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Sample records for latent markov modelling

  1. Discrete Latent Markov Models for Normally Distributed Response Data

    ERIC Educational Resources Information Center

    Schmittmann, Verena D.; Dolan, Conor V.; van der Maas, Han L. J.; Neale, Michael C.

    2005-01-01

    Van de Pol and Langeheine (1990) presented a general framework for Markov modeling of repeatedly measured discrete data. We discuss analogical single indicator models for normally distributed responses. In contrast to discrete models, which have been studied extensively, analogical continuous response models have hardly been considered. These…

  2. Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

    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…

  3. Fitting and interpreting continuous-time latent Markov models for panel data.

    PubMed

    Lange, Jane M; Minin, Vladimir N

    2013-11-20

    Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting. PMID:23740756

  4. Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood

    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…

  5. Modeling threat assessments of water supply systems using markov latent effects methodology.

    SciTech Connect

    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.

  6. A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates.

    PubMed

    Bartolucci, Francesco; Farcomeni, Alessio

    2015-03-01

    Mixed latent Markov (MLM) models represent an important tool of analysis of longitudinal data when response variables are affected by time-fixed and time-varying unobserved heterogeneity, in which the latter is accounted for by a hidden Markov chain. In order to avoid bias when using a model of this type in the presence of informative drop-out, we propose an event-history (EH) extension of the latent Markov approach that may be used with multivariate longitudinal data, in which one or more outcomes of a different nature are observed at each time occasion. The EH component of the resulting model is referred to the interval-censored drop-out, and bias in MLM modeling is avoided by correlated random effects, included in the different model components, which follow common latent distributions. In order to perform maximum likelihood estimation of the proposed model by the expectation-maximization algorithm, we extend the usual forward-backward recursions of Baum and Welch. The algorithm has the same complexity as the one adopted in cases of non-informative drop-out. We illustrate the proposed approach through simulations and an application based on data coming from a medical study about primary biliary cirrhosis in which there are two outcomes of interest, one continuous and the other binary. PMID:25227970

  7. Using a Nonparametric Multilevel Latent Markov Model to Evaluate Diagnostics for Trachoma

    PubMed Central

    Koukounari, Artemis; Moustaki, Irini; Grassly, Nicholas C.; Blake, Isobel M.; Basáñez, María-Gloria; Gambhir, Manoj; Mabey, David C. W.; Bailey, Robin L.; Burton, Matthew J.; Solomon, Anthony W.; Donnelly, Christl A.

    2013-01-01

    In disease control or elimination programs, diagnostics are essential for assessing the impact of interventions, refining treatment strategies, and minimizing the waste of scarce resources. Although high-performance tests are desirable, increased accuracy is frequently accompanied by a requirement for more elaborate infrastructure, which is often not feasible in the developing world. These challenges are pertinent to mapping, impact monitoring, and surveillance in trachoma elimination programs. To help inform rational design of diagnostics for trachoma elimination, we outline a nonparametric multilevel latent Markov modeling approach and apply it to 2 longitudinal cohort studies of trachoma-endemic communities in Tanzania (2000–2002) and The Gambia (2001–2002) to provide simultaneous inferences about the true population prevalence of Chlamydia trachomatis infection and disease and the sensitivity, specificity, and predictive values of 3 diagnostic tests for C. trachomatis infection. Estimates were obtained by using data collected before and after mass azithromycin administration. Such estimates are particularly important for trachoma because of the absence of a true “gold standard” diagnostic test for C. trachomatis. Estimated transition probabilities provide useful insights into key epidemiologic questions about the persistence of disease and the clearance of infection as well as the required frequency of surveillance in the postelimination setting. PMID:23548755

  8. Alcohol Use Among American Indian High School Youths From Adolescence and Young Adulthood: A Latent Markov Model*

    PubMed Central

    Mitchell, Christina M.; Beals, Janette; Whitesell, Nancy Rumbaugh

    2008-01-01

    Objective: We explored patterns of alcohol use among American Indian youths as well as concurrent predictors and developmental outcomes 6 years later. Method: This study used six semi-annual waves of data collected across 3 years from 861 American Indian youths, ages 14-20 initially, from two western tribes. Using a latent Markov model, we examined patterns of change in latent states of adolescent alcohol use in the past 6 months, combining these states of alcohol use into three latent statuses that described patterns of change across the 3 years: abstainers, inconsistent drinkers, and consistent drinkers. We then explored how the latent statuses differed, both initially and in young adulthood (ages 20-26). Results: Both alcohol use and nonuse were quite stable across time, although we also found evidence of change. Despite some rather troubling drinking patterns as teens, especially among consistent drinkers, most of the youths had achieved important tasks of young adulthood. But patterns of use during adolescence were related to greater levels of substance use in young adulthood. Conclusions: Latent Markov modeling provided a useful categorization of alcohol use that more finely differentiated those youths who would otherwise have been considered inconsistent drinkers. Findings also suggest that broad-based interventions during adolescence may not be the most important ones; instead, programs targeting later alcohol and other drug use may be a more strategic use of often limited resources. PMID:18781241

  9. Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates

    PubMed Central

    Lagona, Francesco; Jdanov, Dmitri; Shkolnikova, Maria

    2014-01-01

    Longitudinal data are often segmented by unobserved time-varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject-specific random effects and Markovian sequences of time-varying effects in the linear predictor. We propose an expectation—maximization algorithm for maximum likelihood estimation, based on data augmentation. It reduces to the iterative maximization of the expected value of a complete likelihood function, derived from an augmented dataset with case weights, alternated with weights updating. In a case study of the Survey on Stress Aging and Health in Russia, the model is exploited to estimate the influence of the observed covariates under unobserved time-varying factors, which affect the cardiovascular activity of each subject during the observation period. PMID:24889355

  10. Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model.

