Broca's area. These results underline the need for conjunction analysis based on several language tasks to suppress highly task-specific processes. They also point to the need for high-level cognitive control tasks to partial out general, language supporting but not language critical processes. Higher absolute LIs, which reflect unambiguously hemispheric language dominance, can be thus obtained.
Statistical process control analysis for patient-specific IMRT and VMAT QA.
Sanghangthum, Taweap; Suriyapee, Sivalee; Srisatit, Somyot; Pawlicki, Todd
2013-05-01
This work applied statistical process control to establish the control limits of the % gamma pass of patient-specific intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA), and to evaluate the efficiency of the QA process by using the process capability index (Cpml). A total of 278 IMRT QA plans in nasopharyngeal carcinoma were measured with MapCHECK, while 159 VMAT QA plans were undertaken with ArcCHECK. Six megavolts with nine fields were used for the IMRT plan and 2.5 arcs were used to generate the VMAT plans. The gamma (3%/3 mm) criteria were used to evaluate the QA plans. The % gamma passes were plotted on a control chart. The first 50 data points were employed to calculate the control limits. The Cpml was calculated to evaluate the capability of the IMRT/VMAT QA process. The results showed higher systematic errors in IMRT QA than VMAT QA due to the more complicated setup used in IMRT QA. The variation of random errors was also larger in IMRT QA than VMAT QA because the VMAT plan has more continuity of dose distribution. The average % gamma pass was 93.7% ± 3.7% for IMRT and 96.7% ± 2.2% for VMAT. The Cpml value of IMRT QA was 1.60 and VMAT QA was 1.99, which implied that the VMAT QA process was more accurate than the IMRT QA process. Our lower control limit for % gamma pass of IMRT is 85.0%, while the limit for VMAT is 90%. Both the IMRT and VMAT QA processes are good quality because Cpml values are higher than 1.0.
NASA Astrophysics Data System (ADS)
Cheong, Kwang-Ho; Lee, Me-Yeon; Kang, Sei-Kwon; Yoon, Jai-Woong; Park, Soah; Hwang, Taejin; Kim, Haeyoung; Kim, Kyoung Ju; Han, Tae Jin; Bae, Hoonsik
2015-07-01
The aim of this study is to set up statistical quality control for monitoring the volumetric modulated arc therapy (VMAT) delivery error by using the machine's log data. Eclipse and a Clinac iX linac with the RapidArc system (Varian Medical Systems, Palo Alto, USA) are used for delivery of the VMAT plan. During the delivery of the RapidArc fields, the machine determines the delivered monitor units (MUs) and the gantry angle's position accuracy and the standard deviations of the MU ( σMU: dosimetric error) and the gantry angle ( σGA: geometric error) are displayed on the console monitor after completion of the RapidArc delivery. In the present study, first, the log data were analyzed to confirm its validity and usability; then, statistical process control (SPC) was applied to monitor the σMU and the σGA in a timely manner for all RapidArc fields: a total of 195 arc fields for 99 patients. The MU and the GA were determined twice for all fields, that is, first during the patient-specific plan QA and then again during the first treatment. The sMU and the σGA time series were quite stable irrespective of the treatment site; however, the sGA strongly depended on the gantry's rotation speed. The σGA of the RapidArc delivery for stereotactic body radiation therapy (SBRT) was smaller than that for the typical VMAT. Therefore, SPC was applied for SBRT cases and general cases respectively. Moreover, the accuracy of the potential meter of the gantry rotation is important because the σGA can change dramatically due to its condition. By applying SPC to the σMU and σGA, we could monitor the delivery error efficiently. However, the upper and the lower limits of SPC need to be determined carefully with full knowledge of the machine and log data.
Bayesian inference for psychology. Part II: Example applications with JASP.
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
2017-07-06
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.
Bayesian Face Sketch Synthesis.
Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie
2017-03-01
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
Bayesian homeopathy: talking normal again.
Rutten, A L B
2007-04-01
Homeopathy has a communication problem: important homeopathic concepts are not understood by conventional colleagues. Homeopathic terminology seems to be comprehensible only after practical experience of homeopathy. The main problem lies in different handling of diagnosis. In conventional medicine diagnosis is the starting point for randomised controlled trials to determine the effect of treatment. In homeopathy diagnosis is combined with other symptoms and personal traits of the patient to guide treatment and predict response. Broadening our scope to include diagnostic as well as treatment research opens the possibility of multi factorial reasoning. Adopting Bayesian methodology opens the possibility of investigating homeopathy in everyday practice and of describing some aspects of homeopathy in conventional terms.
