Hidden Markov latent variable models with multivariate longitudinal data.
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu
2017-03-01
Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.
Assessment of School Performance through a Multilevel Latent Markov Rasch Model
ERIC Educational Resources Information Center
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio
2011-01-01
An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, such as students clustered in classes. For each subject, a latent process is used to represent the characteristic of interest (e.g., ability) conditional on the effect of the cluster to…
ERIC Educational Resources Information Center
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio
2016-01-01
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Latent mixed Markov modelling of smoking transitions using Monte Carlo bootstrapping.
Mannan, Haider R; Koval, John J
2003-03-01
It has been established that measures and reports of smoking behaviours are subject to substantial measurement errors. Thus, the manifest Markov model which does not consider measurement error in observed responses may not be adequate to mathematically model changes in adolescent smoking behaviour over time. For this purpose we fit several Mixed Markov Latent Class (MMLC) models using data sets from two longitudinal panel studies--the third Waterloo Smoking Prevention study and the UWO smoking study, which have varying numbers of measurements on adolescent smoking behaviour. However, the conventional statistics used for testing goodness of fit of these models do not follow the theoretical chi-square distribution when there is data sparsity. The two data sets analysed had varying degrees of sparsity. This problem can be solved by estimating the proper distribution of fit measures using Monte Carlo bootstrap simulation. In this study, we showed that incorporating response uncertainty in smoking behaviour significantly improved the fit of a single Markov chain model. However, the single chain latent Markov model did not adequately fit the two data sets indicating that the smoking process was heterogeneous with regard to latent Markov chains. It was found that a higher percentage of students (except for never smokers) changed their smoking behaviours over time at the manifest level compared to the latent or true level. The smoking process generally accelerated with time. The students had a tendency to underreport their smoking behaviours while response uncertainty was estimated to be considerably less for the Waterloo smoking study which adopted the 'bogus pipeline' method for reducing measurement error while the UWO study did not. For the two-chain latent mixed Markov models, incorporating a 'stayer' chain to an unrestricted Markov chain led to a significant improvement in model fit for the UWO study only. For both data sets, the assumption for the existence of an
ERIC Educational Resources Information Center
Bockenholt, Ulf
2005-01-01
Markov models provide a general framework for analyzing and interpreting time dependencies in psychological applications. Recent work extended Markov models to the case of latent states because frequently psychological states are not directly observable and subject to measurement error. This article presents a further generalization of latent…
ERIC Educational Resources Information Center
Bartolucci, Francesco; Solis-Trapala, Ivonne L.
2010-01-01
We demonstrate the use of a multidimensional extension of the latent Markov model to analyse data from studies with repeated binary responses in developmental psychology. In particular, we consider an experiment based on a battery of tests which was administered to pre-school children, at three time periods, in order to measure their inhibitory…
Modeling threat assessments of water supply systems using markov latent effects methodology.
Silva, Consuelo Juanita
2006-12-01
Recent amendments to the Safe Drinking Water Act emphasize efforts toward safeguarding our nation's water supplies against attack and contamination. Specifically, the Public Health Security and Bioterrorism Preparedness and Response Act of 2002 established requirements for each community water system serving more than 3300 people to conduct an assessment of the vulnerability of its system to a terrorist attack or other intentional acts. Integral to evaluating system vulnerability is the threat assessment, which is the process by which the credibility of a threat is quantified. Unfortunately, full probabilistic assessment is generally not feasible, as there is insufficient experience and/or data to quantify the associated probabilities. For this reason, an alternative approach is proposed based on Markov Latent Effects (MLE) modeling, which provides a framework for quantifying imprecise subjective metrics through possibilistic or fuzzy mathematics. Here, an MLE model for water systems is developed and demonstrated to determine threat assessments for different scenarios identified by the assailant, asset, and means. Scenario assailants include terrorists, insiders, and vandals. Assets include a water treatment plant, water storage tank, node, pipeline, well, and a pump station. Means used in attacks include contamination (onsite chemicals, biological and chemical), explosives and vandalism. Results demonstrated highest threats are vandalism events and least likely events are those performed by a terrorist.
Bartolucci, Francesco; Pennoni, Fulvia
2007-06-01
We propose an extension of the latent class model for the analysis of capture-recapture data which allows us to take into account the effect of a capture on the behavior of a subject with respect to future captures. The approach is based on the assumption that the variable indexing the latent class of a subject follows a Markov chain with transition probabilities depending on the previous capture history. Several constraints are allowed on these transition probabilities and on the parameters of the conditional distribution of the capture configuration given the latent process. We also allow for the presence of discrete explanatory variables, which may affect the parameters of the latent process. To estimate the resulting models, we rely on the conditional maximum likelihood approach and for this aim we outline an EM algorithm. We also give some simple rules for point and interval estimation of the population size. The approach is illustrated by applying it to two data sets concerning small mammal populations.
Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model
Moses, Mark W.; Zwerling, Alice; Cattamanchi, Adithya; Denkinger, Claudia M.; Banaei, Niaz; Kik, Sandra V.; Metcalfe, John; Pai, Madhukar; Dowdy, David
2016-01-01
Healthcare workers (HCWs) in low-incidence settings are often serially tested for latent TB infection (LTBI) with the QuantiFERON-TB Gold In-Tube (QFT) assay, which exhibits frequent conversions and reversions. The clinical impact of such variability on serial testing remains unknown. We used a microsimulation Markov model that accounts for major sources of variability to project diagnostic outcomes in a simulated North American HCW cohort. Serial testing using a single QFT with the recommended conversion cutoff (IFN-g > 0.35 IU/mL) resulted in 24.6% (95% uncertainty range, UR: 23.8–25.5) of the entire population testing false-positive over ten years. Raising the cutoff to >1.0 IU/mL or confirming initial positive results with a (presumed independent) second test reduced this false-positive percentage to 2.3% (95%UR: 2.0–2.6%) or 4.1% (95%UR: 3.7–4.5%), but also reduced the proportion of true incident infections detected within the first year of infection from 76.5% (95%UR: 66.3–84.6%) to 54.8% (95%UR: 44.6–64.5%) or 61.5% (95%UR: 51.6–70.9%), respectively. Serial QFT testing of HCWs in North America may result in tremendous over-diagnosis and over-treatment of LTBI, with nearly thirty false-positives for every true infection diagnosed. Using higher cutoffs for conversion or confirmatory tests (for initial positives) can mitigate these effects, but will also diagnose fewer true infections. PMID:27469388
Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model.
Moses, Mark W; Zwerling, Alice; Cattamanchi, Adithya; Denkinger, Claudia M; Banaei, Niaz; Kik, Sandra V; Metcalfe, John; Pai, Madhukar; Dowdy, David
2016-07-29
Healthcare workers (HCWs) in low-incidence settings are often serially tested for latent TB infection (LTBI) with the QuantiFERON-TB Gold In-Tube (QFT) assay, which exhibits frequent conversions and reversions. The clinical impact of such variability on serial testing remains unknown. We used a microsimulation Markov model that accounts for major sources of variability to project diagnostic outcomes in a simulated North American HCW cohort. Serial testing using a single QFT with the recommended conversion cutoff (IFN-g > 0.35 IU/mL) resulted in 24.6% (95% uncertainty range, UR: 23.8-25.5) of the entire population testing false-positive over ten years. Raising the cutoff to >1.0 IU/mL or confirming initial positive results with a (presumed independent) second test reduced this false-positive percentage to 2.3% (95%UR: 2.0-2.6%) or 4.1% (95%UR: 3.7-4.5%), but also reduced the proportion of true incident infections detected within the first year of infection from 76.5% (95%UR: 66.3-84.6%) to 54.8% (95%UR: 44.6-64.5%) or 61.5% (95%UR: 51.6-70.9%), respectively. Serial QFT testing of HCWs in North America may result in tremendous over-diagnosis and over-treatment of LTBI, with nearly thirty false-positives for every true infection diagnosed. Using higher cutoffs for conversion or confirmatory tests (for initial positives) can mitigate these effects, but will also diagnose fewer true infections.
Phillips, Joe Scutt; Patterson, Toby A; Leroy, Bruno; Pilling, Graham M; Nicol, Simon J
2015-07-01
Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions. Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching. Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna. HMMs provide easily interpretable models for the objective classification of
Koukounari, Artemis; Donnelly, Christl A.; Moustaki, Irini; Tukahebwa, Edridah M.; Kabatereine, Narcis B.; Wilson, Shona; Webster, Joanne P.; Deelder, André M.; Vennervald, Birgitte J.; van Dam, Govert J.
2013-01-01
Regular treatment with praziquantel (PZQ) is the strategy for human schistosomiasis control aiming to prevent morbidity in later life. With the recent resolution on schistosomiasis elimination by the 65th World Health Assembly, appropriate diagnostic tools to inform interventions are keys to their success. We present a discrete Markov chains modelling framework that deals with the longitudinal study design and the measurement error in the diagnostic methods under study. A longitudinal detailed dataset from Uganda, in which one or two doses of PZQ treatment were provided, was analyzed through Latent Markov Models (LMMs). The aim was to evaluate the diagnostic accuracy of Circulating Cathodic Antigen (CCA) and of double Kato-Katz (KK) faecal slides over three consecutive days for Schistosoma mansoni infection simultaneously by age group at baseline and at two follow-up times post treatment. Diagnostic test sensitivities and specificities and the true underlying infection prevalence over time as well as the probabilities of transitions between infected and uninfected states are provided. The estimated transition probability matrices provide parsimonious yet important insights into the re-infection and cure rates in the two age groups. We show that the CCA diagnostic performance remained constant after PZQ treatment and that this test was overall more sensitive but less specific than single-day double KK for the diagnosis of S. mansoni infection. The probability of clearing infection from baseline to 9 weeks was higher among those who received two PZQ doses compared to one PZQ dose for both age groups, with much higher re-infection rates among children compared to adolescents and adults. We recommend LMMs as a useful methodology for monitoring and evaluation and treatment decision research as well as CCA for mapping surveys of S. mansoni infection, although additional diagnostic tools should be incorporated in schistosomiasis elimination programs. PMID:24367250
Two Studies of Specification Error in Models for Categorical Latent Variables
ERIC Educational Resources Information Center
Kaplan, David; Depaoli, Sarah
2011-01-01
This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as…
Generalized Latent Trait Models.
ERIC Educational Resources Information Center
Moustaki, Irini; Knott, Martin
2000-01-01
Discusses a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. Presents a unified maximum likelihood method for estimating the parameters of the generalized latent trait model and discusses the scoring of individuals on the latent dimensions.…
Abstraction Augmented Markov Models.
Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina; Honavar, Vasant
2010-12-13
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of overfitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric parameters of k(th) order MMs by successively grouping strings of length k (i.e., k-grams) into abstraction hierarchies. We evaluate AAMMs on three protein subcellular localization prediction tasks. The results of our experiments show that abstraction makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) as compared to MMs. AAMMs are competitive with and, in some cases, significantly outperform MMs. Moreover, the results show that AAMMs often perform significantly better than variable order Markov models, such as decomposed context tree weighting, prediction by partial match, and probabilistic suffix trees.
Latent Variable Interaction Modeling.
ERIC Educational Resources Information Center
Schumacker, Randall E.
2002-01-01
Used simulation to study two different approaches to latent variable interaction modeling with continuous observed variables: (1) a LISREL 8.30 program and (2) data analysis through PRELIS2 and SIMPLIS programs. Results show that parameter estimation was similar but standard errors were different. Discusses differences in ease of implementation.…
Regime Switching in the Latent Growth Curve Mixture Model
ERIC Educational Resources Information Center
Dolan, Conor V.; Schmittmann, Verena D.; Lubke, Gitta H.; Neale, Michael C.
2005-01-01
A linear latent growth curve mixture model is presented which includes switching between growth curves. Switching is accommodated by means of a Markov transition model. The model is formulated with switching as a highly constrained multivariate mixture model and is fitted using the freely available Mx program. The model is illustrated by analyzing…
Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models
NASA Astrophysics Data System (ADS)
Chan, Lay Guat; Ibrahim, Adriana Irawati Nur Binti
2016-10-01
A hidden Markov model (HMM) is a mixture model which has a Markov chain with finite states as its mixing distribution. HMMs have been applied to a variety of fields, such as speech and face recognitions. The main purpose of this study is to investigate the Bayesian approach to HMMs. Using this approach, we can simulate from the parameters' posterior distribution using some Markov chain Monte Carlo (MCMC) sampling methods. HMMs seem to be useful, but there are some limitations. Therefore, by using the Mixture of Dirichlet processes Hidden Markov Model (MDPHMM) based on Yau et. al (2011), we hope to overcome these limitations. We shall conduct a simulation study using MCMC methods to investigate the performance of this model.
A MCMC-Method for Models with Continuous Latent Responses.
ERIC Educational Resources Information Center
Maris, Gunter; Maris, Eric
2002-01-01
Introduces a new technique for estimating the parameters of models with continuous latent data. To streamline presentation of this Markov Chain Monte Carlo (MCMC) method, the Rasch model is used. Also introduces a new sampling-based Bayesian technique, the DA-T-Gibbs sampler. (SLD)
An Overview of Markov Chain Methods for the Study of Stage-Sequential Developmental Processes
ERIC Educational Resources Information Center
Kapland, David
2008-01-01
This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model.…
A Latent Transition Model with Logistic Regression
ERIC Educational Resources Information Center
Chung, Hwan; Walls, Theodore A.; Park, Yousung
2007-01-01
Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…
Building Simple Hidden Markov Models. Classroom Notes
ERIC Educational Resources Information Center
Ching, Wai-Ki; Ng, Michael K.
2004-01-01
Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.
An introduction to hidden Markov models.
Schuster-Böckler, Benjamin; Bateman, Alex
2007-06-01
This unit introduces the concept of hidden Markov models in computational biology. It describes them using simple biological examples, requiring as little mathematical knowledge as possible. The unit also presents a brief history of hidden Markov models and an overview of their current applications before concluding with a discussion of their limitations.
Estimation in Latent Trait Models.
ERIC Educational Resources Information Center
Rigdon, Steven E.; Tsutakawa, Robert K.
Estimation of ability and item parameters in latent trait models is discussed. When both ability and item parameters are considered fixed but unknown, the method of maximum likelihood for the logistic or probit models is well known. Discussed are techniques for estimating ability and item parameters when the ability parameters or item parameters…
A Latent Class Model for Rating Data.
ERIC Educational Resources Information Center
Rost, Jurgen
1985-01-01
A latent class model for rating data is presented which provides an alternative to the latent trait approach of analyzing test data. It is the analog of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). Response probabilities for rating categories follow a binomial distribution and depend on class-specific item…
Latent Growth Modeling for Logistic Response Functions
ERIC Educational Resources Information Center
Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.
2009-01-01
Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…
[Decision analysis in radiology using Markov models].
Golder, W
2000-01-01
Markov models (Multistate transition models) are mathematical tools to simulate a cohort of individuals followed over time to assess the prognosis resulting from different strategies. They are applied on the assumption that persons are in one of a finite number of states of health (Markov states). Each condition is given a transition probability as well as an incremental value. Probabilities may be chosen constant or varying over time due to predefined rules. Time horizon is divided into equal increments (Markov cycles). The model calculates quality-adjusted life expectancy employing real-life units and values and summing up the length of time spent in each health state adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common decision trees. Markov models can be evaluated by matrix algebra, probabilistic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic and therapeutic procedures in radiology. More interventions should be submitted to Markov analyses in order to elucidate their cost-effectiveness.
Latent Class Models for Diary Method Data: Parameter Estimation by Local Computations
ERIC Educational Resources Information Center
Rijmen, Frank; Vansteelandt, Kristof; De Boeck, Paul
2008-01-01
The increasing use of diary methods calls for the development of appropriate statistical methods. For the resulting panel data, latent Markov models can be used to model both individual differences and temporal dynamics. The computational burden associated with these models can be overcome by exploiting the conditional independence relations…
Consequences of Fitting Nonidentified Latent Class Models
ERIC Educational Resources Information Center
Abar, Beau; Loken, Eric
2012-01-01
Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…
Evaluation of Usability Utilizing Markov Models
ERIC Educational Resources Information Center
Penedo, Janaina Rodrigues; Diniz, Morganna; Ferreira, Simone Bacellar Leal; Silveira, Denis S.; Capra, Eliane
2012-01-01
Purpose: The purpose of this paper is to analyze the usability of a remote learning system in its initial development phase, using a quantitative usability evaluation method through Markov models. Design/methodology/approach: The paper opted for an exploratory study. The data of interest of the research correspond to the possible accesses of users…
A Vernacular for Linear Latent Growth Models
ERIC Educational Resources Information Center
Hancock, Gregory R.; Choi, Jaehwa
2006-01-01
In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…
Bayesian variable selection for latent class models.
Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria
2011-09-01
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.
Estimating Neuronal Ageing with Hidden Markov Models
NASA Astrophysics Data System (ADS)
Wang, Bing; Pham, Tuan D.
2011-06-01
Neuronal degeneration is widely observed in normal ageing, meanwhile the neurode-generative disease like Alzheimer's disease effects neuronal degeneration in a faster way which is considered as faster ageing. Early intervention of such disease could benefit subjects with potentials of positive clinical outcome, therefore, early detection of disease related brain structural alteration is required. In this paper, we propose a computational approach for modelling the MRI-based structure alteration with ageing using hidden Markov model. The proposed hidden Markov model based brain structural model encodes intracortical tissue/fluid distribution using discrete wavelet transformation and vector quantization. Further, it captures gray matter volume loss, which is capable of reflecting subtle intracortical changes with ageing. Experiments were carried out on healthy subjects to validate its accuracy and robustness. Results have shown its ability of predicting the brain age with prediction error of 1.98 years without training data, which shows better result than other age predition methods.
ERIC Educational Resources Information Center
Stifter, Cynthia A.; Rovine, Michael
2015-01-01
The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…
A critical appraisal of Markov state models
NASA Astrophysics Data System (ADS)
Schütte, Ch.; Sarich, M.
2015-09-01
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a molecular system in equilibrium has gained a lot of attention recently. The last 10 years have seen an ever increasing publication activity on how to construct Markov State Models (MSMs) for very different molecular systems ranging from peptides to proteins, from RNA to DNA, and via molecular sensors to molecular aggregation. Simultaneously the accompanying theory behind MSM building and approximation quality has been developed well beyond the concepts and ideas used in practical applications. This article reviews the main theoretical results, provides links to crucial new developments, outlines the full power of MSM building today, and discusses the essential limitations still to overcome.
Phase transitions in Hidden Markov Models
NASA Astrophysics Data System (ADS)
Bechhoefer, John; Lathouwers, Emma
In Hidden Markov Models (HMMs), a Markov process is not directly accessible. In the simplest case, a two-state Markov model ``emits'' one of two ``symbols'' at each time step. We can think of these symbols as noisy measurements of the underlying state. With some probability, the symbol implies that the system is in one state when it is actually in the other. The ability to judge which state the system is in sets the efficiency of a Maxwell demon that observes state fluctuations in order to extract heat from a coupled reservoir. The state-inference problem is to infer the underlying state from such noisy measurements at each time step. We show that there can be a phase transition in such measurements: for measurement error rates below a certain threshold, the inferred state always matches the observation. For higher error rates, there can be continuous or discontinuous transitions to situations where keeping a memory of past observations improves the state estimate. We can partly understand this behavior by mapping the HMM onto a 1d random-field Ising model at zero temperature. We also present more recent work that explores a larger parameter space and more states. Research funded by NSERC, Canada.
Hidden Markov Model Analysis of Multichromophore Photobleaching
Messina, Troy C.; Kim, Hiyun; Giurleo, Jason T.; Talaga, David S.
2007-01-01
The interpretation of single-molecule measurements is greatly complicated by the presence of multiple fluorescent labels. However, many molecular systems of interest consist of multiple interacting components. We investigate this issue using multiply labeled dextran polymers that we intentionally photobleach to the background on a single-molecule basis. Hidden Markov models allow for unsupervised analysis of the data to determine the number of fluorescent subunits involved in the fluorescence intermittency of the 6-carboxy-tetramethylrhodamine labels by counting the discrete steps in fluorescence intensity. The Bayes information criterion allows us to distinguish between hidden Markov models that differ by the number of states, that is, the number of fluorescent molecules. We determine information-theoretical limits and show via Monte Carlo simulations that the hidden Markov model analysis approaches these theoretical limits. This technique has resolving power of one fluorescing unit up to as many as 30 fluorescent dyes with the appropriate choice of dye and adequate detection capability. We discuss the general utility of this method for determining aggregation-state distributions as could appear in many biologically important systems and its adaptability to general photometric experiments. PMID:16913765
Variable Assessment in Latent Class Models
Zhang, Q.; Ip, E. H.
2014-01-01
The latent class model provides an important platform for jointly modeling mixed-mode data — i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures. PMID:24910486
A new constant memory recursion for hidden Markov models.
Bartolucci, Francesco; Pandolfi, Silvia
2014-02-01
We develop the recursion for hidden Markov (HM) models proposed by Bartolucci and Besag (2002), and we show how it may be used to implement an estimation algorithm for these models that requires an amount of memory not depending on the length of the observed series of data. This recursion allows us to obtain the conditional distribution of the latent state at every occasion, given the previous state and the observed data. With respect to the estimation algorithm based on the well-known Baum-Welch recursions, which requires an amount of memory that increases with the sample size, the proposed algorithm also has the advantage of not requiring dummy renormalizations to avoid numerical problems. Moreover, it directly allows us to perform global decoding of the latent sequence of states, without the need of a Viterbi method and with a consistent reduction of the memory requirement with respect to the latter. The proposed approach is compared, in terms of computing time and memory requirement, with the algorithm based on the Baum-Welch recursions and with the so-called linear memory algorithm of Churbanov and Winters-Hilt. The comparison is also based on a series of simulations involving an HM model for continuous time-series data.
Constructing Dynamic Event Trees from Markov Models
Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood
2006-05-01
In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: a) partitioning the process variable state space into magnitude intervals (cells), b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank
Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin
2014-01-01
Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.
An overview of Markov chain methods for the study of stage-sequential developmental processes.
Kapland, David
2008-03-01
This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model. A special case of the mixture latent Markov model, the so-called mover-stayer model, is used in this study. Unconditional and conditional models are estimated for the manifest Markov model and the latent Markov model, where the conditional models include a measure of poverty status. Issues of model specification, estimation, and testing using the Mplus software environment are briefly discussed, and the Mplus input syntax is provided. The author applies these 4 methods to a single example of stage-sequential development in reading competency in the early school years, using data from the Early Childhood Longitudinal Study--Kindergarten Cohort.
ERIC Educational Resources Information Center
Kieftenbeld, Vincent; Natesan, Prathiba
2012-01-01
Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…
Modeling Interaction Effects in Latent Growth Curve Models.
ERIC Educational Resources Information Center
Li, Fuzhong; Duncan, Terry E.; Acock, Alan
2000-01-01
Presents an extension of the method of estimating interaction effects among latent variables to latent growth curve models developed by K. Joreskog and F. Yang (1996). Illustrates the procedure and discusses results in terms of practical and statistical problems associated with interaction analyses in latent curve models and structural equation…
Multivariate Markov chain modeling for stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Extended Generalized Linear Latent and Mixed Model
ERIC Educational Resources Information Center
Segawa, Eisuke; Emery, Sherry; Curry, Susan J.
2008-01-01
The generalized linear latent and mixed modeling (GLLAMM framework) includes many models such as hierarchical and structural equation models. However, GLLAMM cannot currently accommodate some models because it does not allow some parameters to be random. GLLAMM is extended to overcome the limitation by adding a submodel that specifies a…
Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses
ERIC Educational Resources Information Center
Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu
2011-01-01
Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…
Latent Curve Models and Latent Change Score Models Estimated in R
ERIC Educational Resources Information Center
Ghisletta, Paolo; McArdle, John J.
2012-01-01
In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…
Markov state models of biomolecular conformational dynamics
Chodera, John D.; Noé, Frank
2014-01-01
It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges. PMID:24836551
Estimation and uncertainty of reversible Markov models
NASA Astrophysics Data System (ADS)
Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank
2015-11-01
Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software — http://pyemma.org — as of version 2.0.
Neale, Michael C.; Clark, Shaunna L.; Dolan, Conor V.; Hunter, Michael D.
2015-01-01
A linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of first-order Markov switching probabilities was specified to be time-invariant, regardless of the pair of occasions being considered. The first extension, time-varying transitions, specifies different Markov transition matrices between each pair of occasions. The second extension is second-order time-invariant Markov transition probabilities, such that the probability of switching depends on the states at the 2 previous occasions. The models are implemented using the R package OpenMx, which facilitates data handling, parallel computation, and further model development. It also enables the extraction and display of relative likelihoods for every individual in the sample. The models are illustrated with previously published data on alcohol use observed on 4 occasions as part of the National Longitudinal Survey of Youth, and demonstrate improved fit to the data. PMID:26924921
Metagenomic Classification Using an Abstraction Augmented Markov Model
Zhu, Xiujun (Sylvia)
2016-01-01
Abstract The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification. PMID:26618474
Markov state models and molecular alchemy
NASA Astrophysics Data System (ADS)
Schütte, Christof; Nielsen, Adam; Weber, Marcus
2015-01-01
In recent years, Markov state models (MSMs) have attracted a considerable amount of attention with regard to modelling conformation changes and associated function of biomolecular systems. They have been used successfully, e.g. for peptides including time-resolved spectroscopic experiments, protein function and protein folding , DNA and RNA, and ligand-receptor interaction in drug design and more complicated multivalent scenarios. In this article, a novel reweighting scheme is introduced that allows to construct an MSM for certain molecular system out of an MSM for a similar system. This permits studying how molecular properties on long timescales differ between similar molecular systems without performing full molecular dynamics simulations for each system under consideration. The performance of the reweighting scheme is illustrated for simple test cases, including one where the main wells of the respective energy landscapes are located differently and an alchemical transformation of butane to pentane where the dimension of the state space is changed.
Probabilistic Resilience in Hidden Markov Models
NASA Astrophysics Data System (ADS)
Panerati, Jacopo; Beltrame, Giovanni; Schwind, Nicolas; Zeltner, Stefan; Inoue, Katsumi
2016-05-01
Originally defined in the context of ecological systems and environmental sciences, resilience has grown to be a property of major interest for the design and analysis of many other complex systems: resilient networks and robotics systems other the desirable capability of absorbing disruption and transforming in response to external shocks, while still providing the services they were designed for. Starting from an existing formalization of resilience for constraint-based systems, we develop a probabilistic framework based on hidden Markov models. In doing so, we introduce two new important features: stochastic evolution and partial observability. Using our framework, we formalize a methodology for the evaluation of probabilities associated with generic properties, we describe an efficient algorithm for the computation of its essential inference step, and show that its complexity is comparable to other state-of-the-art inference algorithms.
Modelling modal gating of ion channels with hierarchical Markov models
Fackrell, Mark; Crampin, Edmund J.; Taylor, Peter
2016-01-01
Many ion channels spontaneously switch between different levels of activity. Although this behaviour known as modal gating has been observed for a long time it is currently not well understood. Despite the fact that appropriately representing activity changes is essential for accurately capturing time course data from ion channels, systematic approaches for modelling modal gating are currently not available. In this paper, we develop a modular approach for building such a model in an iterative process. First, stochastic switching between modes and stochastic opening and closing within modes are represented in separate aggregated Markov models. Second, the continuous-time hierarchical Markov model, a new modelling framework proposed here, then enables us to combine these components so that in the integrated model both mode switching as well as the kinetics within modes are appropriately represented. A mathematical analysis reveals that the behaviour of the hierarchical Markov model naturally depends on the properties of its components. We also demonstrate how a hierarchical Markov model can be parametrized using experimental data and show that it provides a better representation than a previous model of the same dataset. Because evidence is increasing that modal gating reflects underlying molecular properties of the channel protein, it is likely that biophysical processes are better captured by our new approach than in earlier models. PMID:27616917
Markov source model for printed music decoding
NASA Astrophysics Data System (ADS)
Kopec, Gary E.; Chou, Philip A.; Maltz, David A.
1995-03-01
This paper describes a Markov source model for a simple subset of printed music notation. The model is based on the Adobe Sonata music symbol set and a message language of our own design. Chord imaging is the most complex part of the model. Much of the complexity follows from a rule of music typography that requires the noteheads for adjacent pitches to be placed on opposite sides of the chord stem. This rule leads to a proliferation of cases for other typographic details such as dot placement. We describe the language of message strings accepted by the model and discuss some of the imaging issues associated with various aspects of the message language. We also point out some aspects of music notation that appear problematic for a finite-state representation. Development of the model was greatly facilitated by the duality between image synthesis and image decoding. Although our ultimate objective was a music image model for use in decoding, most of the development proceeded by using the evolving model for image synthesis, since it is computationally far less costly to image a message than to decode an image.
Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
Ionides, Edward L.; Nguyen, Dao; Atchadé, Yves; Stoev, Stilian; King, Aaron A.
2015-01-01
Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process. PMID:25568084
Manpower planning using Markov Chain model
NASA Astrophysics Data System (ADS)
Saad, Syafawati Ab; Adnan, Farah Adibah; Ibrahim, Haslinda; Rahim, Rahela
2014-07-01
Manpower planning is a planning model which understands the flow of manpower based on the policies changes. For such purpose, numerous attempts have been made by researchers to develop a model to investigate the track of movements of lecturers for various universities. As huge number of lecturers in a university, it is difficult to track the movement of lecturers and also there is no quantitative way used in tracking the movement of lecturers. This research is aimed to determine the appropriate manpower model to understand the flow of lecturers in a university in Malaysia by determine the probability and mean time of lecturers remain in the same status rank. In addition, this research also intended to estimate the number of lecturers in different status rank (lecturer, senior lecturer and associate professor). From the previous studies, there are several methods applied in manpower planning model and appropriate method used in this research is Markov Chain model. Results obtained from this study indicate that the appropriate manpower planning model used is validated by compare to the actual data. The smaller margin of error gives a better result which means that the projection is closer to actual data. These results would give some suggestions for the university to plan the hiring lecturers and budgetary for university in future.
Component Latent Trait Models for Test Design.
ERIC Educational Resources Information Center
Embretson, Susan Whitely
Latent trait models are presented that can be used for test design in the context of a theory about the variables that underlie task performance. Examples of methods for decomposing and testing hypotheses about the theoretical variables in task performance are given. The methods can be used to determine the processing components that are involved…
Hidden Markov models in automatic speech recognition
NASA Astrophysics Data System (ADS)
Wrzoskowicz, Adam
1993-11-01
This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.
Markov chains and semi-Markov models in time-to-event analysis
Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.
2014-01-01
A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062
Modeling anger and aggressive driving behavior in a dynamic choice-latent variable model.