    PubMed

    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

  11. Serial testing for latent tuberculosis using QuantiFERON-TB Gold In-Tube: A Markov model

    PubMed Central

    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

  12. Objective classification of latent behavioral states in bio-logging data using multivariate-normal hidden Markov models.

    PubMed

    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

  13. 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…

  14. 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.…

  15. Latent variable models with nonparametric interaction effects of latent variables.

    PubMed

    Song, Xinyuan; Lu, Zhaohua; Feng, Xiangnan

    2014-05-10

    Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P-splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology. PMID:24338916

  16. 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.…

  17. Relative survival multistate Markov model.

    PubMed

    Huszti, Ella; Abrahamowicz, Michal; Alioum, Ahmadou; Binquet, Christine; Quantin, Catherine

    2012-02-10

    Prognostic studies often have to deal with two important challenges: (i) separating effects of predictions on different 'competing' events and (ii) uncertainty about cause of death. Multistate Markov models permit multivariable analyses of competing risks of, for example, mortality versus disease recurrence. On the other hand, relative survival methods help estimate disease-specific mortality risks even in the absence of data on causes of death. In this paper, we propose a new Markov relative survival (MRS) model that attempts to combine these two methodologies. Our MRS model extends the existing multistate Markov piecewise constant intensities model to relative survival modeling. The intensity of transitions leading to death in the MRS model is modeled as the sum of an estimable excess hazard of mortality from the disease of interest and an 'offset' defined as the expected hazard of all-cause 'natural' mortality obtained from relevant life-tables. We evaluate the new MRS model through simulations, with a design based on registry-based prognostic studies of colon cancer. Simulation results show almost unbiased estimates of prognostic factor effects for the MRS model. We also applied the new MRS model to reassess the role of prognostic factors for mortality in a study of colorectal cancer. The MRS model considerably reduces the bias observed with the conventional Markov model that does not permit accounting for unknown causes of death, especially if the 'true' effects of a prognostic factor on the two types of mortality differ substantially.

  18. 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.

  19. Semi-Markov Arnason-Schwarz models.

    PubMed

    King, Ruth; Langrock, Roland

    2016-06-01

    We consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. Traditionally, the Arnason-Schwarz model has been fitted to such data where the state process is modeled as a first-order Markov chain, though second-order models have also been proposed and fitted to data. However, low-order Markov models may not accurately represent the underlying biology. For example, specifying a (time-independent) first-order Markov process involves the assumption that the dwell time in each state (i.e., the duration of a stay in a given state) has a geometric distribution, and hence that the modal dwell time is one. Specifying time-dependent or higher-order processes provides additional flexibility, but at the expense of a potentially significant number of additional model parameters. We extend the Arnason-Schwarz model by specifying a semi-Markov model for the state process, where the dwell-time distribution is specified more generally, using, for example, a shifted Poisson or negative binomial distribution. A state expansion technique is applied in order to represent the resulting semi-Markov Arnason-Schwarz model in terms of a simpler and computationally tractable hidden Markov model. Semi-Markov Arnason-Schwarz models come with only a very modest increase in the number of parameters, yet permit a significantly more flexible state process. Model selection can be performed using standard procedures, and in particular via the use of information criteria. The semi-Markov approach allows for important biological inference to be drawn on the underlying state process, for example, on the times spent in the different states. The feasibility of the approach is demonstrated in a simulation study, before being applied to real data corresponding to house finches where the states correspond to the presence or absence of conjunctivitis. PMID:26584064

  20. Semi-Markov Arnason-Schwarz models.

    PubMed

    King, Ruth; Langrock, Roland

    2016-06-01

    We consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. Traditionally, the Arnason-Schwarz model has been fitted to such data where the state process is modeled as a first-order Markov chain, though second-order models have also been proposed and fitted to data. However, low-order Markov models may not accurately represent the underlying biology. For example, specifying a (time-independent) first-order Markov process involves the assumption that the dwell time in each state (i.e., the duration of a stay in a given state) has a geometric distribution, and hence that the modal dwell time is one. Specifying time-dependent or higher-order processes provides additional flexibility, but at the expense of a potentially significant number of additional model parameters. We extend the Arnason-Schwarz model by specifying a semi-Markov model for the state process, where the dwell-time distribution is specified more generally, using, for example, a shifted Poisson or negative binomial distribution. A state expansion technique is applied in order to represent the resulting semi-Markov Arnason-Schwarz model in terms of a simpler and computationally tractable hidden Markov model. Semi-Markov Arnason-Schwarz models come with only a very modest increase in the number of parameters, yet permit a significantly more flexible state process. Model selection can be performed using standard procedures, and in particular via the use of information criteria. The semi-Markov approach allows for important biological inference to be drawn on the underlying state process, for example, on the times spent in the different states. The feasibility of the approach is demonstrated in a simulation study, before being applied to real data corresponding to house finches where the states correspond to the presence or absence of conjunctivitis.

  1. 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…

  2. A Multicomponent Latent Trait Model for Diagnosis

    ERIC Educational Resources Information Center

    Embretson, Susan E.; Yang, Xiangdong

    2013-01-01

    This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait…

  3. On multitarget pairwise-Markov models

    NASA Astrophysics Data System (ADS)

    Mahler, Ronald

    2015-05-01

    Single- and multi-target tracking are both typically based on strong independence assumptions regarding both the target states and sensor measurements. In particular, both are theoretically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain that is observed by an independent observation process. Since HMC assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as a way to weaken those assumptions. In this paper it is shown that the PMC model can be directly generalized to multitarget problems. Since the resulting tracking filters are computationally intractable, the paper investigates generalizations of the cardinalized probability hypothesis density (CPHD) filter to applications with PMC models.