Asymptotic analysis of Bayesian generalization error with Newton diagram.
Yamazaki, Keisuke; Aoyagi, Miki; Watanabe, Sumio
2010-01-01
Statistical learning machines that have singularities in the parameter space, such as hidden Markov models, Bayesian networks, and neural networks, are widely used in the field of information engineering. Singularities in the parameter space determine the accuracy of estimation in the Bayesian scenario. The Newton diagram in algebraic geometry is recognized as an effective method by which to investigate a singularity. The present paper proposes a new technique to plug the diagram in the Bayesian analysis. The proposed technique allows the generalization error to be clarified and provides a foundation for an efficient model selection. We apply the proposed technique to mixtures of binomial distributions.
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.
Childhood autism in India: A case-control study using tract-based spatial statistics analysis.
Assis, Zarina Abdul; Bagepally, Bhavani Shankara; Saini, Jitender; Srinath, Shoba; Bharath, Rose Dawn; Naidu, Purushotham R; Gupta, Arun Kumar
2015-01-01
Autism is a serious behavioral disorder among young children that now occurs at epidemic rates in developing countries like India. We have used tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) measures to investigate the microstructure of primary neurocircuitry involved in autistic spectral disorders as compared to the typically developed children. To evaluate the various white matter tracts in Indian autistic children as compared to the controls using TBSS. Prospective, case-control, voxel-based, whole-brain DTI analysis using TBSS was performed. The study included 19 autistic children (mean age 8.7 years ± 3.84, 16 males and 3 females) and 34 controls (mean age 12.38 ± 3.76, all males). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) values were used as outcome variables. Compared to the control group, TBSS demonstrated multiple areas of markedly reduced FA involving multiple long white matter tracts, entire corpus callosum, bilateral posterior thalami, and bilateral optic tracts (OTs). Notably, there were no voxels where FA was significantly increased in the autism group. Increased RD was also noted in these regions, suggesting underlying myelination defect. The MD was elevated in many of the projections and association fibers and notably in the OTs. There were no significant changes in the AD in these regions, indicating no significant axonal injury. There was no significant correlation between the FA values and Childhood Autism Rating Scale. This is a first of a kind study evaluating DTI findings in autistic children in India. In our study, DTI has shown a significant fault with the underlying intricate brain wiring system in autism. OT abnormality is a novel finding and needs further research.
Childhood autism in India: A case-control study using tract-based spatial statistics analysis
Assis, Zarina Abdul; Bagepally, Bhavani Shankara; Saini, Jitender; Srinath, Shoba; Bharath, Rose Dawn; Naidu, Purushotham R.; Gupta, Arun Kumar
2015-01-01
Context: Autism is a serious behavioral disorder among young children that now occurs at epidemic rates in developing countries like India. We have used tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) measures to investigate the microstructure of primary neurocircuitry involved in autistic spectral disorders as compared to the typically developed children. Objective: To evaluate the various white matter tracts in Indian autistic children as compared to the controls using TBSS. Materials and Methods: Prospective, case-control, voxel-based, whole-brain DTI analysis using TBSS was performed. The study included 19 autistic children (mean age 8.7 years ± 3.84, 16 males and 3 females) and 34 controls (mean age 12.38 ± 3.76, all males). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) values were used as outcome variables. Results: Compared to the control group, TBSS demonstrated multiple areas of markedly reduced FA involving multiple long white matter tracts, entire corpus callosum, bilateral posterior thalami, and bilateral optic tracts (OTs). Notably, there were no voxels where FA was significantly increased in the autism group. Increased RD was also noted in these regions, suggesting underlying myelination defect. The MD was elevated in many of the projections and association fibers and notably in the OTs. There were no significant changes in the AD in these regions, indicating no significant axonal injury. There was no significant correlation between the FA values and Childhood Autism Rating Scale. Conclusion: This is a first of a kind study evaluating DTI findings in autistic children in India. In our study, DTI has shown a significant fault with the underlying intricate brain wiring system in autism. OT abnormality is a novel finding and needs further research. PMID:26600581
Asensio Villahoz, Paula; Vicente Vírseda, Juan Antonio
2011-08-01
In undertaking a Minimum Basic Data Set (MBDS) of hospital discharges has to be done for all hospitals. It depends on the good function of the Medical Records Archive. The objective of this study is the continuous evaluation of the quality of the Medical Records Archive via Control Diagrams statistical techniques arose. From June 2005 to January 2009 a retrospective search of the discharges/Medical Records pending coding, from a tertiary care university hospital was carried out. The different categories of discharges/Medical Records were registered on an Excel spreadsheet under the following headings: searched for, found, completed/closed, pending and not located for each a particular month and year. The Overall Effectiveness Index was calculated, 0,9 being the standard quality value. Furthermore to assess the process quality over the entire length of the period studied and in each month and year, the Shewhart's Control Diagram and Cumulative Sum (CUSUM) graphic tests were applied. The Overall Effectiveness Index was 0.95. The majority of % errors corresponded to November 2008 with 55 (11.73%) and June 2008 with 14 (10.45%). In the Control Diagrams there are abnormally high values, obtained after standardization, in July 2005 (5.95) and November 2008 (7.00). The CUSUM showed one point outside of the initial control (July 2005 - 5.95), although until June 2007, the average (4.7%) was slightly below the overall averages (4.81%) and then increases from July 2007 on (5.02%). The Archive quality has varied over time, demonstrating that it was especially lower at the end of the study.