Danaf, Mazen; Abou-Zeid, Maya; Kaysi, Isam
2015-02-01
This paper develops a hybrid choice-latent variable model combined with a Hidden Markov model in order to analyze the causes of aggressive driving and forecast its manifestations accordingly. The model is grounded in the state-trait anger theory; it treats trait driving anger as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, and state anger as a dynamic latent variable that evolves over time and affects driving behavior, and that is expressed as a function of trait anger, frustrating events, and contextual variables (e.g., geometric roadway features, flow conditions, etc.). This model may be used in order to test measures aimed at reducing aggressive driving behavior and improving road safety, and can be incorporated into micro-simulation packages to represent aggressive driving. The paper also presents an application of this model to data obtained from a driving simulator experiment performed at the American University of Beirut. The results derived from this application indicate that state anger at a specific time period is significantly affected by the occurrence of frustrating events, trait anger, and the anger experienced at the previous time period. The proposed model exhibited a better goodness of fit compared to a similar simple joint model where driving behavior and decisions are expressed as a function of the experienced events explicitly and not the dynamic latent variable.
Markov state modeling of sliding friction.
Pellegrini, F; Landes, François P; Laio, A; Prestipino, S; Tosatti, E
2016-11-01
Markov state modeling (MSM) has recently emerged as one of the key techniques for the discovery of collective variables and the analysis of rare events in molecular simulations. In particular in biochemistry this approach is successfully exploited to find the metastable states of complex systems and their evolution in thermal equilibrium, including rare events, such as a protein undergoing folding. The physics of sliding friction and its atomistic simulations under external forces constitute a nonequilibrium field where relevant variables are in principle unknown and where a proper theory describing violent and rare events such as stick slip is still lacking. Here we show that MSM can be extended to the study of nonequilibrium phenomena and in particular friction. The approach is benchmarked on the Frenkel-Kontorova model, used here as a test system whose properties are well established. We demonstrate that the method allows the least prejudiced identification of a minimal basis of natural microscopic variables necessary for the description of the forced dynamics of sliding, through their probabilistic evolution. The steps necessary for the application to realistic frictional systems are highlighted.
Markov state modeling of sliding friction
NASA Astrophysics Data System (ADS)
Pellegrini, F.; Landes, François P.; Laio, A.; Prestipino, S.; Tosatti, E.
2016-11-01
Markov state modeling (MSM) has recently emerged as one of the key techniques for the discovery of collective variables and the analysis of rare events in molecular simulations. In particular in biochemistry this approach is successfully exploited to find the metastable states of complex systems and their evolution in thermal equilibrium, including rare events, such as a protein undergoing folding. The physics of sliding friction and its atomistic simulations under external forces constitute a nonequilibrium field where relevant variables are in principle unknown and where a proper theory describing violent and rare events such as stick slip is still lacking. Here we show that MSM can be extended to the study of nonequilibrium phenomena and in particular friction. The approach is benchmarked on the Frenkel-Kontorova model, used here as a test system whose properties are well established. We demonstrate that the method allows the least prejudiced identification of a minimal basis of natural microscopic variables necessary for the description of the forced dynamics of sliding, through their probabilistic evolution. The steps necessary for the application to realistic frictional systems are highlighted.
Stochastic Approximation Methods for Latent Regression Item Response Models
ERIC Educational Resources Information Center
von Davier, Matthias; Sinharay, Sandip
2010-01-01
This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…
Stochastic motif extraction using hidden Markov model
Fujiwara, Yukiko; Asogawa, Minoru; Konagaya, Akihiko
1994-12-31
In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. A stochastic protein motif represents the small segments of protein sequences that have a certain function or structure. The stochastic motif, represented by an HMM, has conditional probabilities to deal with the stochastic nature of the motif. This HMM directive reflects the characteristics of the motif, such as a protein periodical structure or grouping. In order to obtain the optimal HMM, we developed the {open_quotes}iterative duplication method{close_quotes} for HMM topology learning. It starts from a small fully-connected network and iterates the network generation and parameter optimization until it achieves sufficient discrimination accuracy. Using this method, we obtained an HMM for a leucine zipper motif. Compared to the accuracy of a symbolic pattern representation with accuracy of 14.8 percent, an HMM achieved 79.3 percent in prediction. Additionally, the method can obtain an HMM for various types of zinc finger motifs, and it might separate the mixed data. We demonstrated that this approach is applicable to the validation of the protein databases; a constructed HMM b as indicated that one protein sequence annotated as {open_quotes}lencine-zipper like sequence{close_quotes} in the database is quite different from other leucine-zipper sequences in terms of likelihood, and we found this discrimination is plausible.
Clustering metagenomic sequences with interpolated Markov models
2010-01-01
Background Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects. Results We present SCIMM (Sequence Clustering with Interpolated Markov Models), an unsupervised sequence clustering method. SCIMM achieves greater clustering accuracy than previous unsupervised approaches. We examine the limitations of unsupervised learning on complex datasets, and suggest a hybrid of SCIMM and supervised learning method Phymm called PHYSCIMM that performs better when evolutionarily close training genomes are available. Conclusions SCIMM and PHYSCIMM are highly accurate methods to cluster metagenomic sequences. SCIMM operates entirely unsupervised, making it ideal for environments containing mostly novel microbes. PHYSCIMM uses supervised learning to improve clustering in environments containing microbial strains from well-characterized genera. SCIMM and PHYSCIMM are available open source from http://www.cbcb.umd.edu/software/scimm. PMID:21044341
Medical Inpatient Journey Modeling and Clustering: A Bayesian Hidden Markov Model Based Approach
Huang, Zhengxing; Dong, Wei; Wang, Fei; Duan, Huilong
2015-01-01
Modeling and clustering medical inpatient journeys is useful to healthcare organizations for a number of reasons including inpatient journey reorganization in a more convenient way for understanding and browsing, etc. In this study, we present a probabilistic model-based approach to model and cluster medical inpatient journeys. Specifically, we exploit a Bayesian Hidden Markov Model based approach to transform medical inpatient journeys into a probabilistic space, which can be seen as a richer representation of inpatient journeys to be clustered. Then, using hierarchical clustering on the matrix of similarities, inpatient journeys can be clustered into different categories w.r.t their clinical and temporal characteristics. We evaluated the proposed approach on a real clinical data set pertaining to the unstable angina treatment process. The experimental results reveal that our method can identify and model latent treatment topics underlying in personalized inpatient journeys, and yield impressive clustering quality. PMID:26958200
The latent class multitrait-multimethod model.
Oberski, Daniel L; Hagenaars, Jacques A P; Saris, Willem E
2015-12-01
A latent class multitrait-multimethod (MTMM) model is proposed to estimate random and systematic measurement error in categorical survey questions while making fewer assumptions than have been made so far in such evaluations, allowing for possible extreme response behavior and other nonmonotone effects. The method is a combination of the MTMM research design of Campbell and Fiske (1959), the basic response model for survey questions of Saris and Andrews (1991), and the latent class factor model of Vermunt and Magidson (2004, pp. 227-230). The latent class MTMM model thus combines an existing design, model, and method to allow for the estimation of the degree to and manner in which survey questions are affected by systematic measurement error. Starting from a general form of the response function for a survey question, we present the MTMM experimental approach to identification of the response function's parameters. A "trait-method biplot" is introduced as a means of interpreting the estimates of systematic measurement error, whereas the quality of the questions can be evaluated by item information curves and the item information function. An experiment from the European Social Survey is analyzed and the results are discussed, yielding valuable insights into the functioning of a set of example questions on the role of women in society in 2 countries.
Bayesian Semiparametric Structural Equation Models with Latent Variables
ERIC Educational Resources Information Center
Yang, Mingan; Dunson, David B.
2010-01-01
Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…
Skills Diagnosis Using IRT-Based Continuous Latent Trait Models
ERIC Educational Resources Information Center
Stout, William
2007-01-01
This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model…
Tracking Human Pose Using Max-Margin Markov Models.
Zhao, Lin; Gao, Xinbo; Tao, Dacheng; Li, Xuelong
2015-12-01
We present a new method for tracking human pose by employing max-margin Markov models. Representing a human body by part-based models, such as pictorial structure, the problem of pose tracking can be modeled by a discrete Markov random field. Considering max-margin Markov networks provide an efficient way to deal with both structured data and strong generalization guarantees, it is thus natural to learn the model parameters using the max-margin technique. Since tracking human pose needs to couple limbs in adjacent frames, the model will introduce loops and will be intractable for learning and inference. Previous work has resorted to pose estimation methods, which discard temporal information by parsing frames individually. Alternatively, approximate inference strategies have been used, which can overfit to statistics of a particular data set. Thus, the performance and generalization of these methods are limited. In this paper, we approximate the full model by introducing an ensemble of two tree-structured sub-models, Markov networks for spatial parsing and Markov chains for temporal parsing. Both models can be trained jointly using the max-margin technique, and an iterative parsing process is proposed to achieve the ensemble inference. We apply our model on three challengeable data sets, which contains highly varied and articulated poses. Comprehensive experimental results demonstrate the superior performance of our method over the state-of-the-art approaches.
Numerical methods in Markov chain modeling
NASA Technical Reports Server (NTRS)
Philippe, Bernard; Saad, Youcef; Stewart, William J.
1989-01-01
Several methods for computing stationary probability distributions of Markov chains are described and compared. The main linear algebra problem consists of computing an eigenvector of a sparse, usually nonsymmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous singular linear system. Several methods based on combinations of Krylov subspace techniques are presented. The performance of these methods on some realistic problems are compared.
Hidden Markov Models for Fault Detection in Dynamic Systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic
1994-01-01
Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.
Nonparametric Bayes Stochastically Ordered Latent Class Models
Yang, Hongxia; O’Brien, Sean; Dunson, David B.
2012-01-01
Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity. PMID:22505787
Hyppolite, Judex; Trivedi, Pravin
2012-06-01
Cross-sectional latent class regression models, also known as switching regressions or hidden Markov models, cannot identify transitions between classes that may occur over time. This limitation can potentially be overcome when panel data are available. For such data, we develop a sequence of models that combine features of the static cross-sectional latent class (finite mixture) models with those of hidden Markov models. We model the probability of movement between categories in terms of a Markovian structure, which links the current state with a previous state, where state may refer to the category of an individual. This article presents a suite of mixture models of varying degree of complexity and flexibility for use in a panel count data setting, beginning with a baseline model which is a two-component mixture of Poisson distribution in which latent classes are fixed and permanent. Sequentially, we extend this framework (i) to allow the mixing proportions to be smoothly varying continuous functions of time-varying covariates, (ii) to add time dependence to the benchmark model by modeling the class-indicator variable as a first-order Markov chain and (iii) to extend item (i) by making it dynamic and introducing covariate dependence in the transition probabilities. We develop and implement estimation algorithms for these models and provide an empirical illustration using 1995-1999 panel data on the number of doctor visits derived from the German Socio-Economic Panel.
Building Higher-Order Markov Chain Models with EXCEL
ERIC Educational Resources Information Center
Ching, Wai-Ki; Fung, Eric S.; Ng, Michael K.
2004-01-01
Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a…
Quantum hidden Markov models based on transition operation matrices
NASA Astrophysics Data System (ADS)
Cholewa, Michał; Gawron, Piotr; Głomb, Przemysław; Kurzyk, Dariusz
2017-04-01
In this work, we extend the idea of quantum Markov chains (Gudder in J Math Phys 49(7):072105 [3]) in order to propose quantum hidden Markov models (QHMMs). For that, we use the notions of transition operation matrices and vector states, which are an extension of classical stochastic matrices and probability distributions. Our main result is the Mealy QHMM formulation and proofs of algorithms needed for application of this model: Forward for general case and Vitterbi for a restricted class of QHMMs. We show the relations of the proposed model to other quantum HMM propositions and present an example of application.
NASA Astrophysics Data System (ADS)
Zhu, Yanzheng; Zhang, Lixian; Sreeram, Victor; Shammakh, Wafa; Ahmad, Bashir
2016-10-01
In this paper, the resilient model approximation problem for a class of discrete-time Markov jump time-delay systems with input sector-bounded nonlinearities is investigated. A linearised reduced-order model is determined with mode changes subject to domination by a hierarchical Markov chain containing two different nonhomogeneous Markov chains. Hence, the reduced-order model obtained not only reflects the dependence of the original systems but also model external influence that is related to the mode changes of the original system. Sufficient conditions formulated in terms of bilinear matrix inequalities for the existence of such models are established, such that the resulting error system is stochastically stable and has a guaranteed l2-l∞ error performance. A linear matrix inequalities optimisation coupled with line search is exploited to solve for the corresponding reduced-order systems. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.
a Markov-Process Inspired CA Model of Highway Traffic
NASA Astrophysics Data System (ADS)
Wang, Fa; Li, Li; Hu, Jian-Ming; Ji, Yan; Ma, Rui; Jiang, Rui
To provide a more accurate description of the driving behaviors especially in car-following, namely a Markov-Gap cellular automata model is proposed in this paper. It views the variation of the gap between two consequent vehicles as a Markov process whose stationary distribution corresponds to the observed gap distribution. This new model provides a microscopic simulation explanation for the governing interaction forces (potentials) between the queuing vehicles, which cannot be directly measurable for traffic flow applications. The agreement between empirical observations and simulation results suggests the soundness of this new approach.
A semi-Markov model with memory for price changes
NASA Astrophysics Data System (ADS)
D'Amico, Guglielmo; Petroni, Filippo
2011-12-01
We study the high-frequency price dynamics of traded stocks by means of a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov model which depends also on a memory index. The index is introduced to take into account periods of high and low volatility in the market. First of all we derive the equations governing the process and then theoretical results are compared with empirical findings from real data. In particular we analyzed high-frequency data from the Italian stock market from 1 January 2007 until the end of December 2010.
Application of Hidden Markov Models in Biomolecular Simulations.
Shukla, Saurabh; Shamsi, Zahra; Moffett, Alexander S; Selvam, Balaji; Shukla, Diwakar
2017-01-01
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.
Bayesian restoration of ion channel records using hidden Markov models.
Rosales, R; Stark, J A; Fitzgerald, W J; Hladky, S B
2001-03-01
Hidden Markov models have been used to restore recorded signals of single ion channels buried in background noise. Parameter estimation and signal restoration are usually carried out through likelihood maximization by using variants of the Baum-Welch forward-backward procedures. This paper presents an alternative approach for dealing with this inferential task. The inferences are made by using a combination of the framework provided by Bayesian statistics and numerical methods based on Markov chain Monte Carlo stochastic simulation. The reliability of this approach is tested by using synthetic signals of known characteristics. The expectations of the model parameters estimated here are close to those calculated using the Baum-Welch algorithm, but the present methods also yield estimates of their errors. Comparisons of the results of the Bayesian Markov Chain Monte Carlo approach with those obtained by filtering and thresholding demonstrate clearly the superiority of the new methods.
A HIERARCHICAL HIDDEN MARKOV DETERIORATION MODEL FOR PAVEMENT STRUCTURE
NASA Astrophysics Data System (ADS)
Kobayashi, Kiyoshi; Kaito, Kiyoyuki; Eguchi, Toshiyuki; Ohi, Akira; Okizuka, Ryosuke
The deterioration process of pavement is a complex process including the deterioration of road surface and the decrease in load bearing capacity of the entire pavement. The decrease in load bearing capacity influences the deterioration rate of road surface. The soundness of road surface can be observed by a road surface condition investigation. On the other hand, the decrease in load bearing capacity can be partially observed through the FWD testing, etc. In this study, such a deterioration process of road surface is described as a mixed Markov process that depends on the load bearing capacity of pavement. Then, the complex deterioration process, which is composed of the deterioration of road surface and the decrease in load bearing capacity of pavement, is expressed as a hierarchical hidden Markov deterioration model. Through a case study of the application into the expressway, a hierarchical hidden Markov deterioration model is estimated, and its applicability and effectiveness are empirically discussed.
Multiensemble Markov models of molecular thermodynamics and kinetics.
Wu, Hao; Paul, Fabian; Wehmeyer, Christoph; Noé, Frank
2016-06-07
We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models-clustering of high-dimensional spaces and modeling of complex many-state systems-with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein-ligand binding model.
Robot reliability using fuzzy fault trees and Markov models
NASA Astrophysics Data System (ADS)
Leuschen, Martin; Walker, Ian D.; Cavallaro, Joseph R.
1996-10-01
Robot reliability has become an increasingly important issue in the last few years, in part due to the increased application of robots in hazardous and unstructured environments. However, much of this work leads to complex and nonintuitive analysis, which results in many techniques being impractical due to computational complexity or lack of appropriately complex models for the manipulator. In this paper, we consider the application of notions and techniques from fuzzy logic, fault trees, and Markov modeling to robot fault tolerance. Fuzzy logic lends itself to quantitative reliability calculations in robotics. The crisp failure rates which are usually used are not actually known, while fuzzy logic, due to its ability to work with the actual approximate (fuzzy) failure rates available during the design process, avoids making too many unwarranted assumptions. Fault trees are a standard reliability tool that can easily assimilate fuzzy logic. Markov modeling allows evaluation of multiple failure modes simultaneously, and is thus an appropriate method of modeling failures in redundant robotic systems. However, no method of applying fuzzy logic to Markov models was known to the authors. This opens up the possibility of new techniques for reliability using Markov modeling and fuzzy logic techniques, which are developed in this paper.
Multiensemble Markov models of molecular thermodynamics and kinetics
Wu, Hao; Paul, Fabian; Noé, Frank
2016-01-01
We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model. PMID:27226302
A Bayesian Semiparametric Latent Variable Model for Mixed Responses
ERIC Educational Resources Information Center
Fahrmeir, Ludwig; Raach, Alexander
2007-01-01
In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear…
Xu, Lizhen; Paterson, Andrew D; Xu, Wei
2017-04-01
Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero-inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya-Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
NASA Astrophysics Data System (ADS)
Sheehan, D. P.; Wright, J. H.
2006-10-01
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
Sheehan, D. P.; Wright, J. H.
2006-10-16
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
Markov reliability models for digital flight control systems
NASA Technical Reports Server (NTRS)
Mcgough, John; Reibman, Andrew; Trivedi, Kishor
1989-01-01
The reliability of digital flight control systems can often be accurately predicted using Markov chain models. The cost of numerical solution depends on a model's size and stiffness. Acyclic Markov models, a useful special case, are particularly amenable to efficient numerical solution. Even in the general case, instantaneous coverage approximation allows the reduction of some cyclic models to more readily solvable acyclic models. After considering the solution of single-phase models, the discussion is extended to phased-mission models. Phased-mission reliability models are classified based on the state restoration behavior that occurs between mission phases. As an economical approach for the solution of such models, the mean failure rate solution method is introduced. A numerical example is used to show the influence of fault-model parameters and interphase behavior on system unreliability.
Modeling Driver Behavior near Intersections in Hidden Markov Model.
Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei
2016-12-21
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers' behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents.
Modeling Driver Behavior near Intersections in Hidden Markov Model
Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei
2016-01-01
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838
Indexed semi-Markov process for wind speed modeling.
NASA Astrophysics Data System (ADS)
Petroni, F.; D'Amico, G.; Prattico, F.
2012-04-01
The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first
Operations and support cost modeling using Markov chains
NASA Technical Reports Server (NTRS)
Unal, Resit
1989-01-01
Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.
Latent Dirichlet allocation models for image classification.
Rasiwasia, Nikhil; Vasconcelos, Nuno
2013-11-01
Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and class-specific-simplex LDA (css-LDA), are proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification. To address this, ts-LDA models are introduced which replace the automated topic discovery of LDA with specified topics, identical to the classes of interest for classification. While this results in improvements in classification accuracy over existing LDA models, it compromises the ability of LDA to discover unanticipated structure of interest. This limitation is addressed by the introduction of css-LDA, an LDA model with class supervision at the level of image features. In css-LDA topics are discovered per class, i.e., a single set of topics shared across classes is replaced by multiple class-specific topic sets. The css-LDA model is shown to combine the labeling strength of topic-supervision with the flexibility of topic-discovery. Its effectiveness is demonstrated through an extensive experimental evaluation, involving multiple benchmark datasets, where it is shown to outperform existing LDA-based image classification approaches.
Cognitive Diagnosis Using Latent Trait Models.
ERIC Educational Resources Information Center
Samejima, Fumiko
This paper discusses the competency space approach to diagnosing misconceptions, skill, and knowledge acquisition. In some approaches that combine misconceptions, skill, and knowledge acquisition, the latent ability theta is used more or less as an insignificant element, but in the competency space approach, a multidimensional latent space is…
Locally Dependent Latent Trait Model and the Dutch Identity Revisited.
ERIC Educational Resources Information Center
Ip, Edward H.
2002-01-01
Proposes a class of locally dependent latent trait models for responses to psychological and educational tests. Focuses on models based on a family of conditional distributions, or kernel, that describes joint multiple item responses as a function of student latent trait, not assuming conditional independence. Also proposes an EM algorithm for…
Spurious Latent Classes in the Mixture Rasch Model
ERIC Educational Resources Information Center
Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.
2011-01-01
Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…
An Importance Sampling EM Algorithm for Latent Regression Models
ERIC Educational Resources Information Center
von Davier, Matthias; Sinharay, Sandip
2007-01-01
Reporting methods used in large-scale assessments such as the National Assessment of Educational Progress (NAEP) rely on latent regression models. To fit the latent regression model using the maximum likelihood estimation technique, multivariate integrals must be evaluated. In the computer program MGROUP used by the Educational Testing Service for…
Nonlinear and Quasi-Simplex Patterns in Latent Growth Models
ERIC Educational Resources Information Center
Bianconcini, Silvia
2012-01-01
In the SEM literature, simplex and latent growth models have always been considered competing approaches for the analysis of longitudinal data, even if they are strongly connected and both of specific importance. General dynamic models, which simultaneously estimate autoregressive structures and latent curves, have been recently proposed in the…
ERIC Educational Resources Information Center
Grimm, Kevin; Zhang, Zhiyong; Hamagami, Fumiaki; Mazzocco, Michele
2013-01-01
We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of "rates of change" and "acceleration" in latent growth curves--information available indirectly through traditional growth…
State reduction for semi-Markov reliability models
NASA Technical Reports Server (NTRS)
White, Allan L.; Palumbo, Daniel L.
1990-01-01
Trimming, a method of reducing the number of states in a semi-Markov reliability model, is described, and an error bound is derived. The error bound uses only three parameters from the semi-Markov model: (1) the maximum sum of rates for failure transitions leaving any state, (2) the maximum average holding time for a recovery-mode state, (3) and the operating time for the system. The error bound can be computed before any model generation takes places, which means the modeler can decide immediately whether the model can be trimmed. The trimming has a precise and simple description and thus can be easily included in a program that generates reliability models. The simplest version of the error bound for trimming is presented. More accurate versions can be obtained by requesting more information about the system being modeled.
An abstract specification language for Markov reliability models
NASA Technical Reports Server (NTRS)
Butler, R. W.
1985-01-01
Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.
An abstract language for specifying Markov reliability models
NASA Technical Reports Server (NTRS)
Butler, Ricky W.
1986-01-01
Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
Raffa, Jesse D; Dubin, Joel A
2015-09-01
Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.
Towards automatic Markov reliability modeling of computer architectures
NASA Technical Reports Server (NTRS)
Liceaga, C. A.; Siewiorek, D. P.
1986-01-01
The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation.
Higher-Order Item Response Models for Hierarchical Latent Traits
ERIC Educational Resources Information Center
Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming
2013-01-01
Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…
Developments in Latent Trait Theory: Models, Technical Issues, and Applications.
ERIC Educational Resources Information Center
And Others; Hambleton, Ronald K.
1978-01-01
Topics concerning latent trait theory are addressed: (1) dimensionality of latent space, local independence, and item characteristic curves; (2) models--equations, parameter estimation, testing assumptions, and goodness of fit, (3) applications test developments, item bias, tailored testing and equating; and (4) advantages over classical…
The Impact of Noninvariant Intercepts in Latent Means Models
ERIC Educational Resources Information Center
Whittaker, Tiffany A.
2013-01-01
Latent means methods such as multiple-indicator multiple-cause (MIMIC) and structured means modeling (SMM) allow researchers to determine whether or not a significant difference exists between groups' factor means. Strong invariance is typically recommended when interpreting latent mean differences. The extent of the impact of noninvariant…
Visual Recognition of American Sign Language Using Hidden Markov Models.
1995-02-01
3.3 Previous Use of Hidden Markov Models in Gesture Recognition 19 3.4 Use of HMM’s for Recognizing Sign Language 20 4 Tracking and Modeling...Instead, computer systems may be employed to annotate certain features of sequences. A human gesture recognition system adds another dimension to...focus for many gesture recognition systems. Tracking the natural hand in real time using camera imagery is dif- ficult, but successful systems have
Stochastic algorithms for Markov models estimation with intermittent missing data.
Deltour, I; Richardson, S; Le Hesran, J Y
1999-06-01
Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for estimating Markov chain models in the case of intermittent missing data in longitudinal studies, a stochastic EM algorithm and the Gibbs sampler, are described. The first can be viewed as a random perturbation of the EM algorithm and is appropriate when the M step is straightforward but the E step is computationally burdensome. It leads to a good approximation of the maximum likelihood estimates. The Gibbs sampler is used for a full Bayesian inference. The performances of the two algorithms are illustrated on two simulated data sets. A motivating example concerned with the modelling of the evolution of parasitemia by Plasmodium falciparum (malaria) in a cohort of 105 young children in Cameroon is described and briefly analyzed.
A Hidden Markov Approach to Modeling Interevent Earthquake Times
NASA Astrophysics Data System (ADS)
Chambers, D.; Ebel, J. E.; Kafka, A. L.; Baglivo, J.
2003-12-01
A hidden Markov process, in which the interevent time distribution is a mixture of exponential distributions with different rates, is explored as a model for seismicity that does not follow a Poisson process. In a general hidden Markov model, one assumes that a system can be in any of a finite number k of states and there is a random variable of interest whose distribution depends on the state in which the system resides. The system moves probabilistically among the states according to a Markov chain; that is, given the history of visited states up to the present, the conditional probability that the next state is a specified one depends only on the present state. Thus the transition probabilities are specified by a k by k stochastic matrix. Furthermore, it is assumed that the actual states are unobserved (hidden) and that only the values of the random variable are seen. From these values, one wishes to estimate the sequence of states, the transition probability matrix, and any parameters used in the state-specific distributions. The hidden Markov process was applied to a data set of 110 interevent times for earthquakes in New England from 1975 to 2000. Using the Baum-Welch method (Baum et al., Ann. Math. Statist. 41, 164-171), we estimate the transition probabilities, find the most likely sequence of states, and estimate the k means of the exponential distributions. Using k=2 states, we found the data were fit well by a mixture of two exponential distributions, with means of approximately 5 days and 95 days. The steady state model indicates that after approximately one fourth of the earthquakes, the waiting time until the next event had the first exponential distribution and three fourths of the time it had the second. Three and four state models were also fit to the data; the data were inconsistent with a three state model but were well fit by a four state model.
Grasp Recognition by Fuzzy Modeling and Hidden Markov Models
NASA Astrophysics Data System (ADS)
Palm, Rainer; Iliev, Boyko; Kadmiry, Bourhane
Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. This chapter describes three different methods for grasp recognition for a human hand. A human operator wearing a data glove instructs the robot to perform different grasps. For a number of human grasps the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using time as input parameter and joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are compared with each other. In the first method, the test grasp is compared with model grasps using the difference between the model outputs. The second method deals with qualitative fuzzy models which used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning.
EMMA: A Software Package for Markov Model Building and Analysis.
Senne, Martin; Trendelkamp-Schroer, Benjamin; Mey, Antonia S J S; Schütte, Christof; Noé, Frank
2012-07-10
The study of folding and conformational changes of macromolecules by molecular dynamics simulations often requires the generation of large amounts of simulation data that are difficult to analyze. Markov (state) models (MSMs) address this challenge by providing a systematic way to decompose the state space of the molecular system into substates and to estimate a transition matrix containing the transition probabilities between these substates. This transition matrix can be analyzed to reveal the metastable, i.e., long-living, states of the system, its slowest relaxation time scales, and transition pathways and rates, e.g., from unfolded to folded, or from dissociated to bound states. Markov models can also be used to calculate spectroscopic data and thus serve as a way to reconcile experimental and simulation data. To reduce the technical burden of constructing, validating, and analyzing such MSMs, we provide the software framework EMMA that is freely available at https://simtk.org/home/emma .
Behavior Detection using Confidence Intervals of Hidden Markov Models
Griffin, Christopher H
2009-01-01
Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov Models (HMMs) are a class of Markov model commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criteria for determining whether or not a given HMM adequately matches the data stream. In this work, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver Operating Characteristic curves are used to find the optimal threshold for either accepting or rejecting a HMM description. We present one example using a family of HMM's to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.
Of bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research
Link, W.A.; Cam, E.; Nichols, J.D.; Cooch, E.G.
2002-01-01
Markov chain Monte Carlo (MCMC) is a statistical innovation that allows researchers to fit far more complex models to data than is feasible using conventional methods. Despite its widespread use in a variety of scientific fields, MCMC appears to be underutilized in wildlife applications. This may be due to a misconception that MCMC requires the adoption of a subjective Bayesian analysis, or perhaps simply to its lack of familiarity among wildlife researchers. We introduce the basic ideas of MCMC and software BUGS (Bayesian inference using Gibbs sampling), stressing that a simple and satisfactory intuition for MCMC does not require extraordinary mathematical sophistication. We illustrate the use of MCMC with an analysis of the association between latent factors governing individual heterogeneity in breeding and survival rates of kittiwakes (Rissa tridactyla). We conclude with a discussion of the importance of individual heterogeneity for understanding population dynamics and designing management plans.
Probabilistic Independence Networks for Hidden Markov Probability Models
NASA Technical Reports Server (NTRS)
Smyth, Padhraic; Heckerman, Cavid; Jordan, Michael I
1996-01-01
In this paper we explore hidden Markov models(HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general enference algorithms for arbitrary PINs.
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
ERIC Educational Resources Information Center
Henson, Robert A.; Templin, Jonathan L.; Willse, John T.
2009-01-01
This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…
Tensor Decompositions for Learning Latent Variable Models
2012-12-08
of a tensor, 2011. arXiv:1004.4953. [CSC+12] S. B. Cohen, K. Stratos, M. Collins, D. P. Foster, and L. Ungar . Spectral learning of latent-variable...12] P. S. Dhillon, J. Rodu, M. Collins, D. P. Foster, and L. H. Ungar . Spectral dependency parsing with latent variables. In EMNLP-CoNLL, 2012. [DS07...Foster, J. Rodu, and L. H. Ungar . Spectral dimensionality reduction for HMMs, 2012. arXiv:1203.6130. [GvL96] G. H. Golub and C. F. van Loan. Matrix
Markov chain decision model for urinary incontinence procedures.
Kumar, Sameer; Ghildayal, Nidhi; Ghildayal, Neha
2017-03-13
Purpose Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective. Design/methodology/approach This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis. Findings Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure. Originality/value This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.
Bayesian Estimation in the One-Parameter Latent Trait Model.
1980-03-01
3 MASSACHUSETTS LNIV AMHERST LAB OF PSYCHOMETRIC AND -- ETC F/G 12/1 BAYESIAN ESTIMATION IN THE ONE-PARA1ETER LATENT TRAIT MODEL. (U) MAR 80 H...TEST CHART VVNN lfl’ ,. [’ COD BAYESIAN ESTIMATION IN THE ONE-PARAMETER LATENT TRAIT MODEL 0 wtHAR IHARAN SWA I NATHAN AND JANICE A. GIFFORD Research...block numbef) latent trait theory Bayesain estimation 20. ABSTRACT (Continue on reveso aide If neceaar and identlfy by Nock mambe) ,-When several
Image segmentation using hidden Markov Gauss mixture models.
Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M
2007-07-01
Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.
Markov Boundary Discovery with Ridge Regularized Linear Models
Visweswaran, Shyam
2016-01-01
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many investigators are reluctant to draw causal interpretations of the selected variables due to the incomplete knowledge of the capabilities of RRLMs in causal inference. Under reasonable assumptions, we show that a modified form of RRLMs can get “very close” to identifying a subset of the Markov boundary by providing a worst-case bound on the space of possible solutions. The results hold for any convex loss, even when the underlying functional relationship is nonlinear, and the solution is not unique. Our approach combines ideas in Markov boundary and sufficient dimension reduction theory. Experimental results show that the modified RRLMs are competitive against state-of-the-art algorithms in discovering part of the Markov boundary from gene expression data. PMID:27170915
Distribution system reliability assessment using hierarchical Markov modeling
Brown, R.E.; Gupta, S.; Christie, R.D.; Venkata, S.S.; Fletcher, R.
1996-10-01
Distribution system reliability assessment is concerned with power availability and power quality at each customer`s service entrance. This paper presents a new method, termed Hierarchical Markov Modeling (HMM), which can perform predictive distribution system reliability assessment. HMM is unique in that it decomposes the reliability model based on system topology, integrated protection systems, and individual protection devices. This structure, which easily accommodates the effects of backup protection, fault isolation, and load restoration, is compared to simpler reliability models. HMM is then used to assess the reliability of an existing utility distribution system and to explore the reliability impact of several design improvement options.
Probabilistic pairwise Markov models: application to prostate cancer detection
NASA Astrophysics Data System (ADS)
Monaco, James; Tomaszewski, John E.; Feldman, Michael D.; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant
2009-02-01
Markov Random Fields (MRFs) provide a tractable means for incorporating contextual information into a Bayesian framework. This contextual information is modeled using multiple local conditional probability density functions (LCPDFs) which the MRF framework implicitly combines into a single joint probability density function (JPDF) that describes the entire system. However, only LCPDFs of certain functional forms are consistent, meaning they reconstitute a valid JPDF. These forms are specified by the Gibbs-Markov equivalence theorem which indicates that the JPDF, and hence the LCPDFs, should be representable as a product of potential functions (i.e. Gibbs distributions). Unfortunately, potential functions are mathematical abstractions that lack intuition; and consequently, constructing LCPDFs through their selection becomes an ad hoc procedure, usually resulting in generic and/or heuristic models. In this paper we demonstrate that under certain conditions the LCDPFs can be formulated in terms of quantities that are both meaningful and descriptive: probability distributions. Using probability distributions instead of potential functions enables us to construct consistent LCPDFs whose modeling capabilities are both more intuitive and expansive than typical MRF models. As an example, we compare the efficacy of our so-called probabilistic pairwise Markov models (PPMMs) to the prevalent Potts model by incorporating both into a novel computer aided diagnosis (CAD) system for detecting prostate cancer in whole-mount histological sections. Using the Potts model the CAD system is able to detection cancerous glands with a specificity of 0.82 and sensitivity of 0.71; its area under the receiver operator characteristic (AUC) curve is 0.83. If instead the PPMM model is employed the sensitivity (specificity is held fixed) and AUC increase to 0.77 and 0.87.
Estimation in a semi-Markov transformation model
Dabrowska, Dorota M.
2012-01-01
Multi-state models provide a common tool for analysis of longitudinal failure time data. In biomedical applications, models of this kind are often used to describe evolution of a disease and assume that patient may move among a finite number of states representing different phases in the disease progression. Several authors developed extensions of the proportional hazard model for analysis of multi-state models in the presence of covariates. In this paper, we consider a general class of censored semi-Markov and modulated renewal processes and propose the use of transformation models for their analysis. Special cases include modulated renewal processes with interarrival times specified using transformation models, and semi-Markov processes with with one-step transition probabilities defined using copula-transformation models. We discuss estimation of finite and infinite dimensional parameters of the model, and develop an extension of the Gaussian multiplier method for setting confidence bands for transition probabilities. A transplant outcome data set from the Center for International Blood and Marrow Transplant Research is used for illustrative purposes. PMID:22740583
Hidden Markov models for evolution and comparative genomics analysis.
Bykova, Nadezda A; Favorov, Alexander V; Mironov, Andrey A
2013-01-01
The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.
ERIC Educational Resources Information Center
Sun, Shaojing; Konold, Timothy R.; Fan, Xitao
2011-01-01
Interest in testing interaction terms within the latent variable modeling framework has been on the rise in recent years. However, little is known about the influence of nonnormality and model misspecification on such models that involve latent variable interactions. The authors used Mattson's data generation method to control for latent variable…
Estimation and Model Selection for Finite Mixtures of Latent Interaction Models
ERIC Educational Resources Information Center
Hsu, Jui-Chen
2011-01-01
Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…
Protein structure comparison using the markov transition model of evolution.
Kawabata, T; Nishikawa, K
2000-10-01
A number of automatic protein structure comparison methods have been proposed; however, their similarity score functions are often decided by the researchers' intuition and trial-and-error, and not by theoretical background. We propose a novel theory to evaluate protein structure similarity, which is based on the Markov transition model of evolution. Our similarity score between structures i and j is defined as log P(j --> i)/P(i), where P(j --> i) is the probability that structure j changes to structure i during the evolutionary process, and P(i) is the probability that structure i appears by chance. This is a reasonable definition of structure similarity, especially for finding evolutionarily related (homologous) similarity. The probability P(j --> i) is estimated by the Markov transition model, which is similar to the Dayhoff's substitution model between amino acids. To estimate the parameters of the model, homologous protein structure pairs are collected using sequence similarity, and the numbers of structure transitions within the pairs are counted. Next these numbers are transformed to a transition probability matrix of the Markov transition. Transition probabilities for longer time are obtained by multiplying the probability matrix by itself several times. In this study, we generated three types of structure similarity scores: an environment score, a residue-residue distance score, and a secondary structure elements (SSE) score. Using these scores, we developed the structure comparison program, Matras (MArkovian TRAnsition of protein Structure). It employs a hierarchical alignment algorithm, in which a rough alignment is first obtained by SSEs, and then is improved with more detailed functions. We attempted an all-versus-all comparison of the SCOP database, and evaluated its ability to recognize a superfamily relationship, which was manually assigned to be homologous in the SCOP database. A comparison with the FSSP database shows that our program can
Markov Modeling with Soft Aggregation for Safety and Decision Analysis
COOPER,J. ARLIN
1999-09-01
The methodology in this report improves on some of the limitations of many conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems and for expressing imprecise individual metrics as possibilistic or fuzzy numbers. A ''Markov-like'' model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of Markov modeling is to help convey the top-down system perspective. One of the constituent methodologies allows metrics to be weighted according to significance of the attribute and aggregated nonlinearly as to contribution. This aggregation is performed using exponential combination of the metrics, since the accumulating effect of such factors responds less and less to additional factors. This is termed ''soft'' mathematical aggregation. Dependence among the contributing factors is accounted for by incorporating subjective metrics on ''overlap'' of the factors as well as by correspondingly reducing the overall contribution of these combinations to the overall aggregation. Decisions corresponding to the meaningfulness of the results are facilitated in several ways. First, the results are compared to a soft threshold provided by a sigmoid function. Second, information is provided on input ''Importance'' and ''Sensitivity,'' in order to know where to place emphasis on considering new controls that may be necessary. Third, trends in inputs and outputs are tracked in order to obtain significant information% including cyclic information for the decision process. A practical example from the air transportation industry is used to demonstrate application of the methodology. Illustrations are given for developing a structure (along with recommended inputs and weights) for air transportation oversight at three different levels, for developing and using cycle information, for developing Importance and
Upscaling of Mixing Processes using a Spatial Markov Model
NASA Astrophysics Data System (ADS)
Bolster, Diogo; Sund, Nicole; Porta, Giovanni
2016-11-01
The Spatial Markov model is a model that has been used to successfully upscale transport behavior across a broad range of spatially heterogeneous flows, with most examples to date coming from applications relating to porous media. In its most common current forms the model predicts spatially averaged concentrations. However, many processes, including for example chemical reactions, require an adequate understanding of mixing below the averaging scale, which means that knowledge of subscale fluctuations, or closures that adequately describe them, are needed. Here we present a framework, consistent with the Spatial Markov modeling framework, that enables us to do this. We apply and present it as applied to a simple example, a spatially periodic flow at low Reynolds number. We demonstrate that our upscaled model can successfully predict mixing by comparing results from direct numerical simulations to predictions with our upscaled model. To this end we focus on predicting two common metrics of mixing: the dilution index and the scalar dissipation. For both metrics our upscaled predictions very closely match observed values from the DNS. This material is based upon work supported by NSF Grants EAR-1351625 and EAR-1417264.
A cautionary note on testing latent variable models
Ropovik, Ivan
2015-01-01
The article tackles the practice of testing latent variable models. The analysis covered recently published studies from 11 psychology journals varying in orientation and impact. Seventy-five studies that matched the criterion of applying some of the latent modeling techniques were reviewed. Results indicate the presence of a general tendency to ignore the model test (χ2) followed by the acceptance of approximate fit hypothesis without detailed model examination yielding relevant empirical evidence. Due to reduced sensitivity of such a procedure to confront theory with data, there is an almost invariable tendency to accept the theoretical model. This absence of model test consequences, manifested in frequently unsubstantiated neglect of evidence speaking against the model, thus implies the perilous question of whether such empirical testing of latent structures (the way it is widely applied) makes sense at all. PMID:26594192
Knegtering, B; Brombacher, A C
2000-01-01
This paper presents a method that will drastically reduce the calculation effort required to obtain quantitative safety and reliability assessments to determine safety integrity levels for applications in the process industry. The method described combines all benefits of Markov modeling with the practical benefits of reliability block diagrams.
Hidden Markov model using Dirichlet process for de-identification.
Chen, Tao; Cullen, Richard M; Godwin, Marshall
2015-12-01
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance.
A Flexible Latent Trait Model for Response Times in Tests
ERIC Educational Resources Information Center
Ranger, Jochen; Kuhn, Jorg-Tobias
2012-01-01
Latent trait models for response times in tests have become popular recently. One challenge for response time modeling is the fact that the distribution of response times can differ considerably even in similar tests. In order to reduce the need for tailor-made models, a model is proposed that unifies two popular approaches to response time…
ERIC Educational Resources Information Center
Kelava, Augustin; Nagengast, Benjamin
2012-01-01
Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…
A Markov chain model for reliability growth and decay
NASA Technical Reports Server (NTRS)
Siegrist, K.
1982-01-01
A mathematical model is developed to describe a complex system undergoing a sequence of trials in which there is interaction between the internal states of the system and the outcomes of the trials. For example, the model might describe a system undergoing testing that is redesigned after each failure. The basic assumptions for the model are that the state of the system after a trial depends probabilistically only on the state before the trial and on the outcome of the trial and that the outcome of a trial depends probabilistically only on the state of the system before the trial. It is shown that under these basic assumptions, the successive states form a Markov chain and the successive states and outcomes jointly form a Markov chain. General results are obtained for the transition probabilities, steady-state distributions, etc. A special case studied in detail describes a system that has two possible state ('repaired' and 'unrepaired') undergoing trials that have three possible outcomes ('inherent failure', 'assignable-cause' 'failure' and 'success'). For this model, the reliability function is computed explicitly and an optimal repair policy is obtained.
Entropy, complexity, and Markov diagrams for random walk cancer models.
Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-19
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
Entropy, complexity, and Markov diagrams for random walk cancer models
NASA Astrophysics Data System (ADS)
Newton, Paul K.; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-01
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
Inferring phenomenological models of Markov processes from data
NASA Astrophysics Data System (ADS)
Rivera, Catalina; Nemenman, Ilya
Microscopically accurate modeling of stochastic dynamics of biochemical networks is hard due to the extremely high dimensionality of the state space of such networks. Here we propose an algorithm for inference of phenomenological, coarse-grained models of Markov processes describing the network dynamics directly from data, without the intermediate step of microscopically accurate modeling. The approach relies on the linear nature of the Chemical Master Equation and uses Bayesian Model Selection for identification of parsimonious models that fit the data. When applied to synthetic data from the Kinetic Proofreading process (KPR), a common mechanism used by cells for increasing specificity of molecular assembly, the algorithm successfully uncovers the known coarse-grained description of the process. This phenomenological description has been notice previously, but this time it is derived in an automated manner by the algorithm. James S. McDonnell Foundation Grant No. 220020321.
Reduction Of Sizes Of Semi-Markov Reliability Models
NASA Technical Reports Server (NTRS)
White, Allan L.; Palumbo, Dan L.
1995-01-01
Trimming technique reduces computational effort by order of magnitude while introducing negligible error. Error bound depends on only three parameters from semi-Markov model: maximum sum of rates for failure transitions leaving any state, maximum average holding time for recovery-mode state, and operating time for system. Error bound computed before any model generated, enabling modeler to decide immediately whether or not model can be trimmed. Trimming procedure specified by precise and easy description, making it easy to include trimming procedure in program generating mathematical models for use in assessing reliability. Typical application of technique in design of digital control systems required to be extremely reliable. In addition to aerospace applications, fault-tolerant design has growing importance in wide range of industrial applications.
Mediation Analysis in a Latent Growth Curve Modeling Framework
ERIC Educational Resources Information Center
von Soest, Tilmann; Hagtvet, Knut A.
2011-01-01
This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and…
Latent Growth Curves within Developmental Structural Equation Models.
ERIC Educational Resources Information Center
McArdle, J. J.; Epstein, David
1987-01-01
Uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. The model describes a latent growth curve model that permits the estimation of parameters representing individual and group dynamics. (Author/RH)
Specifying Piecewise Latent Trajectory Models for Longitudinal Data
ERIC Educational Resources Information Center
Flora, David B.
2008-01-01
Piecewise latent trajectory models for longitudinal data are useful in a wide variety of situations, such as when a simple model is needed to describe nonlinear change, or when the purpose of the analysis is to evaluate hypotheses about change occurring during a particular period of time within a model for a longer overall time frame, such as…
A Hierarchical Latent Stochastic Differential Equation Model for Affective Dynamics
ERIC Educational Resources Information Center
Oravecz, Zita; Tuerlinckx, Francis; Vandekerckhove, Joachim
2011-01-01
In this article a continuous-time stochastic model (the Ornstein-Uhlenbeck process) is presented to model the perpetually altering states of the core affect, which is a 2-dimensional concept underlying all our affective experiences. The process model that we propose can account for the temporal changes in core affect on the latent level. The key…
Markov models of molecular kinetics: generation and validation.
Prinz, Jan-Hendrik; Wu, Hao; Sarich, Marco; Keller, Bettina; Senne, Martin; Held, Martin; Chodera, John D; Schütte, Christof; Noé, Frank
2011-05-07
Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data.
A hidden Markov model for space-time precipitation
Zucchini, W. ); Guttorp, P. )
1991-08-01
Stochastic models for precipitation events in space and time over mesoscale spatial areas have important applications in hydrology, both as input to runoff models and as parts of general circulation models (GCMs) of global climate. A family of multivariate models for the occurrence/nonoccurrence of precipitation at N sites is constructed by assuming a different probability of events at the sites for each of a number of unobservable climate states. The climate process is assumed to follow a Markov chain. Simple formulae for first- and second-order parameter functions are derived, and used to find starting values for a numerical maximization of the likelihood. The method is illustrated by applying it to data for one site in Washington and to data for a network in the Great plains.
Markov modulated Poisson process models incorporating covariates for rainfall intensity.
Thayakaran, R; Ramesh, N I
2013-01-01
Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.
Α Markov model for longitudinal studies with incomplete dichotomous outcomes.
Efthimiou, Orestis; Welton, Nicky; Samara, Myrto; Leucht, Stefan; Salanti, Georgia
2017-03-01
Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time-dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data.
Stylistic gait synthesis based on hidden Markov models
NASA Astrophysics Data System (ADS)
Tilmanne, Joëlle; Moinet, Alexis; Dutoit, Thierry
2012-12-01
In this work we present an expressive gait synthesis system based on hidden Markov models (HMMs), following and modifying a procedure originally developed for speaking style adaptation, in speech synthesis. A large database of neutral motion capture walk sequences was used to train an HMM of average walk. The model was then used for automatic adaptation to a particular style of walk using only a small amount of training data from the target style. The open source toolkit that we adapted for motion modeling also enabled us to take into account the dynamics of the data and to model accurately the duration of each HMM state. We also address the assessment issue and propose a procedure for qualitative user evaluation of the synthesized sequences. Our tests show that the style of these sequences can easily be recognized and look natural to the evaluators.
Α Markov model for longitudinal studies with incomplete dichotomous outcomes
Welton, Nicky; Samara, Myrto; Leucht, Stefan; Salanti, Georgia
2016-01-01
Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time‐dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data. PMID:27917593
Research on Multi-Stage Inventory Model by Markov Decision Process
NASA Astrophysics Data System (ADS)
Rong, Ke
This paper researched multi-stage inventory system and established limited inventory Markov model, on the other hand it induced DP algorithm of limited inventory Markov model. The results proved that the reorder point of multi-stage inventory system can guarantee demand, and also allows the storage costs to a minimum level in accordance with the above model.
Sequence alignments and pair hidden Markov models using evolutionary history.
Knudsen, Bjarne; Miyamoto, Michael M
2003-10-17
This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.
Implementation of a Markov model for phylogenetic trees.
Bohl, Erich; Lancaster, Peter
2006-04-07
A recently developed mathematical model for the analysis of phylogenetic trees is applied to comparative data for 48 species. The model represents a return to fundamentals and makes no hypothesis with respect to the reversibility of the process. The species have been analysed in all subsets of three, and a measure of reliability of the results is provided. The numerical results of the computations on 17,296 triples of species are made available on the Internet. These results are discussed and the development of reliable tree structures for several species is illustrated. It is shown that, indeed, the Markov model is capable of considerably more interesting predictions than has been recognized to date.
Using Markov state models to study self-assembly
Perkett, Matthew R.; Hagan, Michael F.
2014-01-01
Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude. PMID:24907984
Dimensional Reduction for the General Markov Model on Phylogenetic Trees.
Sumner, Jeremy G
2017-03-01
We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.
Understanding eye movements in face recognition using hidden Markov models.
Chuk, Tim; Chan, Antoni B; Hsiao, Janet H
2014-09-16
We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone.
ERIC Educational Resources Information Center
Schmitt, J. Eric; Mehta, Paras D.; Aggen, Steven H.; Kubarych, Thomas S.; Neale, Michael C.
2006-01-01
Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation,…
Probability distributions of molecular observables computed from Markov models.
Noé, Frank
2008-06-28
Molecular dynamics (MD) simulations can be used to estimate transition rates between conformational substates of the simulated molecule. Such an estimation is associated with statistical uncertainty, which depends on the number of observed transitions. In turn, it induces uncertainties in any property computed from the simulation, such as free energy differences or the time scales involved in the system's kinetics. Assessing these uncertainties is essential for testing the reliability of a given observation and also to plan further simulations in such a way that the most serious uncertainties will be reduced with minimal effort. Here, a rigorous statistical method is proposed to approximate the complete statistical distribution of any observable of an MD simulation provided that one can identify conformational substates such that the transition process between them may be modeled with a memoryless jump process, i.e., Markov or Master equation dynamics. The method is based on sampling the statistical distribution of Markov transition matrices that is induced by the observed transition events. It allows physically meaningful constraints to be included, such as sampling only matrices that fulfill detailed balance, or matrices that produce a predefined equilibrium distribution of states. The method is illustrated on mus MD simulations of a hexapeptide for which the distributions and uncertainties of the free energy differences between conformations, the transition matrix elements, and the transition matrix eigenvalues are estimated. It is found that both constraints, detailed balance and predefined equilibrium distribution, can significantly reduce the uncertainty of some observables.
A coupled hidden Markov model for disease interactions
Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike
2013-01-01
To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. PMID:24223436
A coupled hidden Markov model for disease interactions.
Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike
2013-08-01
To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites.
Class Extraction and Classification Accuracy in Latent Class Models
ERIC Educational Resources Information Center
Wu, Qiong
2009-01-01
Despite the increasing popularity of latent class models (LCM) in educational research, methodological studies have not yet accumulated much information on the appropriate application of this modeling technique, especially with regard to requirement on sample size and number of indicators. This dissertation study represented an initial attempt to…
Higher-Order Latent Trait Models for Cognitive Diagnosis
ERIC Educational Resources Information Center
de la Torre, Jimmy; Douglas, Jeffrey A.
2004-01-01
Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative…
An extended crystal plasticity model for latent hardening in polycrystals
NASA Astrophysics Data System (ADS)
Bargmann, Swantje; Svendsen, Bob; Ekh, Magnus
2011-12-01
In this contribution, a computational approach to modeling size-dependent self- and latent hardening in polycrystals is presented. Latent hardening is the hardening of inactive slip systems due to active slip systems. We focus attention on the investigation of glide system interaction, latent hardening and excess dislocation development. In particular, latent hardening results in a transition to patchy slip as a first indication and expression of the development of dislocation microstructures. To this end, following Nye (Acta Metall 1:153-162, 1953), Kondo (in Proceedings of the second Japan national congress for applied mechanics. Science Council of Japan, Tokyo, pp. 41-47, 1953), and many others, local deformation incompatibility in the material is adopted as a measure of the density of geometrically necessary dislocations. Their development results in additional energy being stored in the material, leading to additional kinematic-like hardening effects. A large-deformation model for latent hardening is introduced. This approach is based on direct exploitation of the dissipation principle to derive all field relations and (sufficient) forms of the constitutive relations as based on the free energy density and dissipation potential. The numerical implementation is done via a dual-mixed finite element method. A numerical example for polycrystals is presented.
A combined feature latent semantic model for scene classification
NASA Astrophysics Data System (ADS)
Jiang, Yue; Wang, Runsheng
2009-10-01
Due to vast growth of image databases, scene image classification methods have become increasingly important in computer vision areas. We propose a new scene image classification framework based on combined feature and a latent semantic model which is based on the Latent Dirichlet Allocation (LDA) in the statistical text literature. Here the model is applied to visual words representation for images. We use Gibbs sampling for parameter estimation and use several different numbers of topics at the same time to obtain the latent topic representation of images. We densely extract multi-scale patches from images and get the combined feature on these patches. Our method is unsupervised. It can also well represent semantic characteristic of images. We demonstrate the effectiveness of our approach by comparing it to those used in previous work in this area. Experiments were conducted on three often used image databases, and our method got better results than the others.
On Markov modelling of near-wall turbulent shear flow
NASA Astrophysics Data System (ADS)
Reynolds, A. M.
1999-11-01
The role of Reynolds number in determining particle trajectories in near-wall turbulent shear flow is investigated in numerical simulations using a second-order Lagrangian stochastic (LS) model (Reynolds, A.M. 1999: A second-order Lagrangian stochastic model for particle trajectories in inhomogeneous turbulence. Quart. J. Roy. Meteorol. Soc. (In Press)). In such models, it is the acceleration, velocity and position of a particle rather than just its velocity and position which are assumed to evolve jointly as a continuous Markov process. It is found that Reynolds number effects are significant in determining simulated particle trajectories in the viscous sub-layer and the buffer zone. These effects are due almost entirely to the change in the Lagrangian integral timescale and are shown to be well represented in a first-order LS model by Sawford's correction footnote Sawford, B.L. 1991: Reynolds number effects in Lagrangian stochastic models of turbulent dispersion. Phys Fluids, 3, 1577-1586). This is found to remain true even when the Taylor-Reynolds number R_λ ~ O(0.1). This is somewhat surprising because the assumption of a Markovian evolution for velocity and position is strictly applicable only in the large Reynolds number limit because then the Lagrangian acceleration autocorrelation function approaches a delta function at the origin, corresponding to an uncorrelated component in the acceleration, and hence a Markov process footnote Borgas, M.S. and Sawford, B.L. 1991: The small-scale structure of acceleration correlations and its role in the statistical theory of turbulent dispersion. J. Fluid Mech. 288, 295-320.
Hidden Markov models for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic J. (Inventor)
1995-01-01
The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.
Hidden Markov models for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic J. (Inventor)
1993-01-01
The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.
Data Stream Prediction Using Incremental Hidden Markov Models
NASA Astrophysics Data System (ADS)
Wakabayashi, Kei; Miura, Takao
In this paper, we propose a new technique for time-series prediction. Here we assume that time-series data occur depending on event which is unobserved directly, and we estimate future data as output from the most likely event which will happen at the time. In this investigation we model time-series based on event sequence by using Hidden Markov Model(HMM), and extract time-series patterns as trained HMM parameters. However, we can’t apply HMM approach to data stream prediction in a straightforward manner. This is because Baum-Welch algorithm, which is traditional unsupervised HMM training algorithm, requires many stored historical data and scan it many times. Here we apply incremental Baum-Welch algorithm which is an on-line HMM training method, and estimate HMM parameters dynamically to adapt new time-series patterns. And we show some experimental results to see the validity of our method.
Markov Model of Accident Progression at Fukushima Daiichi
Cuadra A.; Bari R.; Cheng, L-Y; Ginsberg, T.; Lehner, J.; Martinez-Guridi, G.; Mubayi, V.; Pratt, T.; Yue, M.
2012-11-11
On March 11, 2011, a magnitude 9.0 earthquake followed by a tsunami caused loss of offsite power and disabled the emergency diesel generators, leading to a prolonged station blackout at the Fukushima Daiichi site. After successful reactor trip for all operating reactors, the inability to remove decay heat over an extended period led to boil-off of the water inventory and fuel uncovery in Units 1-3. A significant amount of metal-water reaction occurred, as evidenced by the quantities of hydrogen generated that led to hydrogen explosions in the auxiliary buildings of the Units 1 & 3, and in the de-fuelled Unit 4. Although it was assumed that extensive fuel damage, including fuel melting, slumping, and relocation was likely to have occurred in the core of the affected reactors, the status of the fuel, vessel, and drywell was uncertain. To understand the possible evolution of the accident conditions at Fukushima Daiichi, a Markov model of the likely state of one of the reactors was constructed and executed under different assumptions regarding system performance and reliability. The Markov approach was selected for several reasons: It is a probabilistic model that provides flexibility in scenario construction and incorporates time dependence of different model states. It also readily allows for sensitivity and uncertainty analyses of different failure and repair rates of cooling systems. While the analysis was motivated by a need to gain insight on the course of events for the damaged units at Fukushima Daiichi, the work reported here provides a more general analytical basis for studying and evaluating severe accident evolution over extended periods of time. This work was performed at the request of the U.S. Department of Energy to explore 'what-if' scenarios in the immediate aftermath of the accidents.
Estimating Causal Effects with Ancestral Graph Markov Models
Malinsky, Daniel; Spirtes, Peter
2017-01-01
We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the “IDA” procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. PMID:28217244
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Nonparametric model validations for hidden Markov models with applications in financial econometrics
Zhao, Zhibiao
2011-01-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601
Validity Measures in the Context of Latent Trait Models.
ERIC Educational Resources Information Center
Samejima, Fumiko
Test validity is a concept that has often been ignored in the context of latent trait models and in modern test theory, particularly as it relates to computerized adaptive testing. Some considerations about the validity of a test and of a single item are proposed. This paper focuses on measures that are population-free and that will provide local…
A Latent Trait Model for Differential Strategies in Cognitive Processes.
ERIC Educational Resources Information Center
Samejima, Fumiko
Some cognitive psychologists, who have tried to approach psychometric theories, say that the psychometric approach does not provide them with theories and methods with which they can deal with differential strategies. In this paper, a general latent trait model for differential strategies in cognitive processes is proposed which includes three…
Interrater Agreement Evaluation: A Latent Variable Modeling Approach
ERIC Educational Resources Information Center
Raykov, Tenko; Dimitrov, Dimiter M.; von Eye, Alexander; Marcoulides, George A.
2013-01-01
A latent variable modeling method for evaluation of interrater agreement is outlined. The procedure is useful for point and interval estimation of the degree of agreement among a given set of judges evaluating a group of targets. In addition, the approach allows one to test for identity in underlying thresholds across raters as well as to identify…
Meta-Analysis of Scale Reliability Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2013-01-01
A latent variable modeling approach is outlined that can be used for meta-analysis of reliability coefficients of multicomponent measuring instruments. Important limitations of efforts to combine composite reliability findings across multiple studies are initially pointed out. A reliability synthesis procedure is discussed that is based on…
Linking Academic Entitlement and Student Incivility Using Latent Means Modeling
ERIC Educational Resources Information Center
Kopp, Jason P.; Finney, Sara J.
2013-01-01
Academic entitlement has been theoretically linked with uncivil student behavior; however, this relationship has not been tested. To address this gap in the literature, the authors used latent means modeling to estimate the relationship between the Academic Entitlement Questionnaire and uncivil student behavior. The authors gathered scores on the…
Sensitive protein comparisons with profiles and hidden Markov models.
Hofmann, K
2000-05-01
Sequence database searches have become an important tool for the life sciences in general and for gene discovery-driven biotechnology in particular. Both the functional assignment of newly found proteins and the mining of genome databases for functional candidates are equally important tasks typically addressed by database searches. Sensitivity and reliability of the search methods are of crucial importance. The overall performance of sequence alignments and database searches can be enhanced considerably, when profiles or hidden Markov models (HMMs) derived from protein families are used as query objects instead of single sequences. This review discusses the concept of profiles, generalised profiles and profile-HMMs, the methods how they are constructed and the scope of possible applications in gene discovery and gene functional assignment.
Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models.
Pinamonti, Giovanni; Zhao, Jianbo; Condon, David E; Paul, Fabian; Noè, Frank; Turner, Douglas H; Bussi, Giovanni
2017-02-14
Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely, atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. After properly removing structures that are artificially stabilized by known inaccuracies of the current RNA AMBER force field, the kinetic properties predicted are consistent with the time scales of previously reported relaxation experiments.
pHMM-tree: phylogeny of profile hidden Markov models.
Huo, Luyang; Zhang, Han; Huo, Xueting; Yang, Yasong; Li, Xueqiong; Yin, Yanbin
2017-01-05
Protein families are often represented by profile hidden Markov models (pHMMs). Homology between two distant protein families can be determined by comparing the pHMMs. Here we explored the idea of building a phylogeny of protein families using the distance matrix of their pHMMs. We developed a new software and web server (pHMM-tree) to allow four major types of inputs: (i) multiple pHMM files, (ii) multiple aligned protein sequence files, (iii) mixture of pHMM and aligned sequence files and (iv) unaligned protein sequences in a single file. The output will be a pHMM phylogeny of different protein families delineating their relationships. We have applied pHMM-tree to build phylogenies for CAZyme (carbohydrate active enzyme) classes and Pfam clans, which attested its usefulness in the phylogenetic representation of the evolutionary relationship among distant protein families.