  4. Zipf exponent of trajectory distribution in the hidden Markov model

    NASA Astrophysics Data System (ADS)

    Bochkarev, V. V.; Lerner, E. Yu

    2014-03-01

    This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.

  5. 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…

  6. The infinite hidden Markov random field model.

    PubMed

    Chatzis, Sotirios P; Tsechpenakis, Gabriel

    2010-06-01

    Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation procedure. Existing methods, including likelihood- or entropy-based criteria, and reversible Markov chain Monte Carlo methods, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (DP, infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori; infinite mixture models based on the original DP or spatially constrained variants of it have been applied in unsupervised image segmentation applications showing promising results. Under this motivation, to resolve the aforementioned issues of HMRF models, in this paper, we introduce a nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model, formulated on the basis of a joint Dirichlet process mixture (DPM) and Markov random field (MRF) construction. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally demonstrate its advantages over competing methodologies.

  7. 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…

  8. Insidious Structural Errors in Latent Variable Models.

    ERIC Educational Resources Information Center

    Pohlmann, John T.

    1993-01-01

    Nonlinear relationships and latent variable assumptions can lead to serious specification errors in structural models. A quadratic relationship, described by a linear structural model with a latent variable, is shown to have less predictive validity than a simple manifest variable regression model. Advocates the use of simpler preliminary…

  9. 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.

  10. Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

    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…

  11. Information-Theoretic Latent Distribution Modeling: Distinguishing Discrete and Continuous Latent Variable Models

    ERIC Educational Resources Information Center

    Markon, Kristian E.; Krueger, Robert F.

    2006-01-01

    Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed…

  12. 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.

  13. 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.

  14. Hidden Markov Model Analysis of Multichromophore Photobleaching

    PubMed Central

    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

  15. Markov counting models for correlated binary responses.

    PubMed

    Crawford, Forrest W; Zelterman, Daniel

    2015-07-01

    We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology. PMID:25792624

  16. Markov counting models for correlated binary responses.

    PubMed

    Crawford, Forrest W; Zelterman, Daniel

    2015-07-01

    We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.

  17. Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

    USGS Publications Warehouse

    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.

  18. MODELING PAVEMENT DETERIORATION PROCESSES BY POISSON HIDDEN MARKOV MODELS

    NASA Astrophysics Data System (ADS)

    Nam, Le Thanh; Kaito, Kiyoyuki; Kobayashi, Kiyoshi; Okizuka, Ryosuke

    In pavement management, it is important to estimate lifecycle cost, which is composed of the expenses for repairing local damages, including potholes, and repairing and rehabilitating the surface and base layers of pavements, including overlays. In this study, a model is produced under the assumption that the deterioration process of pavement is a complex one that includes local damages, which occur frequently, and the deterioration of the surface and base layers of pavement, which progresses slowly. The variation in pavement soundness is expressed by the Markov deterioration model and the Poisson hidden Markov deterioration model, in which the frequency of local damage depends on the distribution of pavement soundness, is formulated. In addition, the authors suggest a model estimation method using the Markov Chain Monte Carlo (MCMC) method, and attempt to demonstrate the applicability of the proposed Poisson hidden Markov deterioration model by studying concrete application cases.

  19. Recovery of Graded Response Model Parameters: A Comparison of Marginal Maximum Likelihood and Markov Chain Monte Carlo Estimation

    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…

  20. A Markov model of the Indus script.

    PubMed

    Rao, Rajesh P N; Yadav, Nisha; Vahia, Mayank N; Joglekar, Hrishikesh; Adhikari, R; Mahadevan, Iravatham

    2009-08-18

    Although no historical information exists about the Indus civilization (flourished ca. 2600-1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional subunits of a possible underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Application of this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus script exhibits rich synactic structure and the ability to represent diverse content. both of which are suggestive of a linguistic writing system rather than a nonlinguistic symbol system. PMID:19666571

  1. Hidden Markov models for stochastic thermodynamics

    NASA Astrophysics Data System (ADS)

    Bechhoefer, John

    2015-07-01

    The formalism of state estimation and hidden Markov models can simplify and clarify the discussion of stochastic thermodynamics in the presence of feedback and measurement errors. After reviewing the basic formalism, we use it to shed light on a recent discussion of phase transitions in the optimized response of an information engine, for which measurement noise serves as a control parameter. The HMM formalism also shows that the value of additional information displays a maximum at intermediate signal-to-noise ratios. Finally, we discuss how systems open to information flow can apparently violate causality; the HMM formalism can quantify the performance gains due to such violations.

  2. Monitoring volcano activity through Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.

    2013-12-01

    During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.

  3. Forest Pest Occurrence Predictionca-Markov Model

    NASA Astrophysics Data System (ADS)

    Xie, Fangyi; Zhang, Xiaoli; Chen, Xiaoyan

    Since the spatial pattern of forest pest occurrence is determined by biological characteristics and habitat conditions, this paper introduced construction of a cellular automaton model combined with Markov model to predicate the forest pest occurrence. Rules of the model includes the cell states rules, neighborhood rules and transition rules which are defined according to the factors from stand conditions, stand structures, climate and the influence of the factors on the state conversion. Coding for the model is also part of the implementations of the model. The participants were designed including attributes and operations of participants expressed with a UML diagram. Finally, the scale issues on forest pest occurrence prediction, of which the core are the prediction of element size and time interval, are partly discussed in this paper.

  4. Mixture Hidden Markov Models in Finance Research

    NASA Astrophysics Data System (ADS)

    Dias, José G.; Vermunt, Jeroen K.; Ramos, Sofia

    Finite mixture models have proven to be a powerful framework whenever unobserved heterogeneity cannot be ignored. We introduce in finance research the Mixture Hidden Markov Model (MHMM) that takes into account time and space heterogeneity simultaneously. This approach is flexible in the sense that it can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity. This methodology is applied to model simultaneously 12 time series of Asian stock markets indexes. Because we selected a heterogeneous sample of countries including both developed and emerging countries, we expect that heterogeneity in market returns due to country idiosyncrasies will show up in the results. The best fitting model was the one with two clusters at country level with different dynamics between the two regimes.