Quantification Of Margins And Uncertainties: A Bayesian Approach (full Paper)
Wallstrom, Timothy C
2008-01-01
Quantification of Margins and Uncertainties (QMU) is 'a formalism for dealing with the reliability of complex technical systems, and the confidence which can be placed in estimates of that reliability.' (Eardleyet al, 2005). In this paper, we show how QMU may be interpreted in the framework of Bayesian statistical inference, using a probabilistic network. The Bayesian approach clarifies the probabilistic underpinnings of the formalism, and shows how the formalism can be used for deciSion-making.
A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scena...
A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scena...
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.
NASA Astrophysics Data System (ADS)
Center, Julian L.; Knuth, Kevin H.
2011-03-01
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro-Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Statistical diagnostics emerging from external quality control of real-time PCR.
Marubini, E; Verderio, P; Raggi, Casini C; Pazzagli, M; Orlando, C
2004-01-01
Besides the application of conventional qualitative PCR as a valuable tool to enrich or identify specific sequences of nucleic acids, a new revolutionary technique for quantitative PCR determination has been introduced recently. It is based on real-time detection of PCR products revealed as a homogeneous accumulating signal generated by specific dyes. However, as far as we know, the influence of the variability of this technique on the reliability of the quantitative assay has not been thoroughly investigated. A national program of external quality assurance (EQA) for real-time PCR determination involving 42 Italian laboratories has been developed to assess the analytical performance of real-time PCR procedures. Participants were asked to perform a conventional experiment based on the use of an external reference curve (standard curve) for real-time detection of three cDNA samples with different concentrations of a specific target. In this paper the main analytical features of the standard curve have been investigated in an attempt to produce statistical diagnostics emerging from external quality control. Specific control charts were drawn to help biochemists take technical decisions aimed at improving the performance of their laboratories. Overall, our results indicated a subset of seven laboratories whose performance appeared to be markedly outside the limits for at least one of the standard curve features investigated. Our findings suggest the usefulness of the approach presented here for monitoring the heterogeneity of results produced by different laboratories and for selecting those laboratories that need technical advice on their performance.
McGrath, Leah J; Ellis, Alan R; Brookhart, M Alan
2015-07-01
Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias.
NASA Astrophysics Data System (ADS)
García-Díaz, J. Carlos
2009-11-01
Fault detection and diagnosis is an important problem in process engineering. Process equipments are subject to malfunctions during operation. Galvanized steel is a value added product, furnishing effective performance by combining the corrosion resistance of zinc with the strength and formability of steel. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing and the increasingly stringent quality requirements in automotive industry has also demanded ongoing efforts in process control to make the process more robust. When faults occur, they change the relationship among these observed variables. This work compares different statistical regression models proposed in the literature for estimating the quality of galvanized steel coils on the basis of short time histories. Data for 26 batches were available. Five variables were selected for monitoring the process: the steel strip velocity, four bath temperatures and bath level. The entire data consisting of 48 galvanized steel coils was divided into sets. The first training data set was 25 conforming coils and the second data set was 23 nonconforming coils. Logistic regression is a modeling tool in which the dependent variable is categorical. In most applications, the dependent variable is binary. The results show that the logistic generalized linear models do provide good estimates of quality coils and can be useful for quality control in manufacturing process.