Projection methods for the numerical solution of Markov chain models
NASA Technical Reports Server (NTRS)
Saad, Youcef
1989-01-01
Projection methods for computing stationary probability distributions for Markov chain models are presented. A general projection method is a method which seeks an approximation from a subspace of small dimension to the original problem. Thus, the original matrix problem of size N is approximated by one of dimension m, typically much smaller than N. A particularly successful class of methods based on this principle is that of Krylov subspace methods which utilize subspaces of the form span(v,av,...,A(exp m-1)v). These methods are effective in solving linear systems and eigenvalue problems (Lanczos, Arnoldi,...) as well as nonlinear equations. They can be combined with more traditional iterative methods such as successive overrelaxation, symmetric successive overrelaxation, or with incomplete factorization methods to enhance convergence.
A Markov decision model for determining optimal outpatient scheduling.
Patrick, Jonathan
2012-06-01
Managing an efficient outpatient clinic can often be complicated by significant no-show rates and escalating appointment lead times. One method that has been proposed for avoiding the wasted capacity due to no-shows is called open or advanced access. The essence of open access is "do today's demand today". We develop a Markov Decision Process (MDP) model that demonstrates that a short booking window does significantly better than open access. We analyze a number of scenarios that explore the trade-off between patient-related measures (lead times) and physician- or system-related measures (revenue, overtime and idle time). Through simulation, we demonstrate that, over a wide variety of potential scenarios and clinics, the MDP policy does as well or better than open access in terms of minimizing costs (or maximizing profits) as well as providing more consistent throughput.
Modeling age-of-onset: Cox model with latent major gene effects
Li, H.; Thompson, E.A.
1994-09-01
Analysis of age-of-onset is a key factor in the segregation and linkage analysis of complex genetic traits, but is complicated by the censoring of unaffected individuals. Most previous work has used parametric distributional assumptions, but it is hard to characterize the distribution of age-of-onset by a single distribution. Other approaches discretize age-of-onset and use logistic regression to model incidence; this approach does not use the information fully. Frailty models have been used for age-of-oset in the biostatistics literature, but these models do not lend themselves to modeling the correlations due to genetic effects which segregate within a family. Here, we propose use of the Cox model with latent major gene effects; conditional on the major genotypes, Cox`s proportional hazards model is used for age-of-onset for each individual. This is a semiparametric model; we do not specify the baseline hazard function. Likelihood analysis of such models is restricted by the difficulty in evaluating of maximizing the likelihood, especially when data are available for some of the members of an extended pedigree. Markov chain Monte Carlo permits genotypic configurations to be realized from the posterior distributions given a current model and the observed data. Hence methods for likelihood analysis can be developed: Monte Carlo EM is used for estimation of the parameters and their variance-covariance matrix. Markers and observed covariates are easily incorporated into this analysis. We present the model, methods for likelihood analysis and the results of a simulation study. The results are comparable with those based on a Cox model with known genotypic dependence in a pedigree. An early-onset Alzheimer`s pedigree and some breast cancer pedigrees have been used as real data examples. Some possible extensions are also discussed.
Latent class model characterization of neighborhood socioeconomic status
Michael, Yvonne; Hyslop, Terry
2016-01-01
Purpose Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. Methods Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. Results Complete data was available for 1,664 breast cancer patients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. Conclusions Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts. PMID:26797452
Comparing quantum versus Markov random walk models of judgements measured by rating scales
Wang, Z.; Busemeyer, J. R.
2016-01-01
Quantum and Markov random walk models are proposed for describing how people evaluate stimuli using rating scales. To empirically test these competing models, we conducted an experiment in which participants judged the effectiveness of public health service announcements from either their own personal perspective or from the perspective of another person. The order of the self versus other judgements was manipulated, which produced significant sequential effects. The quantum and Markov models were fitted to the data using the same number of parameters, and the model comparison strongly supported the quantum over the Markov model. PMID:26621984
Developing Markov chain models for road surface simulation
NASA Astrophysics Data System (ADS)
Israel, Wescott B.; Ferris, John B.
2007-04-01
Chassis loads and vehicle handling are primarily impacted by the road surface over which a vehicle is traversing. By accurately measuring the geometries of road surfaces, one can generate computer models of these surfaces that will allow more accurate predictions of the loads introduced to various vehicle components. However, the logistics and computational power necessary to handle such large data files makes this problem a difficult one to resolve, especially when vehicle design deadlines are impending. This work aims to improve this process by developing Markov Chain models by which all relevant characteristics of road surface geometries will be represented in the model. This will reduce the logistical difficulties that are presented when attempting to collect data and run a simulation using large data sets of individual roads. Models will be generated primarily from measured road profiles of highways in the United States. Any synthetic road realized from a particular model is representative of all profiles in the set from which the model was derived. Realizations of any length can then be generated allowing efficient simulation and timely information about chassis loads that can be used to make better informed design decisions, more quickly.
Weighted-indexed semi-Markov models for modeling financial returns
NASA Astrophysics Data System (ADS)
D'Amico, Guglielmo; Petroni, Filippo
2012-07-01
In this paper we propose a new stochastic model based on a generalization of semi-Markov chains for studying the high frequency price dynamics of traded stocks. We assume that the financial returns are described by a weighted-indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able to reproduce important stylized facts of financial time series such as the first-passage-time distributions and the persistence of volatility. The model is applied to data from the Italian and German stock markets from 1 January 2007 until the end of December 2010.
Classifying movement behaviour in relation to environmental conditions using hidden Markov models.
Patterson, Toby A; Basson, Marinelle; Bravington, Mark V; Gunn, John S
2009-11-01
1. Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development. 2. In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner. 3. It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters. 4. To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna (Thunnus maccoyii). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
ERIC Educational Resources Information Center
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Ensemble bayesian model averaging using markov chain Monte Carlo sampling
Vrugt, Jasper A; Diks, Cees G H; Clark, Martyn P
2008-01-01
Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery etal. Mon Weather Rev 133: 1155-1174, 2(05)) has recommended the Expectation-Maximization (EM) algorithm for BMA model training, even though global convergence of this algorithm cannot be guaranteed. In this paper, we compare the performance of the EM algorithm and the recently developed Differential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo (MCMC) algorithm for estimating the BMA weights and variances. Simulation experiments using 48-hour ensemble data of surface temperature and multi-model stream-flow forecasts show that both methods produce similar results, and that their performance is unaffected by the length of the training data set. However, MCMC simulation with DREAM is capable of efficiently handling a wide variety of BMA predictive distributions, and provides useful information about the uncertainty associated with the estimated BMA weights and variances.
Wang, Xin; Su, Xia; Sun, Wentao; Xie, Yanming; Wang, Yongyan
2011-10-01
In post-marketing study of traditional Chinese medicine (TCM), pharmacoeconomic evaluation has an important applied significance. However, the economic literatures of TCM have been unable to fully and accurately reflect the unique overall outcomes of treatment with TCM. For the special nature of TCM itself, we recommend that Markov model could be introduced into post-marketing pharmacoeconomic evaluation of TCM, and also explore the feasibility of model application. Markov model can extrapolate the study time horizon, suit with effectiveness indicators of TCM, and provide measurable comprehensive outcome. In addition, Markov model can promote the development of TCM quality of life scale and the methodology of post-marketing pharmacoeconomic evaluation.
A Spline Regression Model for Latent Variables
ERIC Educational Resources Information Center
Harring, Jeffrey R.
2014-01-01
Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…
ERIC Educational Resources Information Center
Jang, Hyesuk
2014-01-01
This study aims to evaluate a multidimensional latent trait model to determine how well the model works in various empirical contexts. Contrary to the assumption of these latent trait models that the traits are normally distributed, situations in which the latent trait is not shaped with a normal distribution may occur (Sass et al, 2008; Woods…
ERIC Educational Resources Information Center
von Davier, Matthias; Sinharay, Sandip
2009-01-01
This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the…
Fieberg, John R; Conn, Paul B
2014-01-01
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their
NASA Astrophysics Data System (ADS)
Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran
2016-05-01
Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.
Hidden Markov models from molecular dynamics simulations on DNA.
Thayer, Kelly M; Beveridge, D L
2002-06-25
An enhanced bioinformatics tool incorporating the participation of molecular structure as well as sequence in protein DNA recognition is proposed and tested. Boltzmann probability models of sequence-dependent DNA structure from all-atom molecular dynamics simulations were obtained and incorporated into hidden Markov models (HMMs) that can recognize molecular structural signals as well as sequence in protein-DNA binding sites on a genome. The binding of catabolite activator protein (CAP) to cognate DNA sequences was used as a prototype case for implementation and testing of the method. The results indicate that even HMMs based on probabilistic roll/tilt dinucleotide models of sequence-dependent DNA structure have some capability to discriminate between known CAP binding and nonbinding sites and to predict putative CAP binding sites in unknowns. Restricting HMMs to sequence only in regions of strong consensus in which the protein makes base specific contacts with the cognate DNA further improved the discriminatory capabilities of the HMMs. Comparison of results with controls based on sequence only indicates that extending the definition of consensus from sequence to structure improves the transferability of the HMMs, and provides further supportive evidence of a role for dynamical molecular structure as well as sequence in genomic regulatory mechanisms.
A comparison of weighted ensemble and Markov state model methodologies
NASA Astrophysics Data System (ADS)
Feng, Haoyun; Costaouec, Ronan; Darve, Eric; Izaguirre, Jesús A.
2015-06-01
Computation of reaction rates and elucidation of reaction mechanisms are two of the main goals of molecular dynamics (MD) and related simulation methods. Since it is time consuming to study reaction mechanisms over long time scales using brute force MD simulations, two ensemble methods, Markov State Models (MSMs) and Weighted Ensemble (WE), have been proposed to accelerate the procedure. Both approaches require clustering of microscopic configurations into networks of "macro-states" for different purposes. MSMs model a discretization of the original dynamics on the macro-states. Accuracy of the model significantly relies on the boundaries of macro-states. On the other hand, WE uses macro-states to formulate a resampling procedure that kills and splits MD simulations for achieving better efficiency of sampling. Comparing to MSMs, accuracy of WE rate predictions is less sensitive to the definition of macro-states. Rigorous numerical experiments using alanine dipeptide and penta-alanine support our analyses. It is shown that MSMs introduce significant biases in the computation of reaction rates, which depend on the boundaries of macro-states, and Accelerated Weighted Ensemble (AWE), a formulation of weighted ensemble that uses the notion of colors to compute fluxes, has reliable flux estimation on varying definitions of macro-states. Our results suggest that whereas MSMs provide a good idea of the metastable sets and visualization of overall dynamics, AWE provides reliable rate estimations requiring less efforts on defining macro-states on the high dimensional conformational space.
Markov modeling of ion channels: implications for understanding disease.
Lampert, Angelika; Korngreen, Alon
2014-01-01
Ion channels are the bridge between the biochemical and electrical domains of our life. These membrane crossing proteins use the electric energy stored in transmembrane ion gradients, which are produced by biochemical activity to generate ionic currents. Each ion channel can be imagined as a small power plant similar to a hydroelectric power station, in which potential energy is converted into electric current. This current drives basically all physiological mechanisms of our body. It is clear that a functional blueprint of these amazing cellular power plants is essential for understanding the principle of all aspects of physiology, particularly neurophysiology. The golden path toward this blueprint starts with the biophysical investigation of ion channel activity and continues through detailed numerical modeling of these channels that will eventually lead to a full system-level description of cellular and organ physiology. Here, we discuss the first two stages of this process focusing on voltage-gated channels, particularly the voltage-gated sodium channel which is neurologically and pathologically important. We first detail the correlations between the known structure of the channel and its activity and describe some pathologies. We then provide a hands-on description of Markov modeling for voltage-gated channels. These two sections of the chapter highlight the dichotomy between the vast amounts of electrophysiological data available on voltage-gated channels and the relatively meager number of physiologically relevant models for these channels.
Grey-Markov model with state membership degree and its application
NASA Astrophysics Data System (ADS)
Ye, Jing; Li, Bingjun; Liu, Fang
2013-10-01
In the Grey-Markov forecasting, the extent of a given state that a research object belongs to is expressed as state membership degree. The state membership degree can help compensate for the inaccurate states division and improve the predicted results. Based on the Grey-Markov forecasting analysis, this paper uses the central triangle albino function to calculate the state membership degrees of research objects and determine the state transition probability. Thereby, the new model achieves the improvement of conventional Grey-Markov model. Taking the grain production of Henan Province as an example, the validity and applicability of the improved model are verified.
Modeling healthcare data using multiple-channel latent Dirichlet allocation.
Lu, Hsin-Min; Wei, Chih-Ping; Hsiao, Fei-Yuan
2016-04-01
Information and communications technologies have enabled healthcare institutions to accumulate large amounts of healthcare data that include diagnoses, medications, and additional contextual information such as patient demographics. To gain a better understanding of big healthcare data and to develop better data-driven clinical decision support systems, we propose a novel multiple-channel latent Dirichlet allocation (MCLDA) approach for modeling diagnoses, medications, and contextual information in healthcare data. The proposed MCLDA model assumes that a latent health status group structure is responsible for the observed co-occurrences among diagnoses, medications, and contextual information. Using a real-world research testbed that includes one million healthcare insurance claim records, we investigate the utility of MCLDA. Our empirical evaluation results suggest that MCLDA is capable of capturing the comorbidity structures and linking them with the distribution of medications. Moreover, MCLDA is able to identify the pairing between diagnoses and medications in a record based on the assigned latent groups. MCLDA can also be employed to predict missing medications or diagnoses given partial records. Our evaluation results also show that, in most cases, MCLDA outperforms alternative methods such as logistic regressions and the k-nearest-neighbor (KNN) model for two prediction tasks, i.e., medication and diagnosis prediction. Thus, MCLDA represents a promising approach to modeling healthcare data for clinical decision support.
Latent state-space models for neural decoding.
Aghagolzadeh, Mehdi; Truccolo, Wilson
2014-01-01
Ensembles of single-neurons in motor cortex can show strong low-dimensional collective dynamics. In this study, we explore an approach where neural decoding is applied to estimated low-dimensional dynamics instead of to the full recorded neuronal population. A latent state-space model (SSM) approach is used to estimate the low-dimensional neural dynamics from the measured spiking activity in population of neurons. A second state-space model representation is then used to decode kinematics, via a Kalman filter, from the estimated low-dimensional dynamics. The latent SSM-based decoding approach is illustrated on neuronal activity recorded from primary motor cortex in a monkey performing naturalistic 3-D reach and grasp movements. Our analysis show that 3-D reach decoding performance based on estimated low-dimensional dynamics is comparable to the decoding performance based on the full recorded neuronal population.
Efficient inference of hidden Markov models from large observation sequences
NASA Astrophysics Data System (ADS)
Priest, Benjamin W.; Cybenko, George
2016-05-01
The hidden Markov model (HMM) is widely used to model time series data. However, the conventional Baum- Welch algorithm is known to perform poorly when applied to long observation sequences. The literature contains several alternatives that seek to improve the memory or time complexity of the algorithm. However, for an HMM with N states and an observation sequence of length T, these alternatives require at best O(N) space and O(N2T) time. Given the preponderance of applications that increasingly deal with massive amounts of data, an alternative whose time is O(T)+poly(N) is desired. Recent research presents an alternative to the Baum-Welch algorithm that relies on nonnegative matrix factorization. This document examines the space complexity of this alternative approach and proposes further optimizations using approaches adopted from the matrix sketching literature. The result is a streaming algorithm whose space complexity is constant and time complexity is linear with respect to the size of the observation sequence. The paper also presents a batch algorithm that allow for even further improved space complexity at the expense of an additional pass over the observation sequence.
Optical character recognition of handwritten Arabic using hidden Markov models
NASA Astrophysics Data System (ADS)
Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.
2011-04-01
The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.
Optical character recognition of handwritten Arabic using hidden Markov models
Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M
2011-01-01
The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.
ERIC Educational Resources Information Center
Henson, James M.; Reise, Steven P.; Kim, Kevin H.
2007-01-01
The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) [times] 3 (exogenous latent mean difference) [times] 3 (endogenous latent mean difference) [times] 3 (correlation between factors) [times] 3 (mixture proportions) factorial design. In addition, the efficacy of several…
ERIC Educational Resources Information Center
Park, Jungkyu; Yu, Hsiu-Ting
2016-01-01
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
Marginal Maximum Likelihood Estimation of a Latent Variable Model with Interaction
ERIC Educational Resources Information Center
Cudeck, Robert; Harring, Jeffrey R.; du Toit, Stephen H. C.
2009-01-01
There has been considerable interest in nonlinear latent variable models specifying interaction between latent variables. Although it seems to be only slightly more complex than linear regression without the interaction, the model that includes a product of latent variables cannot be estimated by maximum likelihood assuming normality.…
Latent log-linear models for handwritten digit classification.
Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann
2012-06-01
We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.
Modeling Coordination in Multiple Simultaneous Latent Change Scores
Butner, Jonathan E.; Berg, Cynthia A.; Baucom, Brian R.; Wiebe, Deborah J.
2016-01-01
Coordination is a taxonomy of how processes change together through time. It depicts the changes of two or more variables in terms of the strength and consistency of their covariation, the directionality of their covariation (i.e., do increases in one variable correspond with increases [in-phase] or decreases [anti-phase] in the other variable), and the timing of their covariation (i.e., do both variables change at the same rate or does one variable change faster than the other). Current methods are able to characterize some, but not all, of these aspects of coordination and provide incomplete information as a result. The current study addresses this limitation by demonstrating that multivariate latent change score models can be used to fully differentiate all possible coordination patterns. Furthermore, one can then expand coordination beyond the two outcome case to test arrangements of underlying coordination mechanisms or patterns. Examples using two simultaneous latent change score models and four simultaneous latent change score models illustrate this approach within the context of adolescents and parents regulating type 1 diabetes. PMID:26735358
Bayesian Hidden Markov Modeling of Array CGH Data.
Guha, Subharup; Li, Yi; Neuberg, Donna
2008-06-01
Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data.We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Because the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme, and breast cancer are analyzed, and comparisons are made with some widely used algorithms to illustrate the reliability and success of the technique.
Variable Star Signature Classification using Slotted Symbolic Markov Modeling
NASA Astrophysics Data System (ADS)
Johnston, Kyle B.; Peter, Adrian M.
2016-01-01
With the advent of digital astronomy, new benefits and new challenges have been presented to the modern day astronomer. No longer can the astronomer rely on manual processing, instead the profession as a whole has begun to adopt more advanced computational means. Our research focuses on the construction and application of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern classification algorithm for the identification of variable stars. A methodology for the reduction of stellar variable observations (time-domain data) into a novel feature space representation is introduced. The methodology presented will be referred to as Slotted Symbolic Markov Modeling (SSMM) and has a number of advantages which will be demonstrated to be beneficial; specifically to the supervised classification of stellar variables. It will be shown that the methodology outperformed a baseline standard methodology on a standardized set of stellar light curve data. The performance on a set of data derived from the LINEAR dataset will also be shown.
Variable Star Signature Classification using Slotted Symbolic Markov Modeling
NASA Astrophysics Data System (ADS)
Johnston, K. B.; Peter, A. M.
2017-01-01
With the advent of digital astronomy, new benefits and new challenges have been presented to the modern day astronomer. No longer can the astronomer rely on manual processing, instead the profession as a whole has begun to adopt more advanced computational means. This paper focuses on the construction and application of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern classification algorithm for the identification of variable stars. A methodology for the reduction of stellar variable observations (time-domain data) into a novel feature space representation is introduced. The methodology presented will be referred to as Slotted Symbolic Markov Modeling (SSMM) and has a number of advantages which will be demonstrated to be beneficial; specifically to the supervised classification of stellar variables. It will be shown that the methodology outperformed a baseline standard methodology on a standardized set of stellar light curve data. The performance on a set of data derived from the LINEAR dataset will also be shown.
Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.
Spahn, Philipp N; Hansen, Anders H; Kol, Stefan; Voldborg, Bjørn G; Lewis, Nathan E
2017-02-01
Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties is described. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.
Continuous myoelectric control for powered prostheses using hidden Markov models.
Chan, Adrian D C; Englehart, Kevin B
2005-01-01
This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.
Technical manual for basic version of the Markov chain nest productivity model (MCnest)
The Markov Chain Nest Productivity Model (or MCnest) integrates existing toxicity information from three standardized avian toxicity tests with information on species life history and the timing of pesticide applications relative to the timing of avian breeding seasons to quantit...
A Bayesian Hidden Markov Model-based approach for anomaly detection in electronic systems
NASA Astrophysics Data System (ADS)
Dorj, E.; Chen, C.; Pecht, M.
Early detection of anomalies in any system or component prevents impending failures and enhances performance and availability. The complex architecture of electronics, the interdependency of component functionalities, and the miniaturization of most electronic systems make it difficult to detect and analyze anomalous behaviors. A Hidden Markov Model-based classification technique determines unobservable hidden behaviors of complex and remotely inaccessible electronic systems using observable signals. This paper presents a data-driven approach for anomaly detection in electronic systems based on a Bayesian Hidden Markov Model classification technique. The posterior parameters of the Hidden Markov Models are estimated using the conjugate prior method. An application of the developed Bayesian Hidden Markov Model-based anomaly detection approach is presented for detecting anomalous behavior in Insulated Gate Bipolar Transistors using experimental data. The detection results illustrate that the developed anomaly detection approach can help detect anomalous behaviors in electronic systems, which can help prevent system downtime and catastrophic failures.
User’s manual for basic version of MCnest Markov chain nest productivity model
The Markov Chain Nest Productivity Model (or MCnest) integrates existing toxicity information from three standardized avian toxicity tests with information on species life history and the timing of pesticide applications relative to the timing of avian breeding seasons to quantit...
Discriminative Latent Models for Recognizing Contextual Group Activities
Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg
2012-01-01
In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516
Discriminative latent models for recognizing contextual group activities.
Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg
2012-08-01
In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.
Design Improvement and Implementation of 3D Gauss-Markov Mobility Model
2013-02-19
AFFTC-PA-12430 Design Improvement and Implementation of 3D Gauss-Markov Mobility Model Mohammed Alenazi, Cenk Sahin , and James P.G. Sterbenz...0019 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Mohammed Alenazi, Cenk Sahin , and James P.G. Sterbenz 5d. PROJECT...Gauss-Markov Mobility Model Mohammed Alenazi and Cenk Sahin Faculty Advisor: James P.G. Sterbenz Department of Electrical Engineering & Computer Science
Zhou, De; Lin, Zhulu; Liu, Liming
2012-11-15
Land salinization and desalinization are complex processes affected by both biophysical and human-induced driving factors. Conventional approaches of land salinization assessment and simulation are either too time consuming or focus only on biophysical factors. The cellular automaton (CA)-Markov model, when coupled with spatial pattern analysis, is well suited for regional assessments and simulations of salt-affected landscapes since both biophysical and socioeconomic data can be efficiently incorporated into a geographic information system framework. Our hypothesis set forth that the CA-Markov model can serve as an alternative tool for regional assessment and simulation of land salinization or desalinization. Our results suggest that the CA-Markov model, when incorporating biophysical and human-induced factors, performs better than the model which did not account for these factors when simulating the salt-affected landscape of the Yinchuan Plain (China) in 2009. In general, the CA-Markov model is best suited for short-term simulations and the performance of the CA-Markov model is largely determined by the availability of high-quality, high-resolution socioeconomic data. The coupling of the CA-Markov model with spatial pattern analysis provides an improved understanding of spatial and temporal variations of salt-affected landscape changes and an option to test different soil management scenarios for salinity management.
Latent spatial models and sampling design for landscape genetics
Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
2016-01-01
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
Efficient decoding algorithms for generalized hidden Markov model gene finders
Majoros, William H; Pertea, Mihaela; Delcher, Arthur L; Salzberg, Steven L
2005-01-01
Background The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. Results As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. Conclusions In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques. PMID:15667658
Siddique, Juned; Crespi, Catherine M; Gibbons, Robert D; Green, Bonnie L
2011-01-30
We present an approach that uses latent variable modeling and multiple imputation to correct rater bias when one group of raters tends to be more lenient in assigning a diagnosis than another. Our method assumes that there exists an unobserved moderate category of patient who is assigned a positive diagnosis by one type of rater and a negative diagnosis by the other type. We present a Bayesian random effects censored ordinal probit model that allows us to calibrate the diagnoses across rater types by identifying and multiply imputing 'case' or 'non-case' status for patients in the moderate category. A Markov chain Monte Carlo algorithm is presented to estimate the posterior distribution of the model parameters and generate multiple imputations. Our method enables the calibrated diagnosis variable to be used in subsequent analyses while also preserving uncertainty in true diagnosis. We apply our model to diagnoses of posttraumatic stress disorder (PTSD) from a depression study where nurse practitioners were twice as likely as clinical psychologists to diagnose PTSD despite the fact that participants were randomly assigned to either a nurse or a psychologist. Our model appears to balance PTSD rates across raters, provides a good fit to the data, and preserves between-rater variability. After calibrating the diagnoses of PTSD across rater types, we perform an analysis looking at the effects of comorbid PTSD on changes in depression scores over time. Results are compared with an analysis that uses the original diagnoses and show that calibrating the PTSD diagnoses can yield different inferences.
Accelerating Information Retrieval from Profile Hidden Markov Model Databases
Ashhab, Yaqoub; Tamimi, Hashem
2016-01-01
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases. PMID:27875548
Markov Model of Severe Accident Progression and Management
Bari, R.A.; Cheng, L.; Cuadra,A.; Ginsberg,T.; Lehner,J.; Martinez-Guridi,G.; Mubayi,V.; Pratt,W.T.; Yue, M.
2012-06-25
The earthquake and tsunami that hit the nuclear power plants at the Fukushima Daiichi site in March 2011 led to extensive fuel damage, including possible fuel melting, slumping, and relocation at the affected reactors. A so-called feed-and-bleed mode of reactor cooling was initially established to remove decay heat. The plan was to eventually switch over to a recirculation cooling system. Failure of feed and bleed was a possibility during the interim period. Furthermore, even if recirculation was established, there was a possibility of its subsequent failure. Decay heat has to be sufficiently removed to prevent further core degradation. To understand the possible evolution of the accident conditions and to have a tool for potential future hypothetical evaluations of accidents at other nuclear facilities, a Markov model of the state of the reactors was constructed in the immediate aftermath of the accident and was executed under different assumptions of potential future challenges. This work was performed at the request of the U.S. Department of Energy to explore 'what-if' scenarios in the immediate aftermath of the accident. The work began in mid-March and continued until mid-May 2011. The analysis had the following goals: (1) To provide an overall framework for describing possible future states of the damaged reactors; (2) To permit an impact analysis of 'what-if' scenarios that could lead to more severe outcomes; (3) To determine approximate probabilities of alternative end-states under various assumptions about failure and repair times of cooling systems; (4) To infer the reliability requirements of closed loop cooling systems needed to achieve stable core end-states and (5) To establish the importance for the results of the various cooling system and physical phenomenological parameters via sensitivity calculations.
Accelerating Information Retrieval from Profile Hidden Markov Model Databases.
Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem
2016-01-01
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.
Experimental evaluation of a Markov multizone model of particulate contaminant transport.
Jones, Rachael M; Nicas, Mark
2014-10-01
The performance of a Markov chain model of the three-dimensional transport of particulates in indoor environments is evaluated against experimentally measured supermicrometer particle deposition. Previously, the model was found to replicate the predictions of relatively simple particle transport and fate models; and this work represents the next step in model evaluation. The experiments modeled were (i) the release of polydispersed particles inside a building lobby, and (ii) the release of monodispersed fluorescein-tagged particles inside an experimental chamber under natural and forced mixing. The Markov model was able to reproduce the spatial patterns of particle deposition in both experiments, though the model predictions were sensitive to the parameterization of the particle release mechanism in the second experiment. Overall, the results indicate that the Markov model is a plausible tool for modeling the fate and transport of supermicrometer particles.
Geiser, Christian; Bishop, Jacob; Lockhart, Ginger; Shiffman, Saul; Grenard, Jerry L.
2013-01-01
Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models. PMID:24416023
A Simple Discrete Model of Brownian Motors: Time-periodic Markov Chains
NASA Astrophysics Data System (ADS)
Ge, Hao; Jiang, Da-Quan; Qian, Min
2006-05-01
In this paper, we consider periodically inhomogeneous Markov chains, which can be regarded as a simple version of physical model—Brownian motors. We introduce for them the concepts of periodical reversibility, detailed balance, entropy production rate and circulation distribution. We prove the equivalence of the following statements: The time-periodic Markov chain is periodically reversible; It is in detailed balance; Kolmogorov's cycle condition is satisfied; Its entropy production rate vanishes; Every circuit and its reversed circuit have the same circulation weight. Hence, in our model of Markov chains, the directed transport phenomenon of Brownian motors, i.e. the existence of net circulation, can occur only in nonequilibrium and irreversible systems. Moreover, we verify the large deviation property and the Gallavotti-Cohen fluctuation theorem of sample entropy production rates of the Markov chain.
Mexican immigrants' explanatory model of latent tuberculosis infection.
McEwen, Marylyn M
2005-10-01
This article reveals how the multiple and disparate explanations of latent tuberculosis infection (LTBI) from the U.S. and Mexico professional health sectors and the popular sector are used to inform the explanatory model (EM) of LTBI for Mexican immigrants residing in the U.S.-Mexico border region. Fourteen immigrants, nine diagnosed with LTBI (n = 9) and their spouses (n = 5) participated in this critical ethnographic study. Because care seeking and treatment decisions are influenced by EMs, the results indicate that it is imperative that interventions for Mexican immigrants with LTBI are built on an understanding of their illness experience and are contextually meaningful.
A robust hidden semi-Markov model with application to aCGH data processing.
Ding, Jiarui; Shah, Sohrab
2013-01-01
Hidden semi-Markov models are effective at modelling sequences with succession of homogenous zones by choosing appropriate state duration distributions. To compensate for model mis-specification and provide protection against outliers, we design a robust hidden semi-Markov model with Student's t mixture models as the emission distributions. The proposed approach is used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and glioblastoma multiforme data illustrate the reliability of the technique.
Drifting Markov models with polynomial drift and applications to DNA sequences.
Vergne, Nicolas
2008-01-01
In this article, we introduce the drifting Markov models (DMMs) which are inhomogeneous Markov models designed for modeling the heterogeneities of sequences (in our case DNA or protein sequences) in a more flexible way than homogeneous Markov chains or even hidden Markov models (HMMs). We focus here on the polynomial drift: the transition matrix varies in a polynomial way. To show the reliability of our models on DNA, we exhibit high similarities between the probability distributions of nucleotides obtained by our models and the frequencies of these nucleotides computed by using a sliding window. In a further step, these DMMs can be used as the states of an HMM: on each of its segments, the observed process can be modeled by a drifting Markov model. Search of rare words in DNA sequences remains possible with DMMs and according to the fits provided, DMMs turn out to be a powerful tool for this purpose. The software is available on request from the author. It will soon be integrated on seq++ library (http://stat.genopole.cnrs.fr/seqpp/).
Latent variable modeling paradigms for genotype-trait association studies.