  5. Estimation and uncertainty of reversible Markov models.

    PubMed

    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. PMID:26547152

  6. 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.

  7. Markov state models of biomolecular conformational dynamics

    PubMed Central

    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

  8. 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…

  9. 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…

  10. 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…

  11. 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.

  12. Multiple alignment using hidden Markov models

    SciTech Connect

    Eddy, S.R.

    1995-12-31

    A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignments is evaluated on structural alignments of ten different protein families, and compared to the performance of other HMM training methods and the ClustalW program. Simulated annealing is better able to find near-global optima in the multiple alignment probability landscape than the other tested HMM training methods. Neither ClustalW nor simulated annealing produce consistently better alignments compared to each other. Examination of the specific cases in which ClustalW outperforms simulated annealing, and vice versa, provides insight into the strengths and weaknesses of current hidden Maxkov model approaches.

  13. Markov branching in the vertex splitting model

    NASA Astrophysics Data System (ADS)

    Örn Stefánsson, Sigurdur

    2012-04-01

    We study a special case of the vertex splitting model which is a recent model of randomly growing trees. For any finite maximum vertex degree D, we find a one parameter model, with parameter \\alpha \\in [0,1] which has a so-called Markov branching property. When D=\\infty we find a two parameter model with an additional parameter \\gamma \\in [0,1] which also has this feature. In the case D = 3, the model bears resemblance to Ford's α-model of phylogenetic trees and when D=\\infty it is similar to its generalization, the αγ-model. For α = 0, the model reduces to the well known model of preferential attachment. In the case α > 0, we prove convergence of the finite volume probability measures, generated by the growth rules, to a measure on infinite trees which is concentrated on the set of trees with a single spine. We show that the annealed Hausdorff dimension with respect to the infinite volume measure is 1/α. When γ = 0 the model reduces to a model of growing caterpillar graphs in which case we prove that the Hausdorff dimension is almost surely 1/α and that the spectral dimension is almost surely 2/(1 + α). We comment briefly on the distribution of vertex degrees and correlations between degrees of neighbouring vertices.

  14. Confirmatory Measurement Model Comparisons Using Latent Means.

    ERIC Educational Resources Information Center

    Millsap, Roger E.; Everson, Howard

    1991-01-01

    Use of confirmatory factor analysis (CFA) with nonzero latent means in testing six different measurement models from classical test theory is discussed. Implications of the six models for observed mean and covariance structures are described, and three examples of the use of CFA in testing the models are presented. (SLD)

  15. 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.

  16. Modelling modal gating of ion channels with hierarchical Markov models

    PubMed Central

    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.

  17. Modelling modal gating of ion channels with hierarchical Markov models

    PubMed Central

    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

  18. Noiseless compression using non-Markov models

    NASA Technical Reports Server (NTRS)

    Blumer, Anselm

    1989-01-01

    Adaptive data compression techniques can be viewed as consisting of a model specified by a database common to the encoder and decoder, an encoding rule and a rule for updating the model to ensure that the encoder and decoder always agree on the interpretation of the next transmission. The techniques which fit this framework range from run-length coding, to adaptive Huffman and arithmetic coding, to the string-matching techniques of Lempel and Ziv. The compression obtained by arithmetic coding is dependent on the generality of the source model. For many sources, an independent-letter model is clearly insufficient. Unfortunately, a straightforward implementation of a Markov model requires an amount of space exponential in the number of letters remembered. The Directed Acyclic Word Graph (DAWG) can be constructed in time and space proportional to the text encoded, and can be used to estimate the probabilities required for arithmetic coding based on an amount of memory which varies naturally depending on the encoded text. The tail of that portion of the text which was encoded is the longest suffix that has occurred previously. The frequencies of letters following these previous occurrences can be used to estimate the probability distribution of the next letter. Experimental results indicate that compression is often far better than that obtained using independent-letter models, and sometimes also significantly better than other non-independent techniques.

  19. Modeling stereopsis via Markov random field.

    PubMed

    Ming, Yansheng; Hu, Zhanyi

    2010-08-01

    Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity encoders in the visual pathway. Our likelihood function is also relevant to several psychological findings. The potential function in our model is constrained by the psychological finding that the strength of the cooperative interaction minimizing relative disparity decreases as the separation between stimuli increases. Our model is tested on three kinds of stereo images. In simulations on images with repetitive patterns, we demonstrate that our model could account for the human depth percepts that were previously explained by the second-order mechanism. In simulations on random dot stereograms and natural scene images, we demonstrate that false matches introduced by the disparity energy model can be reliably removed using our model. A comparison with the coarse-to-fine model shows that our model is able to compute the absolute disparity of small objects with larger relative disparity. We also relate our model to several physiological findings. The hypothesized neurons of the model are selective for absolute disparity and have facilitative extra receptive field. There are plenty of such neurons in the visual cortex. In conclusion, we think that stereopsis can be implemented by neural networks resembling MRF.

  20. 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.