NASA Astrophysics Data System (ADS)
Ma, Chao; An, Wei; Deng, Xinpu
2015-10-01
The Ground Control Points (GCPs) is an important source of fundamental data in geometric correction for remote sensing imagery. The quantity, accuracy and distribution of GCPs are three factors which may affect the accuracy of geometric correction. It is generally required that the distribution of GCP should be uniform, so they can fully control the accuracy of mapping regions. In this paper, we establish an objective standard of evaluating the uniformity of the GCPs' distribution based on regional statistical information (RSI), and get an optimal distribution of GCPs. This sampling method is called RSIS for short in this work. The Amounts of GCPs in different regions by equally partitioning the image in regions in different manners are counted which forms a vector called RSI vector in this work. The uniformity of GCPs' distribution can be evaluated by a mathematical quantity of the RSI vector. An optimal distribution of GCPs is obtained by searching the RSI vector with the minimum mathematical quantity. In this paper, the simulation annealing is employed to search the optimal distribution of GCPs that have the minimum mathematical quantity of the RSI vector. Experiments are carried out to test the method proposed in this paper, and sampling designs compared are simple random sampling and universal kriging model-based sampling. The experiments indicate that this method is highly recommended as new GCPs sampling design method for geometric correction of remotely sensed imagery.
Zhang, Shiyuan; Paul, James; Nantha-Aree, Manyat; Buckley, Norman; Shahzad, Uswa; Cheng, Ji; DeBeer, Justin; Winemaker, Mitchell; Wismer, David; Punthakee, Dinshaw; Avram, Victoria; Thabane, Lehana
2015-01-01
Background Postoperative pain management in total joint replacement surgery remains ineffective in up to 50% of patients and has an overwhelming impact in terms of patient well-being and health care burden. We present here an empirical analysis of two randomized controlled trials assessing whether addition of gabapentin to a multimodal perioperative analgesia regimen can reduce morphine consumption or improve analgesia for patients following total joint arthroplasty (the MOBILE trials). Methods Morphine consumption, measured for four time periods in patients undergoing total hip or total knee arthroplasty, was analyzed using a linear mixed-effects model to provide a longitudinal estimate of the treatment effect. Repeated-measures analysis of variance and generalized estimating equations were used in a sensitivity analysis to compare the robustness of the methods. Results There was no statistically significant difference in morphine consumption between the treatment group and a control group (mean effect size estimate 1.0, 95% confidence interval −4.7, 6.7, P=0.73). The results remained robust across different longitudinal methods. Conclusion The results of the current reanalysis of morphine consumption align with those of the MOBILE trials. Gabapentin did not significantly reduce morphine consumption in patients undergoing major replacement surgeries. The results remain consistent across longitudinal methods. More work in the area of postoperative pain is required to provide adequate management for this patient population. PMID:25709496
Bayesian Exploratory Factor Analysis
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
NASA Astrophysics Data System (ADS)
van den Brink, Cors; Frapporti, Giuseppe; Griffioen, Jasper; Zaadnoordijk, Willem Jan
2007-04-01
SummaryQuantitative insight into the impact of land use on groundwater composition is vital for the sustainable management of aquifers, especially those situated in urban areas. The objective of the present study was to determine the effects of land use on groundwater composition throughout the Netherlands, relative to the influence of groundwater age, groundwater origin, and geochemical processes. Using a nationwide dataset on shallow groundwater from the Dutch National Groundwater-Monitoring Network, land-use effects were quantified statistically for all major ionic substances. Analysis of variance was applied to the entire dataset, to a priori groups based on groundwater age, and to non-a priori statistical clusters determined by fuzzy c-means clustering. The results indicated that the effect of land use on groundwater composition in the Netherlands strongly depends on geochemical processes and groundwater age. Land-use effects were most pronounced in younger, geochemically less-evolved groundwater: for example, urban areas showed an increase in Ca, Na, Cl, and HCO 3 by a factor 2-4, an increase in K and B by a factor 4-10, and a decrease in Al by a factor 10 compared to natural areas. Similarly, agricultural areas showed an increase in Ca, NO 3, and Fe by a factor 2-4, an increase in K by a factor 4-10, and a decrease in Al by a factor 2. Compared to urban areas, agricultural areas contained less Na, Cl, and Al and more NO 3. Our results demonstrate that the importance of the processes controlling groundwater composition in the Netherlands is scale-dependent, with geochemical processes being more dominant on a national scale and land use on regional and local scales.