Liu, Yan; Foulkes, Andrea S
2011-09-01
Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, this presents a significant analytic challenge due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling approaches offer a natural framework for analysis of data arising from these population-based genetic association investigations of complex diseases as they are well-suited to uncover simultaneous effects of multiple markers. In this manuscript we describe application and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and, finally, an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-infected individuals is provided for illustration.
MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System
Bari,R.A.
2008-07-13
The Generation IV International Forum (GIF) emphasizes proliferation resistance and physical protection (PR&PP) as a main goal for future nuclear energy systems. The GIF PR&PP Working Group has developed a methodology for the evaluation of these systems. As an application of the methodology, Markov model has been developed for the evaluation of proliferation resistance and is demonstrated for a hypothetical Example Sodium Fast Reactor (ESFR) system. This paper presents the case of diversion by the facility owner/operator to obtain material that could be used in a nuclear weapon. The Markov model is applied to evaluate material diversion strategies. The following features of the Markov model are presented here: (1) An effective detection rate has been introduced to account for the implementation of multiple safeguards approaches at a given strategic point; (2) Technical failure to divert material is modeled as intrinsic barriers related to the design of the facility or the properties of the material in the facility; and (3) Concealment to defeat or degrade the performance of safeguards is recognized in the Markov model. Three proliferation risk measures are calculated directly by the Markov model: the detection probability, technical failure probability, and proliferation time. The material type is indicated by an index that is based on the quality of material diverted. Sensitivity cases have been done to demonstrate the effects of different modeling features on the measures of proliferation resistance.
Prediction of User's Web-Browsing Behavior: Application of Markov Model.
Awad, M A; Khalil, I
2012-08-01
Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.
Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W
2007-07-01
Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.
Semi-Markov Models for Degradation-Based Reliability
2010-01-01
standard analysis techniques for Markov processes can be employed (cf. Whitt (1984), Altiok (1985), Perros (1994), and Osogami and Harchol-Balter...We want to approximate X by a PH random variable, sayY, with c.d.f. Ĥ. Marie (1980), Altiok (1985), Johnson (1993), Perros (1994), and Osogami and...provides a minimal representation when matching only two moments. By considering the guidance provided by Marie (1980), Whitt (1984), Altiok (1985), Perros
Reliability analysis and prediction of mixed mode load using Markov Chain Model
Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.
2014-06-19
The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading.
Hierarchical modeling for reliability analysis using Markov models. B.S./M.S. Thesis - MIT
NASA Technical Reports Server (NTRS)
Fagundo, Arturo
1994-01-01
Markov models represent an extremely attractive tool for the reliability analysis of many systems. However, Markov model state space grows exponentially with the number of components in a given system. Thus, for very large systems Markov modeling techniques alone become intractable in both memory and CPU time. Often a particular subsystem can be found within some larger system where the dependence of the larger system on the subsystem is of a particularly simple form. This simple dependence can be used to decompose such a system into one or more subsystems. A hierarchical technique is presented which can be used to evaluate these subsystems in such a way that their reliabilities can be combined to obtain the reliability for the full system. This hierarchical approach is unique in that it allows the subsystem model to pass multiple aggregate state information to the higher level model, allowing more general systems to be evaluated. Guidelines are developed to assist in the system decomposition. An appropriate method for determining subsystem reliability is also developed. This method gives rise to some interesting numerical issues. Numerical error due to roundoff and integration are discussed at length. Once a decomposition is chosen, the remaining analysis is straightforward but tedious. However, an approach is developed for simplifying the recombination of subsystem reliabilities. Finally, a real world system is used to illustrate the use of this technique in a more practical context.
Bucci, P.; Mangan, L. A.; Kirschenbaum, J.; Mandelli, D.; Aldemir, T.; Arndt, S. A.
2006-07-01
Markov models have the ability to capture the statistical dependence between failure events that can arise in the presence of complex dynamic interactions between components of digital instrumentation and control systems. One obstacle to the use of such models in an existing probabilistic risk assessment (PRA) is that most of the currently available PRA software is based on the static event-tree/fault-tree methodology which often cannot represent such interactions. We present an approach to the integration of Markov reliability models into existing PRAs by describing the Markov model of a digital steam generator feedwater level control system, how dynamic event trees (DETs) can be generated from the model, and how the DETs can be incorporated into an existing PRA with the SAPHIRE software. (authors)
ERIC Educational Resources Information Center
Bartolucci, F.; Montanari, G. E.; Pandolfi, S.
2012-01-01
With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item…
Bayesian Analysis of Multivariate Latent Curve Models with Nonlinear Longitudinal Latent Effects
ERIC Educational Resources Information Center
Song, Xin-Yuan; Lee, Sik-Yum; Hser, Yih-Ing
2009-01-01
In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly…
Estimation of Parameters in Latent Class Models with Constraints on the Parameters.
1986-06-01
the item parameters. Let us briefly review the elements of latent class models. The reader desiring a thorough introduction can consult Lazarsfeld and...parameters, including most of the models which have been proposed to date. The latent distance model of Lazarsfeld and Henry (1968) and the quasi...Psychometrika, 1964, 29, 115-129. Lazarsfeld , P.F., and Henry, N.W. Latent structure analysis. Boston: Houghton-Mifflin, 1968. L6. - 29 References continued
The hidden Markov Topic model: a probabilistic model of semantic representation.
Andrews, Mark; Vigliocco, Gabriella
2010-01-01
In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag-of-words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag-of-words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), preserving its strengths while extending its scope to incorporate more fine-grained linguistic information.
The use of latent trajectory models in psychopathology research.
Curran, Patrick J; Hussong, Andrea M
2003-11-01
Despite the recent surge in the development of powerful modeling strategies to test questions about individual differences in stability and change over time, these methods are not currently widely used in psychopathology research. In an attempt to further the dissemination of these new methods, the authors present a pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM. They review several different types of LTMs, discuss matching an optimal LTM to a given question of interest, and highlight several issues that might be particularly salient for research in psychopathology. The authors augment each section with a review of published applications of these methods in psychopathology-related research to demonstrate the implementation and interpretation of LTMs in practice.
Bayesian latent structure modeling of walking behavior in a physical activity intervention
Lawson, Andrew B; Ellerbe, Caitlyn; Carroll, Rachel; Alia, Kassandra; Coulon, Sandra; Wilson, Dawn K; VanHorn, M Lee; St George, Sara M
2017-01-01
The analysis of walking behavior in a physical activity intervention is considered. A Bayesian latent structure modeling approach is proposed whereby the ability and willingness of participants is modeled via latent effects. The dropout process is jointly modeled via a linked survival model. Computational issues are addressed via posterior sampling and a simulated evaluation of the longitudinal model’s ability to recover latent structure and predictor effects is considered. We evaluate the effect of a variety of socio-psychological and spatial neighborhood predictors on the propensity to walk and the estimation of latent ability and willingness in the full study. PMID:24741000
ERIC Educational Resources Information Center
Bray, Bethany C.; Lanza, Stephanie T.; Collins, Linda M.
2010-01-01
To understand one developmental process, it is often helpful to investigate its relations with other developmental processes. Statistical methods that model development in multiple processes simultaneously over time include latent growth curve models with time-varying covariates, multivariate latent growth curve models, and dual trajectory models.…
Projected metastable Markov processes and their estimation with observable operator models
Wu, Hao Prinz, Jan-Hendrik Noé, Frank
2015-10-14
The determination of kinetics of high-dimensional dynamical systems, such as macromolecules, polymers, or spin systems, is a difficult and generally unsolved problem — both in simulation, where the optimal reaction coordinate(s) are generally unknown and are difficult to compute, and in experimental measurements, where only specific coordinates are observable. Markov models, or Markov state models, are widely used but suffer from the fact that the dynamics on a coarsely discretized state spaced are no longer Markovian, even if the dynamics in the full phase space are. The recently proposed projected Markov models (PMMs) are a formulation that provides a description of the kinetics on a low-dimensional projection without making the Markovianity assumption. However, as yet no general way of estimating PMMs from data has been available. Here, we show that the observed dynamics of a PMM can be exactly described by an observable operator model (OOM) and derive a PMM estimator based on the OOM learning.
Latent Tuberculosis Infection: Myths, Models, and Molecular Mechanisms
Dutta, Noton K.
2014-01-01
SUMMARY The aim of this review is to present the current state of knowledge on human latent tuberculosis infection (LTBI) based on clinical studies and observations, as well as experimental in vitro and animal models. Several key terms are defined, including “latency,” “persistence,” “dormancy,” and “antibiotic tolerance.” Dogmas prevalent in the field are critically examined based on available clinical and experimental data, including the long-held beliefs that infection is either latent or active, that LTBI represents a small population of nonreplicating, “dormant” bacilli, and that caseous granulomas are the haven for LTBI. The role of host factors, such as CD4+ and CD8+ T cells, T regulatory cells, tumor necrosis factor alpha (TNF-α), and gamma interferon (IFN-γ), in controlling TB infection is discussed. We also highlight microbial regulatory and metabolic pathways implicated in bacillary growth restriction and antibiotic tolerance under various physiologically relevant conditions. Finally, we pose several clinically important questions, which remain unanswered and will serve to stimulate future research on LTBI. PMID:25184558
The Autonomous Duck: Exploring the Possibilities of a Markov Chain Model in Animation
NASA Astrophysics Data System (ADS)
Villegas, Javier
This document reports the construction of a framework for the generation of animations based in a Markov chain model of the different poses of some drawn character. The model was implemented and is demonstrated with the animation of a virtual duck in a random walk. Some potential uses of this model in interpolation and generation of in between frames are also explored.
Post processing with first- and second-order hidden Markov models
NASA Astrophysics Data System (ADS)
Taghva, Kazem; Poudel, Srijana; Malreddy, Spandana
2013-01-01
In this paper, we present the implementation and evaluation of first order and second order Hidden Markov Models to identify and correct OCR errors in the post processing of books. Our experiments show that the first order model approximately corrects 10% of the errors with 100% precision, while the second order model corrects a higher percentage of errors with much lower precision.
Transition probabilities matrix of Markov Chain in the fatigue crack growth model
NASA Astrophysics Data System (ADS)
Nopiah, Zulkifli Mohd; Januri, Siti Sarah; Ariffin, Ahmad Kamal; Masseran, Nurulkamal; Abdullah, Shahrum
2016-10-01
Markov model is one of the reliable method to describe the growth of the crack from the initial until fracture phase. One of the important subjects in the crack growth models is to obtain the transition probability matrix of the fatigue. Determining probability transition matrix is important in Markov Chain model for describing probability behaviour of fatigue life in the structure. In this paper, we obtain transition probabilities of a Markov chain based on the Paris law equation to describe the physical meaning of fatigue crack growth problem. The results show that the transition probabilities are capable to calculate the probability of damage in the future with the possibilities of comparing each stage between time.
The Moments of Matched and Mismatched Hidden Markov Models
1987-06-11
preprocessor. Denote by hkj the probability that the observation symbol Vk is altered to symbol V. by the noise mechanism and define the m-by-m noise3...probability matrix H = [ hkj ]. It is assumed that H is independent of the state of the Markov chain and of time t. Consequently, the output of a given HMM...chain is i and that symbol j is produced, given that symbol k was the output of the given HMM. The sum over k of bik hkj gives the component b.j of the
A Latent Transition Analysis Model for Assessing Change in Cognitive Skills
ERIC Educational Resources Information Center
Li, Feiming; Cohen, Allan; Bottge, Brian; Templin, Jonathan
2016-01-01
Latent transition analysis (LTA) was initially developed to provide a means of measuring change in dynamic latent variables. In this article, we illustrate the use of a cognitive diagnostic model, the DINA model, as the measurement model in a LTA, thereby demonstrating a means of analyzing change in cognitive skills over time. An example is…
Examining Parallelism of Sets of Psychometric Measures Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Patelis, Thanos; Marcoulides, George A.
2011-01-01
A latent variable modeling approach that can be used to examine whether several psychometric tests are parallel is discussed. The method consists of sequentially testing the properties of parallel measures via a corresponding relaxation of parameter constraints in a saturated model or an appropriately constructed latent variable model. The…
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
ERIC Educational Resources Information Center
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
Addressing the Problem of Switched Class Labels in Latent Variable Mixture Model Simulation Studies
ERIC Educational Resources Information Center
Tueller, Stephen J.; Drotar, Scott; Lubke, Gitta H.
2011-01-01
The discrimination between alternative models and the detection of latent classes in the context of latent variable mixture modeling depends on sample size, class separation, and other aspects that are related to power. Prior to a mixture analysis it is useful to investigate model performance in a simulation study that reflects the research…
ERIC Educational Resources Information Center
Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.
2011-01-01
Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…
Assessing Fit of Latent Regression Models. Research Report. ETS RR-09-50
ERIC Educational Resources Information Center
Sinharay, Sandip; Guo, Zhumei; von Davier, Matthias; Veldkamp, Bernard P.
2009-01-01
The reporting methods used in large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) rely on a "latent regression model". There is a lack of research on the assessment of fit of latent regression models. This paper suggests a simulation-based model-fit technique to assess the fit of such…
Stochastic Ordering of the Latent Trait by the Sum Score Under Various Polytomous IRT Models
ERIC Educational Resources Information Center
van der Ark, L. Andries
2005-01-01
The sum score is often used to order respondents on the latent trait measured by the test. Therefore, it is desirable that under the chosen model the sum score stochastically orders the latent trait. It is known that unlike dichotomous item response theory (IRT) models, most polytomous IRT models do not imply stochastic ordering. It is unknown,…
ERIC Educational Resources Information Center
Whittaker, Tiffany A.; Khojasteh, Jam
2017-01-01
Latent growth modeling (LGM) is a popular and flexible technique that may be used when data are collected across several different measurement occasions. Modeling the appropriate growth trajectory has important implications with respect to the accurate interpretation of parameter estimates of interest in a latent growth model that may impact…
Wang, Hongyan; Zhou, Xiaobo
2013-04-01
By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice.
Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments
NASA Astrophysics Data System (ADS)
Moe, Marie E. G.; Helvik, Bjarne E.; Knapskog, Svein J.
Computational trust and reputation models are used to aid the decision-making process in complex dynamic environments, where we are unable to obtain perfect information about the interaction partners. In this paper we present a comparison of our proposed hidden Markov trust model to the Beta reputation system. The hidden Markov trust model takes the time between observations into account, it also distinguishes between system states and uses methods previously applied to intrusion detection for the prediction of which state an agent is in. We show that the hidden Markov trust model performs better when it comes to the detection of changes in behavior of agents, due to its larger richness in model features. This means that our trust model may be more realistic in dynamic environments. However, the increased model complexity also leads to bigger challenges in estimating parameter values for the model. We also show that the hidden Markov trust model can be parameterized so that it responds similarly to the Beta reputation system.
Spatio-temporal contextual classification based on Markov random field model. [for thematic mapping
NASA Technical Reports Server (NTRS)
Jeon, Byeungwoo; Landgrebe, D. A.
1991-01-01
A contextural classifier based on a Markov random field model, which can utilize both spatial and temporal contexts, is investigated. Spatial and temporal neighbors are defined, and the class assignment of each pixel is assumed to be dependent only on the measurement vectors of itself and those of its spatial and temporal neighbors according to the Markov random field property. Only interpixel class dependency context is used in the classification. The joint prior probability of the classes of each pixel and its spatial and temporal neighbors are modeled by a Gibbs random field. The classification is performed in a recursive manner. Experiments with multi-temporal Thematic Mapper data show promising results.
NASA Astrophysics Data System (ADS)
Jamaluddin, Fadhilah; Rahim, Rahela Abdul
2015-12-01
Markov Chain has been introduced since the 1913 for the purpose of studying the flow of data for a consecutive number of years of the data and also forecasting. The important feature in Markov Chain is obtaining the accurate Transition Probability Matrix (TPM). However to obtain the suitable TPM is hard especially in involving long-term modeling due to unavailability of data. This paper aims to enhance the classical Markov Chain by introducing Exponential Smoothing technique in developing the appropriate TPM.
Measures of discrimination for latent group-based trajectory models.
Shah, Nilesh H; Hipwell, Alison E; Stepp, Stephanie D; Chang, Chung-Chou H
2015-01-01
In clinical research, patient care decisions are often easier to make if patients are classified into a manageable number of groups based on homogeneous risk patterns. Investigators can use latent group-based trajectory modeling to estimate the posterior probabilities that an individual will be classified into a particular group of risk patterns. Although this method is increasingly used in clinical research, there is currently no measure that can be used to determine whether an individual's group assignment has a high level of discrimination. In this study, we propose a discrimination index and provide confidence intervals of the probability of the assigned group for each individual. We also propose a modified form of entropy to measure discrimination. The two proposed measures were applied to assess the group assignments of the longitudinal patterns of conduct disorders among young adolescent girls.
Exponential integrators for a Markov chain model of the fast sodium channel of cardiomyocytes.
Starý, Tomás; Biktashev, Vadim N
2015-04-01
The modern Markov chain models of ionic channels in excitable membranes are numerically stiff. The popular numerical methods for these models require very small time steps to ensure stability. Our objective is to formulate and test two methods addressing this issue, so that the timestep can be chosen based on accuracy rather than stability. Both proposed methods extend Rush-Larsen technique, which was originally developed to Hogdkin-Huxley type gate models. One method, "matrix Rush-Larsen" (MRL) uses a matrix reformulation of the Rush-Larsen scheme, where the matrix exponentials are calculated using precomputed tables of eigenvalues and eigenvectors. The other, "hybrid operator splitting" (HOS) method exploits asymptotic properties of a particular Markov chain model, allowing explicit analytical expressions for the substeps. We test both methods on the Clancy and Rudy (2002) I(Na)Markov chain model. With precomputed tables for functions of the transmembrane voltage, both methods are comparable to the forward Euler method in accuracy and computational cost, but allow longer time steps without numerical instability. We conclude that both methods are of practical interest. MRL requires more computations than HOS, but is formulated in general terms which can be readily extended to other Markov chain channel models, whereas the utility of HOS depends on the asymptotic properties of a particular model. The significance of the methods is that they allow a considerable speed-up of large-scale computations of cardiac excitation models by increasing the time step, while maintaining acceptable accuracy and preserving numerical stability.
Cao, Qi; Buskens, Erik; Feenstra, Talitha; Jaarsma, Tiny; Hillege, Hans; Postmus, Douwe
2016-01-01
Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity.
Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee
2016-01-01
Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956
Lamont, Andrea E; Vermunt, Jeroen K; Van Horn, M Lee
2016-01-01
Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.
Detecting critical state before phase transition of complex systems by hidden Markov model
NASA Astrophysics Data System (ADS)
Liu, Rui; Chen, Pei; Li, Yongjun; Chen, Luonan
Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e., before-transition state, pre-transition state, and after-transition state, which can be considered as three different Markov processes. Thus, based on this dynamical feature, we present a novel computational method, i.e., hidden Markov model (HMM), to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e., the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin, and HCV-induced dysplasia and hepatocellular carcinoma.
ERIC Educational Resources Information Center
Grimm, Kevin J.; An, Yang; McArdle, John J.; Zonderman, Alan B.; Resnick, Susan M.
2012-01-01
Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to…
A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses
ERIC Educational Resources Information Center
Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini
2012-01-01
The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…
Structured Latent Curve Models for the Study of Change in Multivariate Repeated Measures
ERIC Educational Resources Information Center
Blozis, Shelley A.
2004-01-01
This article considers a structured latent curve model for multiple repeated measures. In a structured latent curve model, a smooth nonlinear function characterizes the mean response. A first-order Taylor polynomial taken with regard to the mean function defines elements of a restricted factor matrix that may include parameters that enter…
A Taxonomy of Latent Structure Assumptions for Probability Matrix Decomposition Models.
ERIC Educational Resources Information Center
Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven
2003-01-01
Proposed a taxonomy of latent structure assumptions for probability matrix decomposition (PMD) that includes the original PMD model and a three-way extension of the multiple classification latent class model. Simulation study results show the usefulness of the taxonomy. (SLD)
An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models
ERIC Educational Resources Information Center
Cho, Sun-Joo; Suh, Youngsuk; Lee, Woo-yeol
2016-01-01
The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called…
Optimized parameter selection reveals trends in Markov state models for protein folding
NASA Astrophysics Data System (ADS)
Husic, Brooke E.; McGibbon, Robert T.; Sultan, Mohammad M.; Pande, Vijay S.
2016-11-01
As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this analysis by describing a system's states and the transitions between them. A recently established variational theorem for Markov state models now enables modelers to systematically determine the best way to describe a system's dynamics. In the context of the variational theorem, we analyze ultra-long folding simulations for a canonical set of twelve proteins [K. Lindorff-Larsen et al., Science 334, 517 (2011)] by creating and evaluating many types of Markov state models. We present a set of guidelines for constructing Markov state models of protein folding; namely, we recommend the use of cross-validation and a kinetically motivated dimensionality reduction step for improved descriptions of folding dynamics. We also warn that precise kinetics predictions rely on the features chosen to describe the system and pose the description of kinetic uncertainty across ensembles of models as an open issue.
The algebra of the general Markov model on phylogenetic trees and networks.
Sumner, J G; Holland, B R; Jarvis, P D
2012-04-01
It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.
NASA Technical Reports Server (NTRS)
Bole, Brian; Goebel, Kai; Vachtsevanos, George
2012-01-01
This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.
Liu, Zengkai; Liu, Yonghong; Cai, Baoping
2014-01-01
Reliability analysis of the electrical control system of a subsea blowout preventer (BOP) stack is carried out based on Markov method. For the subsea BOP electrical control system used in the current work, the 3-2-1-0 and 3-2-0 input voting schemes are available. The effects of the voting schemes on system performance are evaluated based on Markov models. In addition, the effects of failure rates of the modules and repair time on system reliability indices are also investigated.
Liu, Zengkai; Liu, Yonghong; Cai, Baoping
2014-01-01
Reliability analysis of the electrical control system of a subsea blowout preventer (BOP) stack is carried out based on Markov method. For the subsea BOP electrical control system used in the current work, the 3-2-1-0 and 3-2-0 input voting schemes are available. The effects of the voting schemes on system performance are evaluated based on Markov models. In addition, the effects of failure rates of the modules and repair time on system reliability indices are also investigated. PMID:25409010
Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus
2014-01-01
One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond
NASA Technical Reports Server (NTRS)
Baldi, Pierre
1994-01-01
We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.
Latent variable mixture modeling in psychiatric research--a review and application.
Miettunen, J; Nordström, T; Kaakinen, M; Ahmed, A O
2016-02-01
Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sorting cases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to provide a non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classify individuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach that represents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categorical and dimensional states of affairs. This article provides an overview of latent variable mixture models and illustrates the application of these methods by applying them to the study of the latent structure of psychotic experiences. The flexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatric and psychological phenomena. They also allow researchers to address research questions that directly compare the viability of dimensional, categorical and hybrid conceptions of constructs.
Markov stochasticity coordinates
NASA Astrophysics Data System (ADS)
Eliazar, Iddo
2017-01-01
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method-termed Markov Stochasticity Coordinates-is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
First and second order semi-Markov chains for wind speed modeling
NASA Astrophysics Data System (ADS)
Prattico, F.; Petroni, F.; D'Amico, G.
2012-04-01
The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [3] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [1], by using two models, first
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
NASA Astrophysics Data System (ADS)
Romberg, Justin K.; Choi, Hyeokho; Baraniuk, Richard G.
1999-06-01
Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree model captures the key features of the joint density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training. In this paper, we prose two reduced-parameter HMT models that capture the general structure of a broad class of real-world images. In the image HMT (iHMT) model we use the fact that for a large class of images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters. In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of image estimation/denoising experiments that these two new models retain nearly all of the key structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms all other wavelet- based estimators in the current literature, both in mean- square error and visual metrics.
Exact Solution of the Markov Propagator for the Voter Model on the Complete Graph
2014-07-01
the generating function form of the Markov prop- agator of the random walk. This can be easily generalized to other models simply by specifying the...detailed information about the prop- agator than the bound on consensus. VI. CONCLUSIONS We have successfully derived exact solutions to the voter
A Test of the Need Hierarchy Concept by a Markov Model of Change in Need Strength.
ERIC Educational Resources Information Center
Rauschenberger, John; And Others
1980-01-01
In this study of 547 high school graduates, Alderfer's and Maslow's need hierarchy theories were expressed in Markov chain form and were subjected to empirical test. Both models were disconfirmed. Corroborative multiwave correlational analysis also failed to support the need hierarchy concept. (Author/IRT)
Obesity status transitions across the elementary years: Use of Markov chain modeling
Technology Transfer Automated Retrieval System (TEKTRAN)
Overweight and obesity status transition probabilities using first-order Markov transition models applied to elementary school children were assessed. Complete longitudinal data across eleven assessments were available from 1,494 elementary school children (from 7,599 students in 41 out of 45 school...
Evidence Feed Forward Hidden Markov Models for Visual Human Action Classification (Preprint)
2011-04-12
Features for 3-D Jester Recognition,” Proceedings from IEEE Automatic Face and Gesture Recognition (AFGR), 1996, pp. 157-162. 9. Yu, C., Ballard, D...pp. 1-4, doi:10.1109/ICPR.2008.4761290. 11. Wilson, A., Bobick, A., “Parametric Hidden Markov Models for Gesture Recognition ,” IEEE Transaction on
ERIC Educational Resources Information Center
Kim, Jee-Seon; Bolt, Daniel M.
2007-01-01
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…
Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model
ERIC Educational Resources Information Center
de la Torre, Jimmy; Stark, Stephen; Chernyshenko, Oleksandr S.
2006-01-01
The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response…
Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms
ERIC Educational Resources Information Center
Anderson, John R.
2012-01-01
Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…
Wavelet-based SAR images despeckling using joint hidden Markov model
NASA Astrophysics Data System (ADS)
Li, Qiaoliang; Wang, Guoyou; Liu, Jianguo; Chen, Shaobo
2007-11-01
In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, we propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). The Expectation Maximization algorithm is used to estimate hyperparameters and specify the mixture model. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.
Treatment-based Markov chain models clarify mechanisms of invasion in an invaded grassland community
Nelis, Lisa Castillo; Wootton, J. Timothy
2010-01-01
What are the relative roles of mechanisms underlying plant responses in grassland communities invaded by both plants and mammals? What type of community can we expect in the future given current or novel conditions? We address these questions by comparing Markov chain community models among treatments from a field experiment on invasive species on Robinson Crusoe Island, Chile. Because of seed dispersal, grazing and disturbance, we predicted that the exotic European rabbit (Oryctolagus cuniculus) facilitates epizoochorous exotic plants (plants with seeds that stick to the skin an animal) at the expense of native plants. To test our hypothesis, we crossed rabbit exclosure treatments with disturbance treatments, and sampled the plant community in permanent plots over 3 years. We then estimated Markov chain model transition probabilities and found significant differences among treatments. As hypothesized, this modelling revealed that exotic plants survive better in disturbed areas, while natives prefer no rabbits or disturbance. Surprisingly, rabbits negatively affect epizoochorous plants. Markov chain dynamics indicate that an overall replacement of native plants by exotic plants is underway. Using a treatment-based approach to multi-species Markov chain models allowed us to examine the changes in the importance of mechanisms in response to experimental impacts on communities. PMID:19864293
Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models
Shepard, Samuel S.; McSweeny, Andrew; Serpen, Gursel; Fedorov, Alexei
2012-01-01
Messenger RNA sequences possess specific nucleotide patterns distinguishing them from non-coding genomic sequences. In this study, we explore the utilization of modified Markov models to analyze sequences up to 44 bp, far beyond the 8-bp limit of conventional Markov models, for exon/intron discrimination. In order to analyze nucleotide sequences of this length, their information content is first reduced by conversion into shorter binary patterns via the application of numerous abstraction schemes. After the conversion of genomic sequences to binary strings, homogenous Markov models trained on the binary sequences are used to discriminate between exons and introns. We term this approach the Binary Abstraction Markov Model (BAMM). High-quality abstraction schemes for exon/intron discrimination are selected using optimization algorithms on supercomputers. The best MM classifiers are then combined using support vector machines into a single classifier. With this approach, over 95% classification accuracy is achieved without taking reading frame into account. With further development, the BAMM approach can be applied to sequences lacking the genetic code such as ncRNAs and 5′-untranslated regions. PMID:22344692
Avian life history profiles for use in the Markov chain nest productivity model (MCnest)
The Markov Chain nest productivity model, or MCnest, quantitatively estimates the effects of pesticides or other toxic chemicals on annual reproductive success of avian species (Bennett and Etterson 2013, Etterson and Bennett 2013). The Basic Version of MCnest was developed as a...
Kirsch, Florian
2015-01-01
Diabetes is the most expensive chronic disease; therefore, disease management programs (DMPs) were introduced. The aim of this review is to determine whether Markov models are adequate to evaluate the cost-effectiveness of complex interventions such as DMPs. Additionally, the quality of the models was evaluated using Philips and Caro quality appraisals. The five reviewed models incorporated the DMP into the model differently: two models integrated effectiveness rates derived from one clinical trial/meta-analysis and three models combined interventions from different sources into a DMP. The results range from cost savings and a QALY gain to costs of US$85,087 per QALY. The Spearman's rank coefficient assesses no correlation between the quality appraisals. With restrictions to the data selection process, Markov models are adequate to determine the cost-effectiveness of DMPs; however, to allow prioritization of medical services, more flexibility in the models is necessary to enable the evaluation of single additional interventions.
Identifying gene regulatory network rewiring using latent differential graphical models
Tian, Dechao; Gu, Quanquan; Ma, Jian
2016-01-01
Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions. PMID:27378774
A two-strain TB model with multiple latent stages.
Jabbari, Azizeh; Castillo-Chavez, Carlos; Nazari, Fereshteh; Song, Baojun; Kheiri, Hossein
2016-08-01
A two-strain tuberculosis (TB) transmission model incorporating antibiotic-generated TB resistant strains and long and variable waiting periods within the latently infected class is introduced. The mathematical analysis is carried out when the waiting periods are modeled via parametrically friendly gamma distributions, a reasonable alternative to the use of exponential distributed waiting periods or to integral equations involving ``arbitrary'' distributions. The model supports a globally-asymptotically stable disease-free equilibrium when the reproduction number is less than one and an endemic equilibriums, shown to be locally asymptotically stable, or l.a.s., whenever the basic reproduction number is greater than one. Conditions for the existence and maintenance of TB resistant strains are discussed. The possibility of exogenous re-infection is added and shown to be capable of supporting multiple equilibria; a situation that increases the challenges faced by public health experts. We show that exogenous re-infection may help established resilient communities of actively-TB infected individuals that cannot be eliminated using approaches based exclusively on the ability to bring the control reproductive number just below 1.
2012-03-01
58 3. Third Category of Data of HN Officers ...........................................59 B. CONCEPTUAL FRAMEWORK/DESCRIPTION OF MARKOV...performance (Barrick & Mount,1991). Factor 4, Emotional Adjustment, is often labeled by its opposite, Neuroticism , which is the tendency to be...category of officers comprises the pool. B. CONCEPTUAL FRAMEWORK/DESCRIPTION OF MARKOV-CHAIN MODELS Military organizations, as has been stated
Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items
ERIC Educational Resources Information Center
Huang, Hung-Yu; Wang, Wen-Chung
2013-01-01
Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian…
ERIC Educational Resources Information Center
Blanchard, Rebecca D.; Konold, Timothy R.