  1. Inference for dynamic and latent variable models via iterated, perturbed Bayes maps

    PubMed Central

    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

  2. Specification test for Markov models with measurement errors*

    PubMed Central

    Kim, Seonjin; Zhao, Zhibiao

    2014-01-01

    Most existing works on specification testing assume that we have direct observations from the model of interest. We study specification testing for Markov models based on contaminated observations. The evolving model dynamics of the unobservable Markov chain is implicitly coded into the conditional distribution of the observed process. To test whether the underlying Markov chain follows a parametric model, we propose measuring the deviation between nonparametric and parametric estimates of conditional regression functions of the observed process. Specifically, we construct a nonparametric simultaneous confidence band for conditional regression functions and check whether the parametric estimate is contained within the band. PMID:25346552

  3. Optimized Markov state models for metastable systems

    NASA Astrophysics Data System (ADS)

    Guarnera, Enrico; Vanden-Eijnden, Eric

    2016-07-01

    A method is proposed to identify target states that optimize a metastability index amongst a set of trial states and use these target states as milestones (or core sets) to build Markov State Models (MSMs). If the optimized metastability index is small, this automatically guarantees the accuracy of the MSM, in the sense that the transitions between the target milestones is indeed approximately Markovian. The method is simple to implement and use, it does not require that the dynamics on the trial milestones be Markovian, and it also offers the possibility to partition the system's state-space by assigning every trial milestone to the target milestones it is most likely to visit next and to identify transition state regions. Here the method is tested on the Gly-Ala-Gly peptide, where it is shown to correctly identify the expected metastable states in the dihedral angle space of the molecule without a priori information about these states. It is also applied to analyze the folding landscape of the Beta3s mini-protein, where it is shown to identify the folded basin as a connecting hub between an helix-rich region, which is entropically stabilized, and a beta-rich region, which is energetically stabilized and acts as a kinetic trap.

  4. Systolic Architectures For Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Hwang, J. N.; Vlontzos, J. A.; Kung, S. Y.

    1988-10-01

    This paper proposes an unidirectional ring systolic architecture for implementing the hidden Markov models (HMMs). This array architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. Both the scoring and learning phases of an HMM are formulated as a consecutive matrix-vector multiplication problem, which can be executed in a fully pipelined fashion (100% utilization effi-ciency) by using an unidirectional ring systolic architecture. By appropriately scheduling the algorithm, which combines both the operations of the backward evaluation procedure and reestimation algorithm at the same time, we can use this systolic HMM in a most efficient manner. The systolic HMM can also be easily adapted to the left-to-right HMM by using bidirectional semi-global links with significant time saving. This architecture can also incorporate the scaling scheme with little extra effort in the computations of forward and backward evaluation variables to prevent the frequently encountered mathematical undertow problems. We also discuss a possible implementation of this proposed architecture using Inmos transputer (T-800) as the building block.

  5. Spatiotemporal pattern recognition using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Fielding, Kenneth H.; Ruck, Dennis W.; Rogers, Steven K.; Welsh, Byron M.; Oxley, Mark E.

    1993-10-01

    A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared to single frame techniques.

  6. Stochastic motif extraction using hidden Markov model

    SciTech Connect

    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.

  7. Modeling anger and aggressive driving behavior in a dynamic choice-latent variable model.

    PubMed

    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.

  8. Facies Reconstruction by hidden Markov models

    NASA Astrophysics Data System (ADS)

    Panzeri, M.; Della Rossa, E.; Dovera, L.; Riva, M.; Guadagnini, A.

    2012-04-01

    The inherent heterogeneity of natural aquifer complex systems can be properly described by a doubly stochastic composite medium approach, where distributions of geomaterials (facies) and attributes, e.g., hydraulic conductivity and porosity, can be uncertain. We focus on the reconstruction of the spatial distribution of facies within a porous medium. The key contribution of our work is to provide a methodology for evaluating the unknown facies distribution while maintaining the spatial correlation between the geological bodies. The latter is considered to be known a priori. The geostatistical model for the spatial distribution of facies is defined in the framework of multiple-point geostatistics, relying on transition probabilities (Stien and Kolbjornsen, 2011). Specifically, we model the facies distribution over the domain by employing the notion of Hidden Markov Model. The hidden states of the system are provided by the value of the indicator function at each cell of the grid, while the the petrophysical properties of the soil (e.g., the permeability) are considered as known. In this context, the key issue is the assessment of the spatial architecture of the geological bodies within the domain of interest upon maximizing the probability associated with a given permeability distribution. This objective is achieved through the Viterbi algorithm. This algorithm was initially introduced for signal denoising problems (e.g., Rabiner, 1989) and has been extended here to a two-dimensional system, following the approach proposed by Li et al. (2000) according to the following steps: (1) the parameters of the transitional probabilities of the facies distribution are estimated from a given training image; (2) the facies distribution maximizing the probability of occurrence considering the probability of (i) facies distribution, (ii) conductivity distribution and (iii) their joint conditional probability is then reconstructed. We demonstrate the reliability and advantage of

  9. Medical Inpatient Journey Modeling and Clustering: A Bayesian Hidden Markov Model Based Approach

    PubMed Central

    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

  10. 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…

  11. Time series segmentation with shifting means hidden markov models

    NASA Astrophysics Data System (ADS)

    Kehagias, Ath.; Fortin, V.

    2006-08-01

    We present a new family of hidden Markov models and apply these to the segmentation of hydrological and environmental time series. The proposed hidden Markov models have a discrete state space and their structure is inspired from the shifting means models introduced by Chernoff and Zacks and by Salas and Boes. An estimation method inspired from the EM algorithm is proposed, and we show that it can accurately identify multiple change-points in a time series. We also show that the solution obtained using this algorithm can serve as a starting point for a Monte-Carlo Markov chain Bayesian estimation method, thus reducing the computing time needed for the Markov chain to converge to a stationary distribution.