Exploring the use of statistical process control methods to assess course changes
NASA Astrophysics Data System (ADS)
Vollstedt, Ann-Marie
This dissertation pertains to the field of Engineering Education. The Department of Mechanical Engineering at the University of Nevada, Reno (UNR) is hosting this dissertation under a special agreement. This study was motivated by the desire to find an improved, quantitative measure of student quality that is both convenient to use and easy to evaluate. While traditional statistical analysis tools such as ANOVA (analysis of variance) are useful, they are somewhat time consuming and are subject to error because they are based on grades, which are influenced by numerous variables, independent of student ability and effort (e.g. inflation and curving). Additionally, grades are currently the only measure of quality in most engineering courses even though most faculty agree that grades do not accurately reflect student quality. Based on a literature search, in this study, quality was defined as content knowledge, cognitive level, self efficacy, and critical thinking. Nineteen treatments were applied to a pair of freshmen classes in an effort in increase the qualities. The qualities were measured via quiz grades, essays, surveys, and online critical thinking tests. Results from the quality tests were adjusted and filtered prior to analysis. All test results were subjected to Chauvenet's criterion in order to detect and remove outlying data. In addition to removing outliers from data sets, it was felt that individual course grades needed adjustment to accommodate for the large portion of the grade that was defined by group work. A new method was developed to adjust grades within each group based on the residual of the individual grades within the group and the portion of the course grade defined by group work. It was found that the grade adjustment method agreed 78% of the time with the manual ii grade changes instructors made in 2009, and also increased the correlation between group grades and individual grades. Using these adjusted grades, Statistical Process Control
SU-E-T-207: Flatness and Symmetry Threshold Detection Using Statistical Process Control.
Able, C; Hampton, C; Baydush, A
2012-06-01
AAPM TG-142 guidelines state that beam uniformity (flatness and symmetry) should maintain a constancy of 1 % relative to baseline. The focus of this study is to determine if statistical process control (SPC) methodology using process control charts (PCC) of steering coil currents (SCC) can detect changes in beam uniformity prior to exceeding the 1% constancy criteria. SCCs for the transverse and radial planes are adjusted such that a reproducibly useful photon or electron beam is available. Transverse and radial - positioning and angle SCC are routinely documented in the Morning Check file during daily warm-up. The 6 MV beam values for our linac were analyzed using average and range (Xbar/R) PCC. Using this data as a baseline, an experiment was performed in which each SCC was changed from its mean value (steps of 0.01 or 0.02 Ampere) while holding the other SCC constant. The effect on beam uniformity was measured using a beam scanning system. These experimental SCC values were plotted in the PCC to determine if they would exceed the predetermined limits. The change in SCC required to exceed the 1% constancy criteria was detected by the PCC for 3 out of the 4 steering coils. The reliability of the result in the one coil not detected (transverse position coil) is questionable because the SCC slowly drifted during the experiment (0.05 A) regardless of the servo control setting. X-bar/R charts of SCC can detect exceptional variation prior to exceeding the beam uniformity criteria set forth in AAPM TG-142. The high level of PCC sensitivity to change may result in an alarm when in fact minimal change in beam uniformity has occurred. Further study is needed to determine if a combination of individual SCC alarms would reduce the false positive rate for beam uniformity intervention. This project was supoorted by a grant from Varian Medical Systems, Inc. © 2012 American Association of Physicists in Medicine.
Bayesian Networks for Social Modeling
Whitney, Paul D.; White, Amanda M.; Walsh, Stephen J.; Dalton, Angela C.; Brothers, Alan J.
2011-03-28
This paper describes a body of work developed over the past five years. The work addresses the use of Bayesian network (BN) models for representing and predicting social/organizational behaviors. The topics covered include model construction, validation, and use. These topics show the bulk of the lifetime of such model, beginning with construction, moving to validation and other aspects of model ‘critiquing’, and finally demonstrating how the modeling approach might be used to inform policy analysis. To conclude, we discuss limitations of using BN for this activity and suggest remedies to address those limitations. The primary benefits of using a well-developed computational, mathematical, and statistical modeling structure, such as BN, are 1) there are significant computational, theoretical and capability bases on which to build 2) ability to empirically critique the model, and potentially evaluate competing models for a social/behavioral phenomena.