2011-01-01
This paper introduces latent growth modeling (LGM) as a statistical method for analyzing change over time in latent, or unobserved, variables, with particular emphasis of the application of this method in higher education research. While increasingly popular in other areas of education research and despite a wealth of publicly-available datasets…
Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change
ERIC Educational Resources Information Center
Hertzog, Christopher; von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman
2008-01-01
We evaluated the statistical power of single-indicator latent growth curve models to detect individual differences in change (variances of latent slopes) as a function of sample size, number of longitudinal measurement occasions, and growth curve reliability. We recommend the 2 degree-of-freedom generalized test assessing loss of fit when both…
ERIC Educational Resources Information Center
Holt, Judith A.; Macready, George B.
When latent class parameters are estimated, maximum likelihood and Pearson chi-square statistics can be derived for assessing the fit of the model to the data. This study used simulated data to compare these two statistics, and is based on mixtures of latent binomial distributions, using data generated from five dichotomous manifest variables.…
Sample Sizes for Two-Group Second-Order Latent Growth Curve Models
ERIC Educational Resources Information Center
Wanstrom, Linda
2009-01-01
Second-order latent growth curve models (S. C. Duncan & Duncan, 1996; McArdle, 1988) can be used to study group differences in change in latent constructs. We give exact formulas for the covariance matrix of the parameter estimates and an algebraic expression for the estimation of slope differences. Formulas for calculations of the required sample…
A Comparison of Latent Growth Models for Constructs Measured by Multiple Items
ERIC Educational Resources Information Center
Leite, Walter L.
2007-01-01
Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…
A Comparative Study of Item Selection Methods Utilizing Latent Trait Theretic Models and Concepts.
Latent trait theory provides the practitioner interested in developing tests with a far more powerful method of item selection than that provided by...follows: 1) Provide some background on information curves for items and tests; 2) Develop several item selection methods and using a typical item pool...into three ability categories. Keywords: Latent trait theory, Psychological tests, Mathematical models, Aptitude tests. (SDW)
ERIC Educational Resources Information Center
Kelava, Augustin; Werner, Christina S.; Schermelleh-Engel, Karin; Moosbrugger, Helfried; Zapf, Dieter; Ma, Yue; Cham, Heining; Aiken, Leona S.; West, Stephen G.
2011-01-01
Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x[superscript 2] [subscript 1], x[subscript 1]x[subscript 4]) to serve as indicators of each nonlinear latent construct. These approaches require the use of…
Latent Class Analysis with Distal Outcomes: A Flexible Model-Based Approach
ERIC Educational Resources Information Center
Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C.
2013-01-01
Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with…
ERIC Educational Resources Information Center
Lee, Sik-Yum; Song, Xin-Yuan; Tang, Nian-Sheng
2007-01-01
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a…
The Biplot as a diagnostic tool of local dependence in latent class models. A medical application.
Sepúlveda, R; Vicente-Villardón, J L; Galindo, M P
2008-05-20
Latent class models (LCMs) can be used to assess diagnostic test performance when no reference test (a gold standard) is available, considering two latent classes representing disease or non-disease status. One of the basic assumptions in such models is that of local or conditional independence: all indicator variables (tests) are statistically independent within each latent class. However, in practice this assumption is often violated; hence, the two-LCM fits the data poorly. In this paper, we propose the use of Biplot methods to identify the conditional dependence between pairs of manifest variables within each latent class. Additionally, we propose incorporating such dependence in the corresponding latent class using the log-linear formulation of the model.
ERIC Educational Resources Information Center
Wang, Zijian Gerald
2012-01-01
A latent class signal detection (SDT) model was recently introduced as an alternative to traditional item response theory (IRT) methods in the analysis of constructed response data. This class of models can be represented as restricted latent class models and differ from the IRT approach in the way the latent construct is conceptualized. One…
A Markov Chain Model for evaluating the effectiveness of randomized surveillance procedures
Edmunds, T.A.
1994-01-01
A Markov Chain Model has been developed to evaluate the effectiveness of randomized surveillance procedures. The model is applicable for surveillance systems that monitor a collection of assets by randomly selecting and inspecting the assets. The model provides an estimate of the detection probability as a function of the amount of time that an adversary would require to steal or sabotage the asset. An interactive computer code has been written to perform the necessary computations.
ERIC Educational Resources Information Center
Li, Dingcheng
2011-01-01
Coreference resolution (CR) and entity relation detection (ERD) aim at finding predefined relations between pairs of entities in text. CR focuses on resolving identity relations while ERD focuses on detecting non-identity relations. Both CR and ERD are important as they can potentially improve other natural language processing (NLP) related tasks…
Modeling individual subtests of the WAIS IV with multiple latent factors.
McFarland, Dennis J
2013-01-01
Performance on a cognitive test can be viewed either as measuring a unitary function or as reflecting the operation of multiple factors. Individual subtests in batteries designed to measure human abilities are commonly modeled as a single latent factor. Several latent factors are then used to model groups of subtests. However these latent factors are not independent as they are related through hierarchical or oblique structures. As a result, the simple structure of subtest performance results in complex latent factors. The present study used structural equation modeling to evaluate several multidimensional models of the Wechsler Adult Intelligence Scales-fourth edition (WAIS-IV) subtests. Multidimensional models of subtest performance provided better model fit as compared to several previously proposed one dimensional models. These multidimensional models also generalized well to new samples of populations differing in age from that used to estimate the model parameters. Overall these results show that models that describe subtests as multidimensional functions of uncorrelated factors provided a better fit to the WAIS-IV correlations than models that describe subtests as one dimensional functions of correlated factors. There appears to be a trade-off in modeling subtests as one dimensional and modeling with homogeneous latent traits. More consideration should be given to models that include multiple uncorrelated latent factors as determinants of the performance on a given subtest. These results support the view that performance on any given cognitive test is potentially the result of multiple factors. Simple structure may be too simple.
Modeling Individual Subtests of the WAIS IV with Multiple Latent Factors
McFarland, Dennis J.
2013-01-01
Performance on a cognitive test can be viewed either as measuring a unitary function or as reflecting the operation of multiple factors. Individual subtests in batteries designed to measure human abilities are commonly modeled as a single latent factor. Several latent factors are then used to model groups of subtests. However these latent factors are not independent as they are related through hierarchical or oblique structures. As a result, the simple structure of subtest performance results in complex latent factors. The present study used structural equation modeling to evaluate several multidimensional models of the Wechsler Adult Intelligence Scales- fourth edition (WAIS-IV) subtests. Multidimensional models of subtest performance provided better model fit as compared to several previously proposed one dimensional models. These multidimensional models also generalized well to new samples of populations differing in age from that used to estimate the model parameters. Overall these results show that models that describe subtests as multidimensional functions of uncorrelated factors provided a better fit to the WAIS-IV correlations than models that describe subtests as one dimensional functions of correlated factors. There appears to be a trade-off in modeling subtests as one dimensional and modeling with homogeneous latent traits. More consideration should be given to models that include multiple uncorrelated latent factors as determinants of the performance on a given subtest. These results support the view that performance on any given cognitive test is potentially the result of multiple factors. Simple structure may be too simple. PMID:24058643
NASA Astrophysics Data System (ADS)
Winkelmann, Stefanie; Schütte, Christof
2016-12-01
Accurate modeling and numerical simulation of reaction kinetics is a topic of steady interest. We consider the spatiotemporal chemical master equation (ST-CME) as a model for stochastic reaction-diffusion systems that exhibit properties of metastability. The space of motion is decomposed into metastable compartments, and diffusive motion is approximated by jumps between these compartments. Treating these jumps as first-order reactions, simulation of the resulting stochastic system is possible by the Gillespie method. We present the theory of Markov state models as a theoretical foundation of this intuitive approach. By means of Markov state modeling, both the number and shape of compartments and the transition rates between them can be determined. We consider the ST-CME for two reaction-diffusion systems and compare it to more detailed models. Moreover, a rigorous formal justification of the ST-CME by Galerkin projection methods is presented.
Golightly, Andrew; Wilkinson, Darren J.
2011-01-01
Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583
NASA Astrophysics Data System (ADS)
Ababaei, Behnam; Sohrabi, Teymour; Mirzaei, Farhad
2014-10-01
Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.
Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W
2016-12-06
Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM(®) 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.
Yu, Elaine; Monaco, James P; Tomaszewski, John; Shih, Natalie; Feldman, Michael; Madabhushi, Anant
2011-01-01
In this paper we present a system for detecting regions of carcinoma of the prostate (CaP) in H&E stained radical prostatectomy specimens using the color fractal dimension. Color textural information is known to be a valuable characteristic to distinguish CaP from benign tissue. In addition to color information, we know that cancer tends to form contiguous regions. Our system leverages the color staining information of histology as well as spatial dependencies. The color and textural information is first captured using color fractal dimension. To incorporate spatial dependencies, we combine the probability map constructed via color fractal dimension with a novel Markov prior called the Probabilistic Pairwise Markov Model (PPMM). To demonstrate the capability of this CaP detection system, we applied the algorithm to 27 radical prostatectomy specimens from 10 patients. A per pixel evaluation was conducted with ground truth provided by an expert pathologist using only the color fractal feature first, yielding an area under the receiver operator characteristic curve (AUC) curve of 0.790. In conjunction with a Markov prior, the resultant color fractal dimension + Markov random field (MRF) classifier yielded an AUC of 0.831.
Markov models and the ensemble Kalman filter for estimation of sorption rates.
Vugrin, Eric D.; McKenna, Sean Andrew; Vugrin, Kay White
2007-09-01
Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.
A non-homogeneous Markov model for phased-mission reliability analysis
NASA Technical Reports Server (NTRS)
Smotherman, Mark; Zemoudeh, Kay
1989-01-01
Three assumptions of Markov modeling for reliability of phased-mission systems that limit flexibility of representation are identified. The proposed generalization has the ability to represent state-dependent behavior, handle phases of random duration using globally time-dependent distributions of phase change time, and model globally time-dependent failure and repair rates. The approach is based on a single nonhomogeneous Markov model in which the concept of state transition is extended to include globally time-dependent phase changes. Phase change times are specified using nonoverlapping distributions with probability distribution functions that are zero outside assigned time intervals; the time intervals are ordered according to the phases. A comparison between a numerical solution of the model and simulation demonstrates that the numerical solution can be several times faster than simulation.
A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models.
Aguirre-Hernández, R; Farewell, V T
2002-07-15
Markov regression models describe the way in which a categorical response variable changes over time for subjects with different explanatory variables. Frequently it is difficult to measure the response variable on equally spaced discrete time intervals. Here we propose a Pearson-type goodness-of-fit test for stationary Markov regression models fitted to panel data. A parametric bootstrap algorithm is used to study the distribution of the test statistic. The proposed technique is applied to examine the fit of a Markov regression model used to identify markers for disease progression in psoriatic arthritis.
Korostil, Igor A; Peters, Gareth W; Cornebise, Julien; Regan, David G
2013-05-20
A Bayesian statistical model and estimation methodology based on forward projection adaptive Markov chain Monte Carlo is developed in order to perform the calibration of a high-dimensional nonlinear system of ordinary differential equations representing an epidemic model for human papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual-activity group. Developing this model and a means to calibrate it efficiently is relevant because HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix on the basis of a formulation first suggested by Garnett and Anderson, frequently used to model sexually transmitted infections. In particular, we consider a stochastic mixing matrix framework that allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities that are a priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters as well as unknown sexual mixing matrix parameters related to assortativity. Finally, we explore the ability of an extension to the class of adaptive Markov chain Monte Carlo algorithms to incorporate a forward projection strategy for the ordinary differential equation state trajectories. Efficient exploration of the Bayesian posterior distribution developed for the ordinary differential equation parameters provides a challenge for any Markov chain sampling methodology, hence the interest in adaptive Markov chain methods. We conclude with simulation studies on synthetic and recent actual data.
ERIC Educational Resources Information Center
Schermelleh-Engel, Karin; Keith, Nina; Moosbrugger, Helfried; Hodapp, Volker
2004-01-01
An extension of latent state-trait (LST) theory to hierarchical LST models is presented. In hierarchical LST models, the covariances between 2 or more latent traits are explained by a general 3rd-order factor, and the covariances between latent state residuals pertaining to different traits measured on the same measurement occasion are explained…
Hidden Markov models and other machine learning approaches in computational molecular biology
Baldi, P.
1995-12-31
This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.
STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning
Kappel, David; Nessler, Bernhard; Maass, Wolfgang
2014-01-01
In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787
NASA Astrophysics Data System (ADS)
Choi, Yeontaek; Sim, Seungwoo; Lee, Sang-Hee
2014-06-01
The locomotion behavior of Caenorhabditis elegans has been extensively studied to understand the relationship between the changes in the organism's neural activity and the biomechanics. However, so far, we have not yet achieved the understanding. This is because the worm complicatedly responds to the environmental factors, especially chemical stress. Constructing a mathematical model is helpful for the understanding the locomotion behavior in various surrounding conditions. In the present study, we built three hidden Markov models for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a polluted environment by formaldehyde, toluene, and benzene (0.1 ppm and 0.5 ppm for each case). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity entropy and classified into five groups by using the self-organizing map. To evaluate and establish the hidden Markov models, we compared correlation coefficients between the simulated behavior (i.e. temporal pattern sequence) generated by the models and the actual crawling behavior. The comparison showed that the hidden Markov models are successful to characterize the crawling behavior. In addition, we briefly discussed the possibility of using the models together with the entropy to develop bio-monitoring systems for determining water quality.
A reward semi-Markov process with memory for wind speed modeling
NASA Astrophysics Data System (ADS)
Petroni, F.; D'Amico, G.; Prattico, F.
2012-04-01
The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first
The Application of Wavelet-Domain Hidden Markov Tree Model in Diabetic Retinal Image Denoising.
Cui, Dong; Liu, Minmin; Hu, Lei; Liu, Keju; Guo, Yongxin; Jiao, Qing
2015-01-01
The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and correlation of fundus angiographic images' wavelet coefficients among scales. Based on the construction of the fundus angiographic images Hidden Markov Tree Models and Gaussian Mixture Models, this paper applied expectation-maximum algorithm to estimate the wavelet coefficients of original fundus angiographic images and the Bayesian estimation to achieve the goal of fundus angiographic images denoising. As is shown in the experimental result, compared with the other algorithms as mean filter and median filter, this method effectively improved the peak signal to noise ratio of fundus angiographic images after denoising and preserved the details of vascular edge in fundus angiographic images.
A path-independent method for barrier option pricing in hidden Markov models
NASA Astrophysics Data System (ADS)
Rashidi Ranjbar, Hedieh; Seifi, Abbas
2015-12-01
This paper presents a method for barrier option pricing under a Black-Scholes model with Markov switching. We extend the option pricing method of Buffington and Elliott to price continuously monitored barrier options under a Black-Scholes model with regime switching. We use a regime switching random Esscher transform in order to determine an equivalent martingale pricing measure, and then solve the resulting multidimensional integral for pricing barrier options. We have calculated prices for down-and-out call options under a two-state hidden Markov model using two different Monte-Carlo simulation approaches and the proposed method. A comparison of the results shows that our method is faster than Monte-Carlo simulation methods.
Transition probability estimates for non-Markov multi-state models.
Titman, Andrew C
2015-12-01
Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al., (1991) (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients.
Cook, Richard J; Yi, Grace Y; Lee, Ker-Ai; Gladman, Dafna D
2004-06-01
Clustered progressive chronic disease processes arise when interest lies in modeling damage in paired organ systems (e.g., kidneys, eyes), in diseases manifest in different organ systems, or in systemic conditions for which damage may occur in several locations of the body. Multistate Markov models have considerable appeal for modeling damage in such settings, particularly when patients are only under intermittent observation. Generalizations are necessary, however, to deal with the fact that processes within subjects may not be independent. We describe a conditional Markov model in which the clustering in processes within subjects is addressed by the use of multiplicative random effects for each transition intensity. The random effects for the different transition intensities may be correlated within subjects, but are assumed to be independent for different subjects. We apply the mixed Markov model to a motivating data set of patients with psoriatic arthritis, and characterize the progressive course of damage in joints of the hand. A generalization to accommodate a subpopulation of "stayers" and extensions which facilitate regression are indicated and illustrated.
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.
2016-01-01
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571
A Comment On Birnbaum's Three-Parameter Logistic Model In The Latent Trait Theory
ERIC Educational Resources Information Center
Samejima, Fumiko
1973-01-01
The three-parameter logistic model by Birnbaum for the multiple-choice item in the latent trait theory is considered with respect to the item response information function and the unique maximum condition. (Editor/RK)
Abstract: Sample Size Planning for Latent Curve Models.
Lai, Keke
2011-11-30
When designing a study that uses structural equation modeling (SEM), an important task is to decide an appropriate sample size. Historically, this task is approached from the power analytic perspective, where the goal is to obtain sufficient power to reject a false null hypothesis. However, hypothesis testing only tells if a population effect is zero and fails to address the question about the population effect size. Moreover, significance tests in the SEM context often reject the null hypothesis too easily, and therefore the problem in practice is having too much power instead of not enough power. An alternative means to infer the population effect is forming confidence intervals (CIs). A CI is more informative than hypothesis testing because a CI provides a range of plausible values for the population effect size of interest. Given the close relationship between CI and sample size, the sample size for an SEM study can be planned with the goal to obtain sufficiently narrow CIs for the population model parameters of interest. Latent curve models (LCMs) is an application of SEM with mean structure to studying change over time. The sample size planning method for LCM from the CI perspective is based on maximum likelihood and expected information matrix. Given a sample, to form a CI for the model parameter of interest in LCM, it requires the sample covariance matrix S, sample mean vector [Formula: see text], and sample size N. Therefore, the width (w) of the resulting CI can be considered a function of S, [Formula: see text], and N. Inverting the CI formation process gives the sample size planning process. The inverted process requires a proxy for the population covariance matrix Σ, population mean vector μ, and the desired width ω as input, and it returns N as output. The specification of the input information for sample size planning needs to be performed based on a systematic literature review. In the context of covariance structure analysis, Lai and Kelley
Model Criticism of Bayesian Networks with Latent Variables.
ERIC Educational Resources Information Center
Williamson, David M.; Mislevy, Robert J.; Almond, Russell G.
This study investigated statistical methods for identifying errors in Bayesian networks (BN) with latent variables, as found in intelligent cognitive assessments. BN, commonly used in artificial intelligence systems, are promising mechanisms for scoring constructed-response examinations. The success of an intelligent assessment or tutoring system…
Multilevel Latent Class Analysis: Parametric and Nonparametric Models
ERIC Educational Resources Information Center
Finch, W. Holmes; French, Brian F.
2014-01-01
Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…
Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant
NASA Astrophysics Data System (ADS)
Aggarwal, Anil Kr.; Kumar, Sanjeev; Singh, Vikram; Garg, Tarun Kr.
2015-12-01
This paper deals with the Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant. This system was modeled using Markov birth-death process with the assumption that the failure and repair rates of each subsystem follow exponential distribution. The first-order Chapman-Kolmogorov differential equations are developed with the use of mnemonic rule and these equations are solved with Runga-Kutta fourth-order method. The long-run availability, reliability and mean time between failures are computed for various choices of failure and repair rates of subsystems of the system. The findings of the paper are discussed with the plant personnel to adopt and practice suitable maintenance policies/strategies to enhance the performance of the urea synthesis system of the fertilizer plant.
Modeling and computing of stock index forecasting based on neural network and Markov chain.
Dai, Yonghui; Han, Dongmei; Dai, Weihui
2014-01-01
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.
Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.
Wang, Xinlei; Zang, Miao; Xiao, Guanghua
2013-06-15
Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method.
Availability analysis of subsea blowout preventer using Markov model considering demand rate
NASA Astrophysics Data System (ADS)
Kim, Sunghee; Chung, Soyeon; Yang, Youngsoon
2014-12-01
Availabilities of subsea Blowout Preventers (BOP) in the Gulf of Mexico Outer Continental Shelf (GoM OCS) is investigated using a Markov method. An updated β factor model by SINTEF is used for common-cause failures in multiple redundant systems. Coefficient values of failure rates for the Markov model are derived using the β factor model of the PDS (reliability of computer-based safety systems, Norwegian acronym) method. The blind shear ram preventer system of the subsea BOP components considers a demand rate to reflect reality more. Markov models considering the demand rate for one or two components are introduced. Two data sets are compared at the GoM OCS. The results show that three or four pipe ram preventers give similar availabilities, but redundant blind shear ram preventers or annular preventers enhance the availability of the subsea BOP. Also control systems (PODs) and connectors are contributable components to improve the availability of the subsea BOPs based on sensitivity analysis.
Semiparametric time-to-event modeling in the presence of a latent progression event.
Rice, John D; Tsodikov, Alex
2016-08-24
In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method; its use in practice is demonstrated in the analysis of a prostate cancer data set.
An Introduction to Markov Modeling: Concepts and Uses
NASA Technical Reports Server (NTRS)
Boyd, Mark A.; Lau, Sonie (Technical Monitor)
1998-01-01
Kharkov modeling is a modeling technique that is widely useful for dependability analysis of complex fault tolerant systems. It is very flexible in the type of systems and system behavior it can model. It is not, however, the most appropriate modeling technique for every modeling situation. The first task in obtaining a reliability or availability estimate for a system is selecting which modeling technique is most appropriate to the situation at hand. A person performing a dependability analysis must confront the question: is Kharkov modeling most appropriate to the system under consideration, or should another technique be used instead? The need to answer this gives rise to other more basic questions regarding Kharkov modeling: what are the capabilities and limitations of Kharkov modeling as a modeling technique? How does it relate to other modeling techniques? What kind of system behavior can it model? What kinds of software tools are available for performing dependability analyses with Kharkov modeling techniques? These questions and others will be addressed in this tutorial.
Introduction to Hidden Markov Models and Their Applications to Classification Problems
1999-09-01
34 model (the model is called ergotic when backward transitions are allowed, as illustrated in Fig 2.2). 5 We define the probability aij as the...Figure 2.2 A Markov Process (Chain), ergotic model 7 For the discrete-time case, a system governed by known or predictable dynamics can be modeled...Total number of positions=N*T :Possible movement / Most likely path Figure 2.4a Trellis Diagram for T=4, and N=3, ergotic model 17 tl t2 t3 t4 $1
Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions
Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G. E-mail: gerhard.hummer@biophys.mpg.de; Hummer, Gerhard E-mail: gerhard.hummer@biophys.mpg.de
2014-09-21
Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.
Sun, Jianan; Chen, Yunxiao; Liu, Jingchen; Ying, Zhiliang; Xin, Tao
2016-12-01
We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an [Formula: see text] penalty term to the log-likelihood. The computation is carried out by the expectation-maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.
Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods
ERIC Educational Resources Information Center
Rabe-Hesketh, Sophia; Skrondal, Anders
2007-01-01
Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models,…
Bildirici, Melike; Ersin, Özgür
2014-01-01
The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200
Bildirici, Melike; Ersin, Özgür
2014-01-01
The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.
A Markov Model for Assessing the Reliability of a Digital Feedwater Control System
Chu,T.L.; Yue, M.; Martinez-Guridi, G.; Lehner, J.
2009-02-11
A Markov approach has been selected to represent and quantify the reliability model of a digital feedwater control system (DFWCS). The system state, i.e., whether a system fails or not, is determined by the status of the components that can be characterized by component failure modes. Starting from the system state that has no component failure, possible transitions out of it are all failure modes of all components in the system. Each additional component failure mode will formulate a different system state that may or may not be a system failure state. The Markov transition diagram is developed by strictly following the sequences of component failures (i.e., failure sequences) because the different orders of the same set of failures may affect the system in completely different ways. The formulation and quantification of the Markov model, together with the proposed FMEA (Failure Modes and Effects Analysis) approach, and the development of the supporting automated FMEA tool are considered the three major elements of a generic conceptual framework under which the reliability of digital systems can be assessed.
Modeling anomalous radar propagation using first-order two-state Markov chains
NASA Astrophysics Data System (ADS)
Haddad, B.; Adane, A.; Mesnard, F.; Sauvageot, H.
In this paper, it is shown that radar echoes due to anomalous propagations (AP) can be modeled using Markov chains. For this purpose, images obtained in southwestern France by means of an S-band meteorological radar recorded every 5 min in 1996 were considered. The daily mean surfaces of AP appearing in these images are sorted into two states and their variations are then represented by a binary random variable. The Markov transition matrix, the 1-day-lag autocorrelation coefficient as well as the long-term probability of having each of both states are calculated on a monthly basis. The same kind of modeling was also applied to the rainfall observed in the radar dataset under study. The first-order two-state Markov chains are then found to fit the daily variations of either AP or rainfall areas very well. For each month of the year, the surfaces filled by both types of echo follow similar stochastic distributions, but their autocorrelation coefficient is different. Hence, it is suggested that this coefficient is a discriminant factor which could be used, among other criteria, to improve the identification of AP in radar images.
A Stable Clock Error Model Using Coupled First and Second Order Gauss-Markov Processes
NASA Technical Reports Server (NTRS)
Carpenter, Russell; Lee, Taesul
2008-01-01
Long data outages may occur in applications of global navigation satellite system technology to orbit determination for missions that spend significant fractions of their orbits above the navigation satellite constellation(s). Current clock error models based on the random walk idealization may not be suitable in these circumstances, since the covariance of the clock errors may become large enough to overflow flight computer arithmetic. A model that is stable, but which approximates the existing models over short time horizons is desirable. A coupled first- and second-order Gauss-Markov process is such a model.
An approximation formula for a class of Markov reliability models
NASA Technical Reports Server (NTRS)
White, A. L.
1984-01-01
A way of considering a small but often used class of reliability model and approximating algebraically the systems reliability is shown. The models considered are appropriate for redundant reconfigurable digital control systems that operate for a short period of time without maintenance, and for such systems the method gives a formula in terms of component fault rates, system recovery rates, and system operating time.
Comparing Latent Structures of the Grade of Membership, Rasch, and Latent Class Models
ERIC Educational Resources Information Center
Erosheva, Elena A.
2005-01-01
This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other…
A Dynamic Model for Induced Reactivation of Latent Virus
2005-12-04
average 1 hour per response , including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed... RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI...Among these, virus- bacteria interactions have been described, including reactivation of latent virus by metabolic end products of anaerobic bacteria. A
Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia
2012-01-01
Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.
D. L. Kelly
2007-06-01
Markov chain Monte Carlo (MCMC) techniques represent an extremely flexible and powerful approach to Bayesian modeling. This work illustrates the application of such techniques to time-dependent reliability of components with repair. The WinBUGS package is used to illustrate, via examples, how Bayesian techniques can be used for parametric statistical modeling of time-dependent component reliability. Additionally, the crucial, but often overlooked subject of model validation is discussed, and summary statistics for judging the model’s ability to replicate the observed data are developed, based on the posterior predictive distribution for the parameters of interest.
Deremigio, Hilary; Kemper, Peter; Lamar, M Drew; Smith, Gregory D
2008-01-01
Mathematical models of calcium release sites derived from Markov chain models of intracellular calcium channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic calcium excitability (i.e., calcium puffs and sparks). We present a Kronecker structured representation for calcium release site models and perform benchmark stationary distribution calculations using numerical iterative solution techniques that leverage this structure. In this context we find multi-level methods and certain preconditioned projection methods superior to simple Gauss-Seidel type iterations. Response measures such as the number of channels in a particular state converge more quickly using these numerical iterative methods than occupation measures calculated via Monte Carlo simulation.
Synthetic bounds for semi-Markov reliability models
NASA Technical Reports Server (NTRS)
White, A. L.
1985-01-01
Upper and lower bounds are derived for the probability of failure for a class of highly reliable process control computers. The bounds are synthetic in the sense that the descriptions of component failure and system recovery are assumed to be obtained from different sources. The reliability model is constructed under the assumption that the processes are independent.
Markov Models for the Simulation of Cancer Screening Process
NASA Astrophysics Data System (ADS)
Chiorean, Ioana; Lupşa, Liana; NeamÅ£iu, Luciana
2008-09-01
The paper presents some mathematical models which optimize, from medico-economics point of view, the natural evolution of the cervical lesions and their evolution when the woman attends a cervical cancer screening program. A new index, MEI, constructed by using vectorial optimization, is given.
Speaker Recognition by Hidden Markov Models and Neural Networks
1996-12-01
of the model itself, a single - layer perceptron is added to perform neural post-processing. The second solution is the novel application of an...the Equal Error Rate from 3.38% to 1.56%. In addition, a new method of cohort selection is implemented based on the structure of the single layer perceptron . Feasibility
Besstremyannaya, Galina
2011-09-01
The paper explores the link between managerial performance and cost efficiency of 617 Japanese general local public hospitals in 1999-2007. Treating managerial performance as unobservable heterogeneity, the paper employs a panel data stochastic cost frontier model with latent classes. Financial parameters associated with better managerial performance are found to be positively significant in explaining the probability of belonging to the more efficient latent class. The analysis of latent class membership was consistent with the conjecture that unobservable technological heterogeneity reflected in the existence of the latent classes is related to managerial performance. The findings may support the cause for raising efficiency of Japanese local public hospitals by enhancing the quality of management.
A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology.
Klopfstein, Seraina; Vilhelmsen, Lars; Ronquist, Fredrik
2015-11-01
Directional evolution has played an important role in shaping the morphological, ecological, and molecular diversity of life. However, standard substitution models assume stationarity of the evolutionary process over the time scale examined, thus impeding the study of directionality. Here we explore a simple, nonstationary model of evolution for discrete data, which assumes that the state frequencies at the root differ from the equilibrium frequencies of the homogeneous evolutionary process along the rest of the tree (i.e., the process is nonstationary, nonreversible, but homogeneous). Within this framework, we develop a Bayesian approach for testing directional versus stationary evolution using a reversible-jump algorithm. Simulations show that when only data from extant taxa are available, the success in inferring directionality is strongly dependent on the evolutionary rate, the shape of the tree, the relative branch lengths, and the number of taxa. Given suitable evolutionary rates (0.1-0.5 expected substitutions between root and tips), accounting for directionality improves tree inference and often allows correct rooting of the tree without the use of an outgroup. As an empirical test, we apply our method to study directional evolution in hymenopteran morphology. We focus on three character systems: wing veins, muscles, and sclerites. We find strong support for a trend toward loss of wing veins and muscles, while stationarity cannot be ruled out for sclerites. Adding fossil and time information in a total-evidence dating approach, we show that accounting for directionality results in more precise estimates not only of the ancestral state at the root of the tree, but also of the divergence times. Our model relaxes the assumption of stationarity and reversibility by adding a minimum of additional parameters, and is thus well suited to studying the nature of the evolutionary process in data sets of limited size, such as morphology and ecology.