  12. Hidden Markov Models: The Best Models for Forager Movements?

    PubMed Central

    Joo, Rocio; Bertrand, Sophie; Tam, Jorge; Fablet, Ronan

    2013-01-01

    One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (∼1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines) for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%), significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance. PMID:24058400

  13. 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…

  14. Multistage sampling for latent variable models.

    PubMed

    Thomas, Duncan C

    2007-12-01

    I consider the design of multistage sampling schemes for epidemiologic studies involving latent variable models, with surrogate measurements of the latent variables on a subset of subjects. Such models arise in various situations: when detailed exposure measurements are combined with variables that can be used to assign exposures to unmeasured subjects; when biomarkers are obtained to assess an unobserved pathophysiologic process; or when additional information is to be obtained on confounding or modifying variables. In such situations, it may be possible to stratify the subsample on data available for all subjects in the main study, such as outcomes, exposure predictors, or geographic locations. Three circumstances where analytic calculations of the optimal design are possible are considered: (i) when all variables are binary; (ii) when all are normally distributed; and (iii) when the latent variable and its measurement are normally distributed, but the outcome is binary. In each of these cases, it is often possible to considerably improve the cost efficiency of the design by appropriate selection of the sampling fractions. More complex situations arise when the data are spatially distributed: the spatial correlation can be exploited to improve exposure assignment for unmeasured locations using available measurements on neighboring locations; some approaches for informative selection of the measurement sample using location and/or exposure predictor data are considered.

  15. 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.

  16. Utilizing Gaussian Markov random field properties of Bayesian animal models.

    PubMed

    Steinsland, Ingelin; Jensen, Henrik

    2010-09-01

    In this article, we demonstrate how Gaussian Markov random field properties give large computational benefits and new opportunities for the Bayesian animal model. We make inference by computing the posteriors for important quantitative genetic variables. For the single-trait animal model, a nonsampling-based approximation is presented. For the multitrait model, we set up a robust and fast Markov chain Monte Carlo algorithm. The proposed methodology was used to analyze quantitative genetic properties of morphological traits of a wild house sparrow population. Results for single- and multitrait models were compared.

  17. Modeling Heterogeneity of Latent Growth Depending on Initial Status

    ERIC Educational Resources Information Center

    Klein, Andreas G.; Muthen, Bengt O.

    2006-01-01

    In this article, a heterogeneous latent growth curve model for modeling heterogeneity of growth rates is proposed. The suggested model is an extension of a conventional growth curve model and a complementary tool to mixed growth modeling. It allows the modeling of heterogeneity of growth rates as a continuous function of latent initial status and…

  18. Resilient model approximation for Markov jump time-delay systems via reduced model with hierarchical Markov chains

    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.

  19. Assessment of optimized Markov models in protein fold classification.

    PubMed

    Lampros, Christos; Simos, Thomas; Exarchos, Themis P; Exarchos, Konstantinos P; Papaloukas, Costas; Fotiadis, Dimitrios I

    2014-08-01

    Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%. PMID:25152041

  20. Alternative approaches for econometric analysis of panel count data using dynamic latent class models (with application to doctor visits data).

    PubMed

    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. PMID:22556003

  1. Lie Markov models with purine/pyrimidine symmetry.

    PubMed

    Fernández-Sánchez, Jesús; Sumner, Jeremy G; Jarvis, Peter D; Woodhams, Michael D

    2015-03-01

    Continuous-time Markov chains are a standard tool in phylogenetic inference. If homogeneity is assumed, the chain is formulated by specifying time-independent rates of substitutions between states in the chain. In applications, there are usually extra constraints on the rates, depending on the situation. If a model is formulated in this way, it is possible to generalise it and allow for an inhomogeneous process, with time-dependent rates satisfying the same constraints. It is then useful to require that, under some time restrictions, there exists a homogeneous average of this inhomogeneous process within the same model. This leads to the definition of "Lie Markov models" which, as we will show, are precisely the class of models where such an average exists. These models form Lie algebras and hence concepts from Lie group theory are central to their derivation. In this paper, we concentrate on applications to phylogenetics and nucleotide evolution, and derive the complete hierarchy of Lie Markov models that respect the grouping of nucleotides into purines and pyrimidines-that is, models with purine/pyrimidine symmetry. We also discuss how to handle the subtleties of applying Lie group methods, most naturally defined over the complex field, to the stochastic case of a Markov process, where parameter values are restricted to be real and positive. In particular, we explore the geometric embedding of the cone of stochastic rate matrices within the ambient space of the associated complex Lie algebra.

  2. Multiensemble Markov models of molecular thermodynamics and kinetics

    PubMed Central

    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

  3. 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.

  4. A Markov switching model for annual hydrologic time series

    NASA Astrophysics Data System (ADS)

    Akıntuǧ, B.; Rasmussen, P. F.

    2005-09-01

    This paper investigates the properties of Markov switching (MS) models (also known as hidden Markov models) for generating annual time series. This type of model has been used in a number of recent studies in the water resources literature. The model considered here assumes that climate is switching between M states and that the state sequence can be described by a Markov chain. Observations are assumed to be drawn from a normal distribution whose parameters depend on the state variable. We present the stochastic properties of this class of models along with procedures for model identification and parameter estimation. Although, at a first glance, MS models appear to be quite different from ARMA models, we show that it is possible to find an ARMA model that has the same autocorrelation function and the same marginal distribution as any given MS model. Hence, despite the difference in model structure, there are strong similarities between MS and ARMA models. MS and ARMA models are applied to the time series of mean annual discharge of the Niagara River. Although it is difficult to draw any general conclusion from a single case study, it appears that MS models (and ARMA models derived from MS models) generally have stronger autocorrelation at higher lags than ARMA models estimated by conventional maximum likelihood. This may be an important property if the purpose of the study is the analysis of multiyear droughts.

  5. Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories

    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 curve models when change…

  6. 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…

  7. 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

  8. Teacher’s Corner: Latent Curve Models and Latent Change Score Models Estimated in R

    PubMed Central

    Ghisletta, Paolo; McArdle, John J.

    2014-01-01

    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 to 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 statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, & McArdle, 2004. The data and all scripts used here are available on the first author’s website. PMID:25505366

  9. Nonlinear Latent Curve Models for Multivariate Longitudinal Data

    ERIC Educational Resources Information Center

    Blozis, Shelley A.; Conger, Katherine J.; Harring, Jeffrey R.

    2007-01-01

    Latent curve models have become a useful approach to analyzing longitudinal data, due in part to their allowance of and emphasis on individual differences in features that describe change. Common applications of latent curve models in developmental studies rely on polynomial functions, such as linear or quadratic functions. Although useful for…

  10. 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…

  11. 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…

  12. On the Utilization of Sample Weights in Latent Variable Models.

    ERIC Educational Resources Information Center

    Kaplan, David; Ferguson, Aaron J.

    1999-01-01

    Examines the use of sample weights in latent variable models in the case where a simple random sample is drawn from a population containing a mixture of strata through a bootstrap simulation study. Results show that ignoring weights can lead to serious bias in latent variable model parameters and reveal the advantages of using sample weights. (SLD)

  13. 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…

  14. Multivariate longitudinal data analysis with mixed effects hidden Markov models.

    PubMed

    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. PMID:25761965

  15. 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.

  16. Stochastic algorithms for Markov models estimation with intermittent missing data.

    PubMed

    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.

  17. 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…

  18. Evaluating Intercept-Slope Interactions in Latent Growth Modeling

    ERIC Educational Resources Information Center

    Sun, Ronghua; Willson, Victor L.

    2009-01-01

    The effects of misspecifying intercept-covariate interactions in a 4 time-point latent growth model were the focus of this investigation. The investigation was motivated by school growth studies in which students' entry-level skills may affect their rate of growth. We studied the latent interaction of intercept and a covariate in predicting growth…

  19. 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.

  20. Infinite Factorial Unbounded-State Hidden Markov Model.

    PubMed

    Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando

    2016-09-01

    There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markovmodels (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem. PMID:26571511

  1. Behavior Detection using Confidence Intervals of Hidden Markov Models

    SciTech Connect

    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.

  2. Of bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research

    USGS Publications Warehouse

    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.

  3. 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…

  4. Hidden Markov Modeling for Weigh-In-Motion Estimation

    SciTech Connect

    Abercrombie, Robert K; Ferragut, Erik M; Boone, Shane

    2012-01-01

    This paper describes a hidden Markov model to assist in the weight measurement error that arises from complex vehicle oscillations of a system of discrete masses. Present reduction of oscillations is by a smooth, flat, level approach and constant, slow speed in a straight line. The model uses this inherent variability to assist in determining the true total weight and individual axle weights of a vehicle. The weight distribution dynamics of a generic moving vehicle were simulated. The model estimation converged to within 1% of the true mass for simulated data. The computational demands of this method, while much greater than simple averages, took only seconds to run on a desktop computer.

  5. Distribution system reliability assessment using hierarchical Markov modeling

    SciTech Connect

    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.

  6. Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms.

    PubMed

    Langrock, Roland; Swihart, Bruce J; Caffo, Brian S; Punjabi, Naresh M; Crainiceanu, Ciprian M

    2013-08-30

    In this manuscript, we consider methods for the analysis of populations of electroencephalogram signals during sleep for the study of sleep disorders using hidden Markov models (HMMs). Notably, we propose an easily implemented method for simultaneously modeling multiple time series that involve large amounts of data. We apply these methods to study sleep-disordered breathing (SDB) in the Sleep Heart Health Study (SHHS), a landmark study of SDB and cardiovascular consequences. We use the entire, longitudinally collected, SHHS cohort to develop HMM population parameters, which we then apply to obtain subject-specific Markovian predictions. From these predictions, we create several indices of interest, such as transition frequencies between latent states. Our HMM analysis of electroencephalogram signals uncovers interesting findings regarding differences in brain activity during sleep between those with and without SDB. These findings include stability of the percent time spent in HMM latent states across matched diseased and non-diseased groups and differences in the rate of transitioning. PMID:23348835

  7. CLUSTERING SOUTH AFRICAN HOUSEHOLDS BASED ON THEIR ASSET STATUS USING LATENT VARIABLE MODELS

    PubMed Central

    McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.

    2014-01-01

    The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region. PMID:25485026

  8. Trajectory classification using switched dynamical hidden Markov models.

    PubMed

    Nascimento, Jacinto C; Figueiredo, Mario; Marques, Jorge S

    2010-05-01

    This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical hidden Markov model (SD-HMM). The performance of the proposed method is illustrated with real data from two different scenarios: a shopping center and a university campus. A set of human activities in both scenarios is successfully recognized by the proposed system. These experiments show the ability of our approach to properly describe trajectories with sudden changes.

  9. Estimation in a semi-Markov transformation model

    PubMed Central

    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

  10. Trajectory classification using switched dynamical hidden Markov models.

    PubMed

    Nascimento, Jacinto C; Figueiredo, Mario; Marques, Jorge S

    2010-05-01

    This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical hidden Markov model (SD-HMM). The performance of the proposed method is illustrated with real data from two different scenarios: a shopping center and a university campus. A set of human activities in both scenarios is successfully recognized by the proposed system. These experiments show the ability of our approach to properly describe trajectories with sudden changes. PMID:20051342

  11. Hidden Markov Models for Detecting Aseismic Events in Southern California

    NASA Astrophysics Data System (ADS)

    Granat, R.