A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology
Klopfstein, Seraina; Vilhelmsen, Lars; Ronquist, Fredrik
2015-01-01
Directional evolution has played an important role in shaping the morphological, ecological, and molecular diversity of life. However, standard substitution models assume stationarity of the evolutionary process over the time scale examined, thus impeding the study of directionality. Here we explore a simple, nonstationary model of evolution for discrete data, which assumes that the state frequencies at the root differ from the equilibrium frequencies of the homogeneous evolutionary process along the rest of the tree (i.e., the process is nonstationary, nonreversible, but homogeneous). Within this framework, we develop a Bayesian approach for testing directional versus stationary evolution using a reversible-jump algorithm. Simulations show that when only data from extant taxa are available, the success in inferring directionality is strongly dependent on the evolutionary rate, the shape of the tree, the relative branch lengths, and the number of taxa. Given suitable evolutionary rates (0.1–0.5 expected substitutions between root and tips), accounting for directionality improves tree inference and often allows correct rooting of the tree without the use of an outgroup. As an empirical test, we apply our method to study directional evolution in hymenopteran morphology. We focus on three character systems: wing veins, muscles, and sclerites. We find strong support for a trend toward loss of wing veins and muscles, while stationarity cannot be ruled out for sclerites. Adding fossil and time information in a total-evidence dating approach, we show that accounting for directionality results in more precise estimates not only of the ancestral state at the root of the tree, but also of the divergence times. Our model relaxes the assumption of stationarity and reversibility by adding a minimum of additional parameters, and is thus well suited to studying the nature of the evolutionary process in data sets of limited size, such as morphology and ecology. PMID:26272507
Nonlinear Stochastic Markov Processes and Modeling Uncertainty in Populations
2011-07-06
growth rate g(x) = rx ( 1− x κ ) and the general tran- sition rates g(x, t) = (a0(t) − a1(t) ln x)x of which the standard Gompertz growth rates g(x) = r...probabilistic formulation (5.4) and the stochastic formulation (5.5), which nicely illustrates our earlier theoretical results. Example 5.3 ( Gompertz ...stochastic version of the generalized Gompertz model ẋ = (a0(t)− a1(t) lnx)x, which has been extensively used in biological and medical research to describe
On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models
ERIC Educational Resources Information Center
Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus
2015-01-01
Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…
Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models
ERIC Educational Resources Information Center
Kim, Minjung; Kwok, Oi-Man; Yoon, Myeongsun; Willson, Victor; Lai, Mark H. C.
2016-01-01
This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting…
The Exploration of the Relationship between Guessing and Latent Ability in IRT Models
ERIC Educational Resources Information Center
Gao, Song
2011-01-01
This study explored the relationship between successful guessing and latent ability in IRT models. A new IRT model was developed with a guessing function integrating probability of guessing an item correctly with the examinee's ability and the item parameters. The conventional 3PL IRT model was compared with the new 2PL-Guessing model on…
NASA Astrophysics Data System (ADS)
Dong, Ming; He, David
2007-07-01
Diagnostics and prognostics are two important aspects in a condition-based maintenance (CBM) program. However, these two tasks are often separately performed. For example, data might be collected and analysed separately for diagnosis and prognosis. This practice increases the cost and reduces the efficiency of CBM and may affect the accuracy of the diagnostic and prognostic results. In this paper, a statistical modelling methodology for performing both diagnosis and prognosis in a unified framework is presented. The methodology is developed based on segmental hidden semi-Markov models (HSMMs). An HSMM is a hidden Markov model (HMM) with temporal structures. Unlike HMM, an HSMM does not follow the unrealistic Markov chain assumption and therefore provides more powerful modelling and analysis capability for real problems. In addition, an HSMM allows modelling the time duration of the hidden states and therefore is capable of prognosis. To facilitate the computation in the proposed HSMM-based diagnostics and prognostics, new forward-backward variables are defined and a modified forward-backward algorithm is developed. The existing state duration estimation methods are inefficient because they require a huge storage and computational load. Therefore, a new approach is proposed for training HSMMs in which state duration probabilities are estimated on the lattice (or trellis) of observations and states. The model parameters are estimated through the modified forward-backward training algorithm. The estimated state duration probability distributions combined with state-changing point detection can be used to predict the useful remaining life of a system. The evaluation of the proposed methodology was carried out through a real world application: health monitoring of hydraulic pumps. In the tests, the recognition rates for all states are greater than 96%. For each individual pump, the recognition rate is increased by 29.3% in comparison with HMMs. Because of the temporal
Availability Control for Means of Transport in Decisive Semi-Markov Models of Exploitation Process
NASA Astrophysics Data System (ADS)
Migawa, Klaudiusz
2012-12-01
The issues presented in this research paper refer to problems connected with the control process for exploitation implemented in the complex systems of exploitation for technical objects. The article presents the description of the method concerning the control availability for technical objects (means of transport) on the basis of the mathematical model of the exploitation process with the implementation of the decisive processes by semi-Markov. The presented method means focused on the preparing the decisive for the exploitation process for technical objects (semi-Markov model) and after that specifying the best control strategy (optimal strategy) from among possible decisive variants in accordance with the approved criterion (criteria) of the activity evaluation of the system of exploitation for technical objects. In the presented method specifying the optimal strategy for control availability in the technical objects means a choice of a sequence of control decisions made in individual states of modelled exploitation process for which the function being a criterion of evaluation reaches the extreme value. In order to choose the optimal control strategy the implementation of the genetic algorithm was chosen. The opinions were presented on the example of the exploitation process of the means of transport implemented in the real system of the bus municipal transport. The model of the exploitation process for the means of transports was prepared on the basis of the results implemented in the real transport system. The mathematical model of the exploitation process was built taking into consideration the fact that the model of the process constitutes the homogenous semi-Markov process.
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Alignment of multiple proteins with an ensemble of Hidden Markov Models
Song, Yinglei; Qu, Junfeng; Hura, Gurdeep S.
2011-01-01
In this paper, we developed a new method that progressively construct and update a set of alignments by adding sequences in certain order to each of the existing alignments. Each of the existing alignments is modelled with a profile Hidden Markov Model (HMM) and an added sequence is aligned to each of these profile HMMs. We introduced an integer parameter for the number of profile HMMs. The profile HMMs are then updated based on the alignments with leading scores. Our experiments on BaliBASE showed that our approach could efficiently explore the alignment space and significantly improve the alignment accuracy. PMID:20376922
NASA Astrophysics Data System (ADS)
Bozhalkina, Yana; Timofeeva, Galina
2016-12-01
Mathematical model of loan portfolio in the form of a controlled Markov chain with discrete time is considered. It is assumed that coefficients of migration matrix depend on corrective actions and external factors. Corrective actions include process of receiving applications, interaction with existing solvent and insolvent clients. External factors are macroeconomic indicators, such as inflation and unemployment rates, exchange rates, consumer price indices, etc. Changes in corrective actions adjust the intensity of transitions in the migration matrix. The mathematical model for forecasting the credit portfolio structure taking into account a cumulative impact of internal and external changes is obtained.
Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors
NASA Astrophysics Data System (ADS)
Zhang, Yingjun; Liu, Wen; Yang, Xuefeng; Xing, Shengwei
2015-02-01
In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.
Markov chain Monte Carlo methods for state-space models with point process observations.
Yuan, Ke; Girolami, Mark; Niranjan, Mahesan
2012-06-01
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.
What drives Hong Kong's residential property market—A Markov switching present value model
NASA Astrophysics Data System (ADS)
Xiao, Qin
2007-09-01
The property market of Hong Kong is one of the most volatile in the world. This study attempts to investigate the proposition that the Hong Kong residential market is only driven by fundamentals. The investigation is based on a Markov switching present value model, which explicitly accounts for a rational speculative bubble. The estimates show that not only does the model capture the asymmetric market responses to information and noise, but it also gives evidence on investor heterogeneity. The study also finds that the influence of the rational bubble is statistically significant.
Frank, T D
2002-07-01
Using the method of steps, we describe stochastic processes with delays in terms of Markov diffusion processes. Thus, multivariate Langevin equations and Fokker-Planck equations are derived for stochastic delay differential equations. Natural, periodic, and reflective boundary conditions are discussed. Both Ito and Stratonovich calculus are used. In particular, our Fokker-Planck approach recovers the generalized delay Fokker-Planck equation proposed by Guillouzic et al. The results obtained are applied to a model for population growth: the Gompertz model with delay and multiplicative white noise.
A Linear Regression and Markov Chain Model for the Arabian Horse Registry
1993-04-01
as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to
An analytical study of various telecomminication networks using markov models
NASA Astrophysics Data System (ADS)
Ramakrishnan, M.; Jayamani, E.; Ezhumalai, P.
2015-04-01
The main aim of this paper is to examine issues relating to the performance of various Telecommunication networks, and applied queuing theory for better design and improved efficiency. Firstly, giving an analytical study of queues deals with quantifying the phenomenon of waiting lines using representative measures of performances, such as average queue length (on average number of customers in the queue), average waiting time in queue (on average time to wait) and average facility utilization (proportion of time the service facility is in use). In the second, using Matlab simulator, summarizes the finding of the investigations, from which and where we obtain results and describing methodology for a) compare the waiting time and average number of messages in the queue in M/M/1 and M/M/2 queues b) Compare the performance of M/M/1 and M/D/1 queues and study the effect of increasing the number of servers on the blocking probability M/M/k/k queue model.
A nonhomogeneous hidden markov model for gene mapping based on next-generation sequencing data.
Ghavidel, Fatemeh Zamanzad; Claesen, Jürgen; Burzykowski, Tomasz
2015-02-01
The analysis of polygenetic characteristics for mapping quantitative trait loci (QTL) remains an important challenge. QTL analysis requires two or more strains of organisms that differ substantially in the (poly-)genetic trait of interest, resulting in a heterozygous offspring. The offspring with the trait of interest is selected and subsequently screened for molecular markers such as single-nucleotide polymorphisms (SNPs) with next-generation sequencing. Gene mapping relies on the co-segregation between genes and/or markers. Genes and/or markers that are linked to a QTL influencing the trait will segregate more frequently with this locus. For each identified marker, observed mismatch frequencies between the reads of the offspring and the parental reference strains can be modeled by a multinomial distribution with the probabilities depending on the state of an underlying, unobserved Markov process. The states indicate whether the SNP is located in a (vicinity of a) QTL or not. Consequently, genomic loci associated with the QTL can be discovered by analyzing hidden states along the genome. The aforementioned hidden Markov model assumes that the identified SNPs are equally distributed along the chromosome and does not take the distance between neighboring SNPs into account. The distance between the neighboring SNPs could influence the chance of co-segregation between genes and markers. To address this issue, we propose a nonhomogeneous hidden Markov model with a transition matrix that depends on a set of distance-varying observed covariates. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast.
Controlling influenza disease: Comparison between discrete time Markov chain and deterministic model
NASA Astrophysics Data System (ADS)
Novkaniza, F.; Ivana, Aldila, D.
2016-04-01
Mathematical model of respiratory diseases spread with Discrete Time Markov Chain (DTMC) and deterministic approach for constant total population size are analyzed and compared in this article. Intervention of medical treatment and use of medical mask included in to the model as a constant parameter to controlling influenza spreads. Equilibrium points and basic reproductive ratio as the endemic criteria and it level set depend on some variable are given analytically and numerically as a results from deterministic model analysis. Assuming total of human population is constant from deterministic model, number of infected people also analyzed with Discrete Time Markov Chain (DTMC) model. Since Δt → 0, we could assume that total number of infected people might change only from i to i + 1, i - 1, or i. Approximation probability of an outbreak with gambler's ruin problem will be presented. We find that no matter value of basic reproductive ℛ0, either its larger than one or smaller than one, number of infection will always tends to 0 for t → ∞. Some numerical simulation to compare between deterministic and DTMC approach is given to give a better interpretation and a better understanding about the models results.
Markovian and Non-Markovian Protein Sequence Evolution: Aggregated Markov Process Models
Kosiol, Carolin; Goldman, Nick
2011-01-01
Over the years, there have been claims that evolution proceeds according to systematically different processes over different timescales and that protein evolution behaves in a non-Markovian manner. On the other hand, Markov models are fundamental to many applications in evolutionary studies. Apparent non-Markovian or time-dependent behavior has been attributed to influence of the genetic code at short timescales and dominance of physicochemical properties of the amino acids at long timescales. However, any long time period is simply the accumulation of many short time periods, and it remains unclear why evolution should appear to act systematically differently across the range of timescales studied. We show that the observed time-dependent behavior can be explained qualitatively by modeling protein sequence evolution as an aggregated Markov process (AMP): a time-homogeneous Markovian substitution model observed only at the level of the amino acids encoded by the protein-coding DNA sequence. The study of AMPs sheds new light on the relationship between amino acid-level and codon-level models of sequence evolution, and our results suggest that protein evolution should be modeled at the codon level rather than using amino acid substitution models. PMID:21718704
Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.
Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C
2014-12-01
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase
Study of behavior and determination of customer lifetime value(CLV) using Markov chain model
Permana, Dony; Indratno, Sapto Wahyu; Pasaribu, Udjianna S.
2014-03-24
Customer Lifetime Value or CLV is a restriction on interactive marketing to help a company in arranging financial for the marketing of new customer acquisition and customer retention. Additionally CLV can be able to segment customers for financial arrangements. Stochastic models for the fairly new CLV used a Markov chain. In this model customer retention probability and new customer acquisition probability play an important role. This model is originally introduced by Pfeifer and Carraway in 2000 [1]. They introduced several CLV models, one of them only involves customer and former customer. In this paper we expand the model by adding the assumption of the transition from former customer to customer. In the proposed model, the CLV value is higher than the CLV value obtained by Pfeifer and Caraway model. But our model still requires a longer convergence time.
Study of behavior and determination of customer lifetime value(CLV) using Markov chain model
NASA Astrophysics Data System (ADS)
Permana, Dony; Indratno, Sapto Wahyu; Pasaribu, Udjianna S.
2014-03-01
Customer Lifetime Value or CLV is a restriction on interactive marketing to help a company in arranging financial for the marketing of new customer acquisition and customer retention. Additionally CLV can be able to segment customers for financial arrangements. Stochastic models for the fairly new CLV used a Markov chain. In this model customer retention probability and new customer acquisition probability play an important role. This model is originally introduced by Pfeifer and Carraway in 2000 [1]. They introduced several CLV models, one of them only involves customer and former customer. In this paper we expand the model by adding the assumption of the transition from former customer to customer. In the proposed model, the CLV value is higher than the CLV value obtained by Pfeifer and Caraway model. But our model still requires a longer convergence time.
Interlocking directorates in Irish companies using a latent space model for bipartite networks.
Friel, Nial; Rastelli, Riccardo; Wyse, Jason; Raftery, Adrian E
2016-06-14
We analyze the temporal bipartite network of the leading Irish companies and their directors from 2003 to 2013, encompassing the end of the Celtic Tiger boom and the ensuing financial crisis in 2008. We focus on the evolution of company interlocks, whereby a company director simultaneously sits on two or more boards. We develop a statistical model for this dataset by embedding the positions of companies and directors in a latent space. The temporal evolution of the network is modeled through three levels of Markovian dependence: one on the model parameters, one on the companies' latent positions, and one on the edges themselves. The model is estimated using Bayesian inference. Our analysis reveals that the level of interlocking, as measured by a contraction of the latent space, increased before and during the crisis, reaching a peak in 2009, and has generally stabilized since then.
Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution
Djordjevic, Ivan B.
2015-01-01
Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually
Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution.
Djordjevic, Ivan B
2015-08-24
Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually
Multilayer Markov Random Field models for change detection in optical remote sensing images
NASA Astrophysics Data System (ADS)
Benedek, Csaba; Shadaydeh, Maha; Kato, Zoltan; Szirányi, Tamás; Zerubia, Josiane
2015-09-01
In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.
NASA Astrophysics Data System (ADS)
Durán, E.
2012-04-01
The interbeded sandstones, siltstones and shale layers within the stratigraphic units of the Oficina Formation were stochastically characterized. The units within the Oritupano field are modeled using the information from 12 wells and a post-stack 3-D seismic cube. The Markov Chain algorithm was successful at maintaining the proportion of lithotypes of the columns in the study area. Different transition probability matrixes are evaluated by changing the length of the sequences represented in the transition matrix and how this choice of length affects ciclicity and the genetic relations between lithotypes. The Gibbs algorithm, using small sequences as building blocks for modeling, kept the main stratigraphic succession according to the geology. Although the modeled stratigraphy depends strongly on initial conditions, the use of longer sequences in the substitution helps not to overweight the transition counts from one lithotype to the same in the main diagonal of the probability matrix of the Markov Chain in the Gibbs algorithm. A methodology based on the phase spectrum of the seismic trace for tying the modeled sequences with the seismic data is evaluated and discussed. The results point to the phase spectrum as an alternate way to cross-correlate synthetic seismograms with the seismic trace in favor of the well known amplitude correlation. Finally, a map of net sand over the study area is generated from the modeled columns and compared with previous stratigraphic and facies analysis at the levels of interest.
Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences
Siebert, Matthias; Söding, Johannes
2016-01-01
Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k − 1 act as priors for those of order k. This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly (P = 1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26–101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs. PMID:27288444
Comparison of the kinetics of different Markov models for ligand binding under varying conditions
Martini, Johannes W. R.; Habeck, Michael
2015-03-07
We recently derived a Markov model for macromolecular ligand binding dynamics from few physical assumptions and showed that its stationary distribution is the grand canonical ensemble [J. W. R. Martini, M. Habeck, and M. Schlather, J. Math. Chem. 52, 665 (2014)]. The transition probabilities of the proposed Markov process define a particular Glauber dynamics and have some similarity to the Metropolis-Hastings algorithm. Here, we illustrate that this model is the stochastic analog of (pseudo) rate equations and the corresponding system of differential equations. Moreover, it can be viewed as a limiting case of general stochastic simulations of chemical kinetics. Thus, the model links stochastic and deterministic approaches as well as kinetics and equilibrium described by the grand canonical ensemble. We demonstrate that the family of transition matrices of our model, parameterized by temperature and ligand activity, generates ligand binding kinetics that respond to changes in these parameters in a qualitatively similar way as experimentally observed kinetics. In contrast, neither the Metropolis-Hastings algorithm nor the Glauber heat bath reflects changes in the external conditions correctly. Both converge rapidly to the stationary distribution, which is advantageous when the major interest is in the equilibrium state, but fail to describe the kinetics of ligand binding realistically. To simulate cellular processes that involve the reversible stochastic binding of multiple factors, our pseudo rate equation model should therefore be preferred to the Metropolis-Hastings algorithm and the Glauber heat bath, if the stationary distribution is not of only interest.
Short-term droughts forecast using Markov chain model in Victoria, Australia
NASA Astrophysics Data System (ADS)
Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.
2016-04-01
A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.
Hidden Markov model analysis of force/torque information in telemanipulation
NASA Technical Reports Server (NTRS)
Hannaford, Blake; Lee, Paul
1991-01-01
A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.
ERIC Educational Resources Information Center
Yang, Ji Seung
2012-01-01
Nonlinear multilevel latent variable modeling has been suggested as an alternative to traditional hierarchical linear modeling to more properly handle measurement error and sampling error issues in contextual effects modeling. However, a nonlinear multilevel latent variable model requires significant computational effort because the estimation…
Markov Jump-Linear Performance Models for Recoverable Flight Control Computers
NASA Technical Reports Server (NTRS)
Zhang, Hong; Gray, W. Steven; Gonzalez, Oscar R.
2004-01-01
Single event upsets in digital flight control hardware induced by atmospheric neutrons can reduce system performance and possibly introduce a safety hazard. One method currently under investigation to help mitigate the effects of these upsets is NASA Langley s Recoverable Computer System. In this paper, a Markov jump-linear model is developed for a recoverable flight control system, which will be validated using data from future experiments with simulated and real neutron environments. The method of tracking error analysis and the plan for the experiments are also described.
Multi-state Markov model for disability: A case of Malaysia Social Security (SOCSO)
NASA Astrophysics Data System (ADS)
Samsuddin, Shamshimah; Ismail, Noriszura
2016-06-01
Studies of SOCSO's contributor outcomes like disability are usually restricted to a single outcome. In this respect, the study has focused on the approach of multi-state Markov model for estimating the transition probabilities among SOCSO's contributor in Malaysia between states: work, temporary disability, permanent disability and death at yearly intervals on age, gender, year and disability category; ignoring duration and past disability experience which is not consider of how or when someone arrived in that category. These outcomes represent different states which depend on health status among the workers.
Upper and lower bounds for semi-Markov reliability models of reconfigurable systems
NASA Technical Reports Server (NTRS)
White, A. L.
1984-01-01
This paper determines the information required about system recovery to compute the reliability of a class of reconfigurable systems. Upper and lower bounds are derived for these systems. The class consists of those systems that satisfy five assumptions: the components fail independently at a low constant rate, fault occurrence and system reconfiguration are independent processes, the reliability model is semi-Markov, the recovery functions which describe system configuration have small means and variances, and the system is well designed. The bounds are easy to compute, and examples are included.
Monaco, James P.; Tomaszewski, John E.; Feldman, Michael D.; Hagemann, Ian; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant
2010-01-01
In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80K×70K pixels — far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: 1) detecting cancerous regions and 2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2×1.75 cm2) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8 µm per pixel). This motivates the following algorithm: Step 1) glands are segmented, Step 2) the segmented glands are classified as malignant or benign, and Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate
Theory of Distribution Estimation of Hyperparameters in Markov Random Field Models
NASA Astrophysics Data System (ADS)
Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato
2016-06-01
We investigated the performance of distribution estimation of hyperparameters in Markov random field models proposed by Nakanishi-Ohno et al., http://doi.org/10.1088/1751-8113/47/4/045001, J. Phys. A 47, 045001 (2014) when used to evaluate the confidence of data. We analytically calculated the configurational average, with respect to data, of the negative logarithm of the posterior distribution, which is called free energy based on an analogy with statistical mechanics. This configurational average of free energy shrinks as the amount of data increases. Our results theoretically confirm the numerical results from that previous study.
Uddin, Md; Lee, J J; Kim, T S
2008-01-01
In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.
Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain.
Shauly, Michal; Rabinowitz, Gad; Gilutz, Harel; Parmet, Yisrael
2011-10-01
The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.
Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)
NASA Astrophysics Data System (ADS)
Can, Ceren Eda; Ergun, Gul; Gokceoglu, Candan
2014-09-01
Earthquakes are one of the most important natural hazards to be evaluated carefully in engineering projects, due to the severely damaging effects on human-life and human-made structures. The hazard of an earthquake is defined by several approaches and consequently earthquake parameters such as peak ground acceleration occurring on the focused area can be determined. In an earthquake prone area, the identification of the seismicity patterns is an important task to assess the seismic activities and evaluate the risk of damage and loss along with an earthquake occurrence. As a powerful and flexible framework to characterize the temporal seismicity changes and reveal unexpected patterns, Poisson hidden Markov model provides a better understanding of the nature of earthquakes. In this paper, Poisson hidden Markov model is used to predict the earthquake hazard in Bilecik (NW Turkey) as a result of its important geographic location. Bilecik is in close proximity to the North Anatolian Fault Zone and situated between Ankara and Istanbul, the two biggest cites of Turkey. Consequently, there are major highways, railroads and many engineering structures are being constructed in this area. The annual frequencies of earthquakes occurred within a radius of 100 km area centered on Bilecik, from January 1900 to December 2012, with magnitudes ( M) at least 4.0 are modeled by using Poisson-HMM. The hazards for the next 35 years from 2013 to 2047 around the area are obtained from the model by forecasting the annual frequencies of M ≥ 4 earthquakes.
Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)
NASA Astrophysics Data System (ADS)
Can, Ceren; Ergun, Gul; Gokceoglu, Candan
2014-09-01
Earthquakes are one of the most important natural hazards to be evaluated carefully in engineering projects, due to the severely damaging effects on human-life and human-made structures. The hazard of an earthquake is defined by several approaches and consequently earthquake parameters such as peak ground acceleration occurring on the focused area can be determined. In an earthquake prone area, the identification of the seismicity patterns is an important task to assess the seismic activities and evaluate the risk of damage and loss along with an earthquake occurrence. As a powerful and flexible framework to characterize the temporal seismicity changes and reveal unexpected patterns, Poisson hidden Markov model provides a better understanding of the nature of earthquakes. In this paper, Poisson hidden Markov model is used to predict the earthquake hazard in Bilecik (NW Turkey) as a result of its important geographic location. Bilecik is in close proximity to the North Anatolian Fault Zone and situated between Ankara and Istanbul, the two biggest cites of Turkey. Consequently, there are major highways, railroads and many engineering structures are being constructed in this area. The annual frequencies of earthquakes occurred within a radius of 100 km area centered on Bilecik, from January 1900 to December 2012, with magnitudes (M) at least 4.0 are modeled by using Poisson-HMM. The hazards for the next 35 years from 2013 to 2047 around the area are obtained from the model by forecasting the annual frequencies of M ≥ 4 earthquakes.
Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig
2011-01-01
Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763
Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S N; Nelson, Trisalyn A; Nathoo, Farouk; Stephen, Craig
2011-01-01
Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines.
Lu, Ji; Pan, Junhao; Zhang, Qiang; Dubé, Laurette; Ip, Edward H.
2015-01-01
With intensively collected longitudinal data, recent advances in Experience Sampling Method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well-equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet & Dube, 2011) that observed 160 participants’ food consumption and momentary emotions six times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal healthiness decision, the proposed Reciprocal Markov Model (RMM) can accommodate both hidden (“general” emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent to the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters. PMID:26717120
Hidden Markov models and neural networks for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic
1994-01-01
Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.
ERIC Educational Resources Information Center
Quinn, Jamie M.; Wagner, Richard K.; Petscher, Yaacov; Lopez, Danielle
2015-01-01
The present study followed a sample of first-grade (N = 316, M[subscript age] = 7.05 at first test) through fourth-grade students to evaluate dynamic developmental relations between vocabulary knowledge and reading comprehension. Using latent change score modeling, competing models were fit to the repeated measurements of vocabulary knowledge and…
Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko
2004-01-01
A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…
Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes
ERIC Educational Resources Information Center
Leite, Walter L.; Stapleton, Laura M.
2011-01-01
In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…
Group Comparisons in the Presence of Missing Data Using Latent Variable Modeling Techniques
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2010-01-01
A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…
ERIC Educational Resources Information Center
Story, Roger E.
1996-01-01
Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…
Divorce and Child Behavior Problems: Applying Latent Change Score Models to Life Event Data
ERIC Educational Resources Information Center
Malone, Patrick S.; Lansford, Jennifer E.; Castellino, Domini R.; Berlin, Lisa J.; Dodge, Kenneth A.; Bates, John E.; Pettit, Gregory S.
2004-01-01
Effects of parents' divorce on children's adjustment have been studied extensively. This article applies new advances in trajectory modeling to the problem of disentangling the effects of divorce on children's adjustment from related factors such as the child's age at the time of divorce and the child's gender. Latent change score models were used…
A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates
ERIC Educational Resources Information Center
Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.
2012-01-01
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…
ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
A Note on the Specification of Error Structures in Latent Interaction Models
ERIC Educational Resources Information Center
Mao, Xiulin; Harring, Jeffrey R.; Hancock, Gregory R.
2015-01-01
Latent interaction models have motivated a great deal of methodological research, mainly in the area of estimating such models. Product-indicator methods have been shown to be competitive with other methods of estimation in terms of parameter bias and standard error accuracy, and their continued popularity in empirical studies is due, in part, to…
Xie, Jun; Kim, Nak-Kyeong
2005-09-01
Statistical methods have been developed for finding local patterns, also called motifs, in multiple protein sequences. The aligned segments may imply functional or structural core regions. However, the existing methods often have difficulties in aligning multiple proteins when sequence residue identities are low (e.g., less than 25%). In this article, we develop a Bayesian model and Markov chain Monte Carlo (MCMC) methods for identifying subtle motifs in protein sequences. Specifically, a motif is defined not only in terms of specific sites characterized by amino acid frequency vectors, but also as a combination of secondary characteristics such as hydrophobicity, polarity, etc. Markov chain Monte Carlo methods are proposed to search for a motif pattern with high posterior probability under the new model. A special MCMC algorithm is developed, involving transitions between state spaces of different dimensions. The proposed methods were supported by a simulated study. It was then tested by two real datasets, including a group of helix-turn-helix proteins, and one set from the CATH Protein Structure Classification Database. Statistical comparisons showed that the new approach worked better than a typical Gibbs sampling approach which is based only on an amino acid model.
Syed, Sheyum; Müllner, Fiona E; Selvin, Paul R; Sigworth, Fred J
2010-12-01
Unbiased interpretation of noisy single molecular motor recordings remains a challenging task. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. It improves on currently available Markov-model based techniques by allowing for arbitrary distributions of step sizes, and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise. In this article, we extend the VS-HMM framework for better performance with experimental data. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and accounts for random baseline drifts. Further, as an extension, maximum a posteriori estimation is provided. When used as a blind step detector, the VSI-HMM outperforms conventional step detectors. The fidelity of the VSI-HMM is tested with simulations and is applied to in vitro myosin V data where a small 10 nm population of steps is identified. It is also applied to an in vivo recording of melanosome motion, where strong evidence is found for repeated, bidirectional steps smaller than 8 nm in size, implying that multiple motors simultaneously carry the cargo.
Markov-switching model for nonstationary runoff conditioned on El Niño information
NASA Astrophysics Data System (ADS)
Gelati, E.; Madsen, H.; Rosbjerg, D.
2010-02-01
We define a Markov-modulated autoregressive model with exogenous input (MARX) to generate runoff scenarios using climatic information. Runoff parameterization is assumed to be conditioned on a hidden climate state following a Markov chain, where state transition probabilities are functions of the climatic input. MARX allows stochastic modeling of nonstationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We apply MARX to inflow time series of the Daule Peripa reservoir (Ecuador). El Niño-Southern Oscillation (ENSO) information is used to condition runoff parameterization. Among the investigated ENSO indexes, the NINO 1+2 sea surface temperature anomalies and the trans-Niño index perform best as predictors. In the perspective of reservoir optimization at various time scales, MARX produces realistic long-term scenarios and short-term forecasts, especially when intense El Niño events occur. Low predictive ability is found for negative runoff anomalies, as no climatic index correlating properly with negative inflow anomalies has yet been identified.
NASA Astrophysics Data System (ADS)
Mottaghy, Darius; Rath, Volker
2006-01-01
In cold regions the thermal regime is strongly affected by freezing or melting processes, consuming or releasing large amounts of latent heat. This changes enthalpy by orders of magnitude. We present a numerical approach for the implementation of these effects into a 3-D finite-difference heat transport model. The latent heat effect can be handled by substituting an apparent heat capacity for the volumetric heat capacity of unfrozen soil in the heat transfer equation. The model is verified by the analytical solution of the heat transport equation including phase change. We found significant deviations of temperature profiles when applying the latent heat effect on forward calculations of deep temperature logs. Ground surface temperature histories derived from synthetic data and field data from NE Poland underline the importance of considering freezing processes. In spite of its limitations, the proposed method is appropriate for the study of long-period climatic changes.
ERIC Educational Resources Information Center
Yang, Chongming; Nay, Sandra; Hoyle, Rick H.
2010-01-01
Lengthy scales or testlets pose certain challenges for structural equation modeling (SEM) if all the items are included as indicators of a latent construct. Three general approaches to modeling lengthy scales in SEM (parceling, latent scoring, and shortening) have been reviewed and evaluated. A hypothetical population model is simulated containing…
Dodds, Michael G; Vicini, Paolo
2004-09-01
Advances in computer hardware and the associated computer-intensive algorithms made feasible by these advances [like Markov chain Monte Carlo (MCMC) data analysis techniques] have made possible the application of hierarchical full Bayesian methods in analyzing pharmacokinetic and pharmacodynamic (PK-PD) data sets that are multivariate in nature. Pharmacokinetic data analysis in particular has been one area that has seized upon this technology to refine estimates of drug parameters from sparse data gathered in a large, highly variable population of patients. A drawback in this type of analysis is that it is difficult to quantitatively assess convergence of the Markov chains to a target distribution, and thus, it is sometimes difficult to assess the reliability of estimates gained from this procedure. Another complicating factor is that, although the application of MCMC methods to population PK-PD problems has been facilitated by new software designed for the PK-PD domain (specifically PKBUGS), experts in PK-PD may not have the necessary experience with MCMC methods to detect and understand problems with model convergence. The objective of this work is to provide an example of a set of diagnostics useful to investigators, by analyzing in detail three convergence criteria (namely the Raftery and Lewis, Geweke, and Heidelberger and Welch methods) on a simulated problem and with a rule of thumb of 10,000 chain elements in the Markov chain. We used two publicly available software packages to assess convergence of MCMC parameter estimates; the first performs Bayesian parameter estimation (PKBUGS/WinBUGS), and the second is focused on posterior analysis of estimates (BOA). The main message that seems to emerge is that accurately estimating confidence regions for the parameters of interest is more demanding than estimating the parameter means. Together, these tools provide numerical means by which an investigator can establish confidence in convergence and thus in the
A nonparametric vs. latent class model of general practitioner utilization: evidence from Canada.
McLeod, Logan
2011-12-01
Predicting health care utilization is the foundation of many health economics analyses, such as calculating risk-adjustment capitation payments or measuring equity in health care utilization. The most common econometric models of physician utilization are parametric count data models, since the most common metric of physician utilization is the number of physician visits. This paper makes two distinct contributions to the literature analyzing GP utilization: (i) it is the first to use a nonparametric kernel conditional density estimator to model GP utilization and compare the predicted utilization with that from a latent class negative binomial model; and (ii) it uses panel data to control for the potential endogeneity between self-reported health status and the number of GP visits. The goodness-of-fit results show the kernel conditional density estimator provides a better fit to the observed distribution of GP visits than the latent class negative binomial model. There are some meaningful differences in how the predicted conditional mean number of GP visits changes with a change in an individual's characteristics, called the incremental effect (IE), between the kernel conditional density estimator and the latent class negative binomial model. The most notable differences are observed in the right tail of the distribution where the IEs from the latent class negative binomial model are up to 190 times the magnitude of the IEs from the kernel conditional density estimator.
Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.
Yasmin, Shamsunnahar; Eluru, Naveen
2016-10-01
The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation.
NASA Astrophysics Data System (ADS)
Numazawa, Satoshi; Smith, Roger
2011-10-01
Classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The scheme is then used to determine transitions that can be applied in a lattice-based kinetic Monte Carlo (KMC) atomistic simulation model. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multidimensional Boolean valued functions in three-dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology-dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. Excellent agreement with observed experimental results is obtained.
On metastability and Markov state models for non-stationary molecular dynamics
NASA Astrophysics Data System (ADS)
Koltai, Péter; Ciccotti, Giovanni; Schütte, Christof
2016-11-01
Unlike for systems in equilibrium, a straightforward definition of a metastable set in the non-stationary, non-equilibrium case may only be given case-by-case—and therefore it is not directly useful any more, in particular in cases where the slowest relaxation time scales are comparable to the time scales at which the external field driving the system varies. We generalize the concept of metastability by relying on the theory of coherent sets. A pair of sets A and B is called coherent with respect to the time interval [t1, t2] if (a) most of the trajectories starting in A at t1 end up in B at t2 and (b) most of the trajectories arriving in B at t2 actually started from A at t1. Based on this definition, we can show how to compute coherent sets and then derive finite-time non-stationary Markov state models. We illustrate this concept and its main differences to equilibrium Markov state modeling on simple, one-dimensional examples.
Mitavskiy, Boris; Cannings, Chris
2009-01-01
The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.
NASA Astrophysics Data System (ADS)
Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea; Montalto, Placido; Patanè, Domenico; Privitera, Eugenio
2016-07-01
From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.
Numazawa, Satoshi; Smith, Roger
2011-10-01
Classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The scheme is then used to determine transitions that can be applied in a lattice-based kinetic Monte Carlo (KMC) atomistic simulation model. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multidimensional Boolean valued functions in three-dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology-dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. Excellent agreement with observed experimental results is obtained.
Zaric, Gregory S
2003-01-01
Many factors related to the spread and progression of diseases vary throughout a population. This heterogeneity is frequently ignored in cost-effectiveness analyses by using average or representative values or by considering multiple risk groups. The author explores the impact that such simplifying assumptions may have on the results and interpretation of cost-effectiveness analyses when Markov models are used to calculate the costs and health impact of interventions. A discrete-time Markov model for a disease is defined, and 5 potential interventions are considered. Health benefits, costs, and incremental cost-effectiveness ratios are calculated for each intervention. It is assumed that the population is heterogeneous with respect to the probability of becoming sick. Ignoring this heterogeneity may lead to optimistic or pessimistic estimates of cost-effectiveness ratios, depending on the intervention and, in some cases, the parameter values. Implications are discussed of this finding on the use of league tables and on comparisons of cost-effectiveness ratios versus commonly accepted threshold values.
Sun, Shuying; Yu, Xiaoqing
2016-03-01
DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher's exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher's exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.
Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models.
Trifa, Vlad M; Kirschel, Alexander N G; Taylor, Charles E; Vallejo, Edgar E
2008-04-01
Behavioral and ecological studies would benefit from the ability to automatically identify species from acoustic recordings. The work presented in this article explores the ability of hidden Markov models to distinguish songs from five species of antbirds that share the same territory in a rainforest environment in Mexico. When only clean recordings were used, species recognition was nearly perfect, 99.5%. With noisy recordings, performance was lower but generally exceeding 90%. Besides the quality of the recordings, performance has been found to be heavily influenced by a multitude of factors, such as the size of the training set, the feature extraction method used, and number of states in the Markov model. In general, training with noisier data also improved recognition in test recordings, because of an increased ability to generalize. Considerations for improving performance, including beamforming with sensor arrays and design of preprocessing methods particularly suited for bird songs, are discussed. Combining sensor network technology with effective event detection and species identification algorithms will enable observation of species interactions at a spatial and temporal resolution that is simply impossible with current tools. Analysis of animal behavior through real-time tracking of individuals and recording of large amounts of data with embedded devices in remote locations is thus a realistic goal.
Discovery of multiple hidden allosteric sites by combining Markov state models and experiments.
Bowman, Gregory R; Bolin, Eric R; Hart, Kathryn M; Maguire, Brendan C; Marqusee, Susan
2015-03-03
The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets--called hidden allosteric sites--remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond- to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target--TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain
Dai, Yonghui; Han, Dongmei; Dai, Weihui
2014-01-01
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659
NASA Astrophysics Data System (ADS)
Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N.
2010-07-01
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders, we fit hidden Markov models to the time series of the sign of the tick-by-tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a significant majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transaction size distributions of these patches are fat tailed. Long patches are characterized by a large fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly dependent on the local market trend. We also compare the hidden Markov model patches with those obtained with the segmentation method used in Vaglica et al (2008 Phys. Rev. E 77 036110), and we conclude that the former ones can be interpreted as a partition of the latter ones.
Modeling strategic use of human computer interfaces with novel hidden Markov models
Mariano, Laura J.; Poore, Joshua C.; Krum, David M.; Schwartz, Jana L.; Coskren, William D.; Jones, Eric M.
2015-01-01
Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit. PMID
Post processing of optically recognized text via second order hidden Markov model
NASA Astrophysics Data System (ADS)
Poudel, Srijana
In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.
An estimator of the survival function based on the semi-Markov model under dependent censorship.
Lee, Seung-Yeoun; Tsai, Wei-Yann
2005-06-01
Lee and Wolfe (Biometrics vol. 54 pp. 1176-1178, 1998) proposed the two-stage sampling design for testing the assumption of independent censoring, which involves further follow-up of a subset of lost-to-follow-up censored subjects. They also proposed an adjusted estimator for the survivor function for a proportional hazards model under the dependent censoring model. In this paper, a new estimator for the survivor function is proposed for the semi-Markov model under the dependent censorship on the basis of the two-stage sampling data. The consistency and the asymptotic distribution of the proposed estimator are derived. The estimation procedure is illustrated with an example of lung cancer clinical trial and simulation results are reported of the mean squared errors of estimators under a proportional hazards and two different nonproportional hazards models.
Mixture model and Markov random field-based remote sensing image unsupervised clustering method
NASA Astrophysics Data System (ADS)
Hou, Y.; Yang, Y.; Rao, N.; Lun, X.; Lan, J.
2011-03-01
In this paper, a novel method for remote sensing image clustering based on mixture model and Markov random field (MRF) is proposed. A remote sensing image can be considered as Gaussian mixture model. The image clustering result corresponding to the image label field is a MRF. So, the image clustering procedure is transformed to a maximum a posterior (MAP) problem by Bayesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique are introduced into the traditional MRF potential function. The iterative conditional model (ICM) is employed to find the solution of MAP. We use the max entropy criterion to choose the optimal clustering number. In the experiments, the method is compared with the traditional MRF clustering method using ICM and simulated annealing (SA). The results show that this method is better than the traditional MRF model both in noise filtering and miss-classification ratio.
Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models
Riber Marklund, A.; Prakash, V.; Rajan, K.K.
2015-07-01
Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)
Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.
Huang, Hsien-Da; Lee, Tzong-Yi; Tzeng, Shih-Wei; Wu, Li-Cheng; Horng, Jorng-Tzong; Tsou, Ann-Ping; Huang, Kuan-Tsae
2005-07-30
Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.
"Markov at the bat": a model of cognitive processing in baseball batters.
Gray, Rob
2002-11-01
Anecdotal evidence from players and coaches indicates that cognitive processing (e.g., expectations about the upcoming pitch) plays an important role in successful baseball batting, yet this aspect of hitting has not been investigated in detail. The present study provides experimental evidence that prior expectations significantly influence the timing of a baseball swing. A two-state Markov model was used to predict the effects of pitch sequence and pitch count on batting performance. The model is a hitting strategy of switching between expectancy states using a simple set of transition rules. In a simulated batting experiment, the model provided a good fit to the hitting performance of 6 experienced college baseball players, and the estimated model parameters were highly correlated with playing level.
NASA Astrophysics Data System (ADS)
Turner, Sean; Galelli, Stefano; Wilcox, Karen
2015-04-01
Water reservoir systems are often affected by recurring large-scale ocean-atmospheric anomalies, known as teleconnections, that cause prolonged periods of climatological drought. Accurate forecasts of these events -- at lead times in the order of weeks and months -- may enable reservoir operators to take more effective release decisions to improve the performance of their systems. In practice this might mean a more reliable water supply system, a more profitable hydropower plant or a more sustainable environmental release policy. To this end, climate indices, which represent the oscillation of the ocean-atmospheric system, might be gainfully employed within reservoir operating models that adapt the reservoir operation as a function of the climate condition. This study develops a Stochastic Dynamic Programming (SDP) approach that can incorporate climate indices using a Hidden Markov Model. The model simulates the climatic regime as a hidden state following a Markov chain, with the state transitions driven by variation in climatic indices, such as the Southern Oscillation Index. Time series analysis of recorded streamflow data reveals the parameters of separate autoregressive models that describe the inflow to the reservoir under three representative climate states ("normal", "wet", "dry"). These models then define inflow transition probabilities for use in a classic SDP approach. The key advantage of the Hidden Markov Model is that it allows conditioning the operating policy not only on the reservoir storage and the antecedent inflow, but also on the climate condition, thus potentially allowing adaptability to a broader range of climate conditions. In practice, the reservoir operator would effect a water release tailored to a specific climate state based on available teleconnection data and forecasts. The approach is demonstrated on the operation of a realistic, stylised water reservoir with carry-over capacity in South-East Australia. Here teleconnections relating
Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models
Aghagolzadeh, Mehdi; Truccolo, Wilson
2016-01-01
Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling. PMID:26336135
Cher, D J; Miyamoto, J; Lenert, L A
1997-01-01
Most decision models published in the medical literature take a risk-neutral perspective. Under risk neutrality, the utility of a gamble is equivalent to its expected value and the marginal utility of living a given unit of time is the same regardless of when it occurs. Most patients, however, are not risk-neutral. Not only does risk aversion affect decision analyses when tradeoffs between short- and long-term survival are involved, it also affects the interpretation of time-tradeoff measures of health-state utility. The proportional time tradeoff under- or overestimates the disutility of an inferior health state, depending on whether the patient is risk-seeking or risk-averse (it is unbiased if the patient is risk-neutral). The authors review how risk attitude with respect to gambles for survival duration can be incorporated into decision models using the framework of risk-adjusted quality-adjusted life years (RA-QALYs). They present a simple extension of this framework that allows RA-QALYs to be calculated for Markov-process decision models. Using a previously published Markov-process model of surgical vs expectant treatment for benign prostatic hypertrophy (BPH), they show how attitude towards risk affects the expected number of QALYs calculated by the model. In this model, under risk neutrality, surgery was the preferred option. Under mild risk aversion, expectant treatment was the preferred option. Risk attitude is an important aspect of preferences that should be incorporated into decision models where one treatment option has upfront risks of morbidity or mortality.
A second-order Markov process for modeling diffusive motion through spatial discretization.
Sant, Marco; Papadopoulos, George K; Theodorou, Doros N
2008-01-14
A new "mesoscopic" stochastic model has been developed to describe the diffusive behavior of a system of particles at equilibrium. The model is based on discretizing space into slabs by drawing equispaced parallel planes along a coordinate direction. A central role is played by the probability that a particle exits a slab via the face opposite to the one through which it entered (transmission probability), as opposed to exiting via the same face through which it entered (reflection probability). A simple second-order Markov process invoking this probability is developed, leading to an expression for the self-diffusivity, applicable for large slab widths, consistent with a continuous formulation of diffusional motion. This model is validated via molecular dynamics simulations in a bulk system of soft spheres across a wide range of densities.
Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data.
Cooper, Nicola J; Sutton, Alex J; Mugford, Miranda; Abrams, Keith R
2003-01-01
It is well known that the modeling of cost data is often problematic due to the distribution of such data. Commonly observed problems include 1) a strongly right-skewed data distribution and 2) a significant percentage of zero-cost observations. This article demonstrates how a hurdle model can be implemented from a Bayesian perspective by means of Markov Chain Monte Carlo simulation methods using the freely available software WinBUGS. Assessment of model fit is addressed through the implementation of two cross-validation methods. The relative merits of this Bayesian approach compared to the classical equivalent are discussed in detail. To illustrate the methods described, patient-specific non-health-care resource-use data from a prospective longitudinal study and the Norfolk Arthritis Register (NOAR) are utilized for 218 individuals with early inflammatory polyarthritis (IP). The NOAR database also includes information on various patient-level covariates.
Autoregressive hidden Markov models for the early detection of neonatal sepsis.
Stanculescu, Ioan; Williams, Christopher K I; Freer, Yvonne
2014-09-01
Late onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patient's monitoring traces could be used for the early detection of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM). Both learning and inference carefully use domain knowledge to extract the baby's true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit at the Royal Infirmary of Edinburgh.
Brain tumor segmentation in 3D MRIs using an improved Markov random field model
NASA Astrophysics Data System (ADS)
Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza
2011-10-01
Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.
Development of the hidden Markov models based Lithuanian speech recognition system
NASA Astrophysics Data System (ADS)
Ringeliene, Z.; Lipeika, A.
2010-09-01
The paper presents a prototype of the speaker-independent Lithuanian isolated word recognition system. The system is based on the hidden Markov models, a powerful statistical method for modeling speech signals. The prototype system can be used for Lithuanian words recognition investigations and is a good starting point for the development of a more sophisticated recognition system. The system graphical user interface is easy to control. Visualization of the entire recognition process is useful for analyzing of the recognition results. Based on this recognizer, a system for Web browser control by voice was developed. The program, which implements control by voice commands, was integrated in the speech recognition system. The system performance was evaluated by using different sets of acoustic models and vocabularies.
Modeling treatment of ischemic heart disease with partially observable Markov decision processes.
Hauskrecht, M; Fraser, H
1998-01-01
Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of Partially observable Markov decision processes (POMDPs) developed and used in operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In the paper, we show how the POMDP framework could be used to model and solve the problem of the management of patients with ischemic heart disease, and point out modeling advantages of the framework over standard decision formalisms.
ERIC Educational Resources Information Center
Bilir, Mustafa Kuzey
2009-01-01
This study uses a new psychometric model (mixture item response theory-MIMIC model) that simultaneously estimates differential item functioning (DIF) across manifest groups and latent classes. Current DIF detection methods investigate DIF from only one side, either across manifest groups (e.g., gender, ethnicity, etc.), or across latent classes…
Fight deck human-automation mode confusion detection using a generalized fuzzy hidden Markov model
NASA Astrophysics Data System (ADS)
Lyu, Hao Lyu
Due to the need for aviation safety, convenience, and efficiency, the autopilot has been introduced into the cockpit. The fast development of the autopilot has brought great benefits to the aviation industry. On the human side, the flight deck has been designed to be a complex, tightly-coupled, and spatially distributed system. The problem of dysfunctional interaction between the pilot and the automation (human-automation interaction issue) has become more and more visible. Thus, detection of a mismatch between the pilot's expectation and automation's behavior in a timely manner is required. In order to solve this challenging problem, separate modeling of the pilot and the automation is necessary. In this thesis, an intent-based framework is introduced to detect the human-automation interaction issue. Under this framework, the pilot's expectation of the aircraft is modeled by pilot intent while the behavior of the automation system is modeled by automation intent. The mode confusion is detected when the automation intent differs from the pilot intent. The pilot intent is inferred by comparing the target value set by the pilot with the aircraft's current state. Meanwhile, the automation intent is inferred through the Generalized Fuzzy Hidden Markov Model (GFHMM), which is an extension of the classical Hidden Markov Model. The stochastic characteristic of the ``hidden'' intents is considered by introducing fuzzy logic. Different from the previous approaches of inferring automation intent, GFHMM does not require a probabilistic model for certain flight modes as prior knowledge. The parameters of GFHMM (initial fuzzy density of the intent, fuzzy transmission density, and fuzzy emission density) are determined through the flight data by using a machine learning technique, the Fuzzy C-Means clustering algorithm (FCM). Lastly, both the pilot's and automation's intent inference algorithms and the mode confusion detection method are validated through flight data.
What If We Took Our Models Seriously? Estimating Latent Scores in Individuals
ERIC Educational Resources Information Center
Schneider, W. Joel
2013-01-01
Researchers often argue that the structural models of the constructs they study are relevant to clinicians. Unfortunately, few clinicians are able to translate the mathematically precise relationships between latent constructs and observed scores into information that can be usefully applied to individuals. Typically this means that when a new…
ERIC Educational Resources Information Center
Penfield, Randall D.; Bergeron, Jennifer M.
2005-01-01
This article applies a weighted maximum likelihood (WML) latent trait estimator to the generalized partial credit model (GPCM). The relevant equations required to obtain the WML estimator using the Newton-Raphson algorithm are presented, and a simulation study is described that compared the properties of the WML estimator to those of the maximum…
Modeling Individual Differences in Unfolding Preference Data: A Restricted Latent Class Approach.
ERIC Educational Resources Information Center
Bockenholt, Ulf; Bockenholt, Ingo
1990-01-01
A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)
Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2012-01-01
A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…
On the Latent Regression Model of Item Response Theory. Research Report. ETS RR-07-12
ERIC Educational Resources Information Center
Antal, Tamás
2007-01-01
Full account of the latent regression model for the National Assessment of Educational Progress is given. The treatment includes derivation of the EM algorithm, Newton-Raphson method, and the asymptotic standard errors. The paper also features the use of the adaptive Gauss-Hermite numerical integration method as a basic tool to evaluate…
ERIC Educational Resources Information Center
Raykov, Tenko
2011-01-01
Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
Evaluation of Reliability Coefficients for Two-Level Models via Latent Variable Analysis
ERIC Educational Resources Information Center
Raykov, Tenko; Penev, Spiridon
2010-01-01
A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…
Classical Item Analysis Using Latent Variable Modeling: A Note on a Direct Evaluation Procedure
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2011-01-01
A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits…
Evaluation of Scale Reliability for Unidimensional Measures Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko
2009-01-01
This article outlines a readily and widely applicable procedure of reliability evaluation for scales with unidimensional measures. The method is developed within the framework of the popular latent variable modeling methodology, and it accomplishes point, as well as interval, estimation of measuring instrument reliability. The approach can be…
Evaluation of Scale Reliability with Binary Measures Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Dimitrov, Dimiter M.; Asparouhov, Tihomir
2010-01-01
A method for interval estimation of scale reliability with discrete data is outlined. The approach is applicable with multi-item instruments consisting of binary measures, and is developed within the latent variable modeling methodology. The procedure is useful for evaluation of consistency of single measures and of sum scores from item sets…
Evaluation of Weighted Scale Reliability and Criterion Validity: A Latent Variable Modeling Approach
ERIC Educational Resources Information Center
Raykov, Tenko
2007-01-01
A method is outlined for evaluating the reliability and criterion validity of weighted scales based on sets of unidimensional measures. The approach is developed within the framework of latent variable modeling methodology and is useful for point and interval estimation of these measurement quality coefficients in counseling and education…
Further Examining Berry's Model: The Applicability of Latent Profile Analysis to Acculturation
ERIC Educational Resources Information Center
Fox, Rina S.; Merz, Erin L.; Solórzano, Martha T.; Roesch, Scott C.
2013-01-01
This study used latent profile analysis (LPA) to identify acculturation profiles. A three-profile solution fit the data best, and comparisons on demographic and psychosocial outcomes as a function of profile yielded expected results. The findings support using LPA as a parsimonious way to model acculturation without anticipating profiles in…
Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling.
ERIC Educational Resources Information Center
Cheong, JeeWon; MacKinnon, David P.; Khoo, Siek Toon
2003-01-01
Investigated a method to evaluate mediational processes using latent growth curve modeling and tested it with empirical data from a longitudinal steroid use prevention program focusing on 1,506 high school football players over 4 years. Findings suggest the usefulness of the approach. (SLD)
Aptitude, Achievement and Competence in Medicine: A Latent Variable Path Model
ERIC Educational Resources Information Center
Collin, V. Terri; Violato, Claudio; Hecker, Kent
2009-01-01
To develop and test a latent variable path model of general achievement, aptitude for medicine and competence in medicine employing data from the Medical College Admission Test (MCAT), pre-medical undergraduate grade point average (UGPA) and demographic characteristics for competence in pre-clinical and measures of competence (United States…
Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables
ERIC Educational Resources Information Center
Song, Xin-Yuan; Lee, Sik-Yum
2006-01-01
In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…
Some Results and Comments on Using Latent Structure Models to Measure Achievement.
ERIC Educational Resources Information Center
Wilcox, Rand R.
1980-01-01
Technical problems in achievement testing associated with using latent structure models to estimate the probability of guessing correct responses by examinees is studied; also the lack of problems associated with using Wilcox's formula score. Maximum likelihood estimates are derived which may be applied when items are hierarchically related.…
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.; Lee, Chun-Lung; Chang, Chi
2013-01-01
This note is concerned with a latent variable modeling approach for the study of differential item functioning in a multigroup setting. A multiple-testing procedure that can be used to evaluate group differences in response probabilities on individual items is discussed. The method is readily employed when the aim is also to locate possible…
Technology Transfer Automated Retrieval System (TEKTRAN)
Radiometric brightness temperature can be used in energy balance models that estimate sensible and latent heat fluxes of the land surface. However, brightness temperature is usually available only at one time of day when acquired from aircraft, fine-scale satellite platforms, or infrared thermometer...
Self-Esteem and Delinquency in South Korean Adolescents: Latent Growth Modeling
ERIC Educational Resources Information Center
Lee, Kyungeun; Lee, Julie
2012-01-01
This study examined the inter-related development of self-esteem and delinquency across three years. Participants were 3449 Korean high school adolescents (age M = 15.8, SD = 0.42, 1725 boys, 1724 girls) from Korea Youth Panel Study (KYPS), in 2005-2007, nationally representative of Korean adolescents. Latent growth modeling was employed for…
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2015-01-01
A direct approach to point and interval estimation of Cronbach's coefficient alpha for multiple component measuring instruments is outlined. The procedure is based on a latent variable modeling application with widely circulated software. As a by-product, using sample data the method permits ascertaining whether the population discrepancy…
ERIC Educational Resources Information Center
Weissman, Alexander
2013-01-01
Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…
ERIC Educational Resources Information Center
Wang, Chee Keng John; Pyun, Do Young; Liu, Woon Chia; Lim, Boon San Coral; Li, Fuzhong
2013-01-01
Using a multilevel latent growth curve modeling (LGCM) approach, this study examined longitudinal change in levels of physical fitness performance over time (i.e. four years) in young adolescents aged from 12-13 years. The sample consisted of 6622 students from 138 secondary schools in Singapore. Initial analyses found between-school variation on…
Propensity Score Analysis with Fallible Covariates: A Note on a Latent Variable Modeling Approach
ERIC Educational Resources Information Center
Raykov, Tenko
2012-01-01
A latent variable modeling approach that permits estimation of propensity scores in observational studies containing fallible independent variables is outlined, with subsequent examination of treatment effect. When at least one covariate is measured with error, it is indicated that the conventional propensity score need not possess the desirable…
An Evaluation of Latent Growth Models for Propensity Score Matched Groups
ERIC Educational Resources Information Center
Leite, Walter L.; Sandbach, Robert; Jin, Rong; MacInnes, Jann W.; Jackman, M. Grace-Anne
2012-01-01
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and…
Assessing the Reliability of Curriculum-Based Measurement: An Application of Latent Growth Modeling
ERIC Educational Resources Information Center
Yeo, Seungsoo; Kim, Dong-Il; Branum-Martin, Lee; Wayman, Miya Miura; Espin, Christine A.
2012-01-01
The purpose of this study was to demonstrate the use of Latent Growth Modeling (LGM) as a method for estimating reliability of Curriculum-Based Measurement (CBM) progress-monitoring data. The LGM approach permits the error associated with each measure to differ at each time point, thus providing an alternative method for examining of the…
The Structure of Student Satisfaction with College Services: A Latent Class Model
ERIC Educational Resources Information Center
Adwere-Boamah, Joseph
2011-01-01
Latent Class Analysis (LCA) was used to identify distinct groups of Community college students based on their self-ratings of satisfaction with student service programs. The programs were counseling, financial aid, health center, student programs and student government. The best fitting model to describe the data was a two Discrete-Factor model…
Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes
ERIC Educational Resources Information Center
Steele, Joel S.; Ferrer, Emilio
2011-01-01
We examine emotion self-regulation and coregulation in romantic couples using daily self-reports of positive and negative affect. We fit these data using a damped linear oscillator model specified as a latent differential equation to investigate affect dynamics at the individual level and coupled influences for the 2 partners in each couple.…
Latent Curve Modeling of Internalizing Behaviors and Interpersonal Skills through Elementary School
ERIC Educational Resources Information Center
Reynolds, Matthew R.; Sander, Janay B.; Irvin, Matthew J.
2010-01-01
The trajectories of internalizing and interpersonal behaviors from kindergarten through fifth grade were studied using univariate and bivariate latent curve models. Internalizing behaviors demonstrated a small, yet statistically significant, linear increase over time, while interpersonal behaviors showed a small, yet statistically significant,…
A General Multivariate Latent Growth Model with Applications to Student Achievement
ERIC Educational Resources Information Center
Bianconcini, Silvia; Cagnone, Silvia
2012-01-01
The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
ERIC Educational Resources Information Center
Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta
2011-01-01
"Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…
A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification
ERIC Educational Resources Information Center
Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi
2012-01-01
This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…
On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change
ERIC Educational Resources Information Center
Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von
2006-01-01
We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…
Longitudinal Analysis with Regressions among Random Effects: A Latent Variable Modeling Approach
ERIC Educational Resources Information Center
Raykov, Tenko
2007-01-01
A didactic discussion of a latent variable modeling approach is presented that addresses frequent empirical concerns of social, behavioral, and educational researchers involved in longitudinal studies. The method is suitable when the purpose is to analyze repeated measure data along several interrelated dimensions and to explain some of the…
Multivariate Effect Size Estimation: Confidence Interval Construction via Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2010-01-01
A latent variable modeling method is outlined for constructing a confidence interval (CI) of a popular multivariate effect size measure. The procedure uses the conventional multivariate analysis of variance (MANOVA) setup and is applicable with large samples. The approach provides a population range of plausible values for the proportion of…
Hu, Chuanpu; Szapary, Philippe O; Mendelsohn, Alan M; Zhou, Honghui
2014-08-01
Informative exposure-response modeling of clinical endpoints is important in drug development. There has been much recent progress in latent variable modeling of ordered categorical endpoints, including the application of indirect response (IDR) models and accounting for residual correlations between multiple categorical endpoints. This manuscript describes a framework of latent-variable-based IDR models that facilitate easy simultaneous modeling of a continuous and a categorical clinical endpoint. The model was applied to data from two phase III clinical trials of subcutaneously administered ustekinumab for the treatment of psoriatic arthritis, where Psoriasis Area and Severity Index scores and 20, 50, and 70 % improvement in the American College of Rheumatology response criteria were used as efficacy endpoints. Visual predictive check and external validation showed reasonable parameter estimation precision and model performance.
Hu, Chuanpu; Xu, Zhenhua; Mendelsohn, Alan M; Zhou, Honghui
2013-02-01
Accurate exposure-response modeling is important in drug development. Methods are still evolving in the use of mechanistic, e.g., indirect response (IDR) models to relate discrete endpoints, mostly of the ordered categorical form, to placebo/co-medication effect and drug exposure. When the discrete endpoint is derived using change-from-baseline measurements, a mechanistic exposure-response modeling approach requires adjustment to maintain appropriate interpretation. This manuscript describes a new modeling method that integrates a latent-variable representation of IDR models with standard logistic regression. The new method also extends to general link functions that cover probit regression or continuous clinical endpoint modeling. Compared to an earlier latent variable approach that constrained the baseline probability of response to be 0, placebo effect parameters in the new model formulation are more readily interpretable and can be separately estimated from placebo data, thus allowing convenient and robust model estimation. A general inherent connection of some latent variable representations with baseline-normalized standard IDR models is derived. For describing clinical response endpoints, Type I and Type III IDR models are shown to be equivalent, therefore there are only three identifiable IDR models. This approach was applied to data from two phase III clinical trials of intravenously administered golimumab for the treatment of rheumatoid arthritis, where 20, 50, and 70% improvement in the American College of Rheumatology disease severity criteria were used as efficacy endpoints. Likelihood profiling and visual predictive checks showed reasonable parameter estimation precision and model performance.