    2004-12-01

    We employ a hidden Markov model (HMM) to segment surface displacement time series collection by the Southern California Integrated Geodetic Network (SCIGN). These segmented time series are then used to detect regional events by observing the number of simultaneous mode changes across the network; if a large number of stations change at the same time, that indicates an event. The hidden Markov model (HMM) approach assumes that the observed data has been generated by an unobservable dynamical statistical process. The process is of a particular form such that each observation is coincident with the system being in a particular discrete state, which is interpreted as a behavioral mode. The dynamics are the model are constructed so that the next state is directly dependent only on the current state -- it is a first order Markov process. The model is completely described by a set of parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probability distribution of observable outputs associated with each state. The result of this approach is that our segmentation decisions are based entirely on statistical changes in the behavior of the observed daily displacements. In general, finding the optimal model parameters to fit the data is a difficult problem. We present an innovative model fitting method that is unsupervised (i.e., it requires no labeled training data) and uses a regularized version of the expectation-maximization (EM) algorithm to ensure that model solutions are both robust with respect to initial conditions and of high quality. We demonstrate the reliability of the method as compared to standard model fitting methods and show that it results in lower noise in the mode change correlation signal used to detect regional events. We compare candidate events detected by this method to the seismic record and observe that most are not correlated with a significant seismic event. Our analysis

  12. Improved Hidden-Markov-Model Method Of Detecting Faults

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J.

    1994-01-01

    Method of automated, continuous monitoring to detect faults in complicated dynamic system based on hidden-Markov-model (HMM) approach. Simpler than another, recently proposed HMM method, but retains advantages of that method, including low susceptibility to false alarms, no need for mathematical model of dynamics of system under normal or faulty conditions, and ability to detect subtle changes in characteristics of monitored signals. Examples of systems monitored by use of this method include motors, turbines, and pumps critical in their applications; chemical-processing plants; powerplants; and biomedical systems.

  13. Hidden Markov models for evolution and comparative genomics analysis.

    PubMed

    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.

  14. Markov Modeling with Soft Aggregation for Safety and Decision Analysis

    SciTech Connect

    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

  15. Hidden Markov model using Dirichlet process for de-identification.

    PubMed

    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. PMID:26407642

  16. Markov-random-field modeling for linear seismic tomography.

    PubMed

    Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro

    2014-10-01

    We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.

  17. Markov-random-field modeling for linear seismic tomography

    NASA Astrophysics Data System (ADS)

    Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro

    2014-10-01

    We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.

  18. AIRWAY LABELING USING A HIDDEN MARKOV TREE MODEL

    PubMed Central

    Ross, James C.; Díaz, Alejandro A.; Okajima, Yuka; Wassermann, Demian; Washko, George R.; Dy, Jennifer; San José Estépar, Raúl

    2014-01-01

    We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal’s minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study. PMID:25436039

  19. 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…

  20. Effects of Latent Variable Nonnormality and Model Misspecification on Testing Structural Equation Modeling Interactions

    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…

  1. Parameter estimation in deformable models using Markov chain Monte Carlo

    NASA Astrophysics Data System (ADS)

    Chalana, Vikram; Haynor, David R.; Sampson, Paul D.; Kim, Yongmin

    1997-04-01

    Deformable models have gained much popularity recently for many applications in medical imaging, such as image segmentation, image reconstruction, and image registration. Such models are very powerful because various kinds of information can be integrated together in an elegant statistical framework. Each such piece of information is typically associated with a user-defined parameter. The values of these parameters can have a significant effect on the results generated using these models. Despite the popularity of deformable models for various applications, not much attention has been paid to the estimation of these parameters. In this paper we describe systematic methods for the automatic estimation of these deformable model parameters. These methods are derived by posing the deformable models as a Bayesian inference problem. Our parameter estimation methods use Markov chain Monte Carlo methods for generating samples from highly complex probability distributions.

  2. Efficient Parallel Learning of Hidden Markov Chain Models on SMPs

    NASA Astrophysics Data System (ADS)

    Li, Lei; Fu, Bin; Faloutsos, Christos

    Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.

  3. Entropy, complexity, and Markov diagrams for random walk cancer models.

    PubMed

    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.

  4. 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.

  5. Simple Estimators for the Simple Latent Class Mastery Testing Model. Twente Educational Memorandum No. 19.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    Latent class models for mastery testing differ from continuum models in that they do not postulate a latent mastery continuum but conceive mastery and non-mastery as two latent classes, each characterized by different probabilities of success. Several researchers use a simple latent class model that is basically a simultaneous application of the…

  6. A Bayesian Model for the Estimation of Latent Interaction and Quadratic Effects When Latent Variables Are Non-Normally Distributed

    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…

  7. 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.

  8. 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…

  9. Self-Organizing Hidden Markov Model Map (SOHMMM).

    PubMed

    Ferles, Christos; Stafylopatis, Andreas

    2013-12-01

    A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. PMID:24001407

  10. Learning atomic human actions using variable-length Markov models.

    PubMed

    Liang, Yu-Ming; Shih, Sheng-Wen; Shih, Arthur Chun-Chieh; Liao, Hong-Yuan Mark; Lin, Cheng-Chung

    2009-02-01

    Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.

  11. Markov models of molecular kinetics: generation and validation.

    PubMed

    Prinz, Jan-Hendrik; Wu, Hao; Sarich, Marco; Keller, Bettina; Senne, Martin; Held, Martin; Chodera, John D; Schütte, Christof; Noé, Frank

    2011-05-01

    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.

  12. A Dynamic Model for Induced Reactivation of Latent Virus

    PubMed Central

    Kepler, G.M.; Nguyen, H.K.; Webster-Cyriaque, J.; Banks, H.T.

    2007-01-01

    We develop a deterministic mathematical model to describe reactivation of latent virus by chemical inducers. This model is applied to the reactivation of latent KSHV in BCBL-1 cell cultures with butyrate as the inducing agent. Parameters for the model are first estimated from known properties of the exponentially growing, uninduced cell cultures. Additional parameters that are necessary to describe induction are determined from fits to experimental data from the literature. Our initial model provides good agreement with two independent sets of experimental data, but also points to the need for a new class of experiments which are required for further understanding of the underlying mechanisms. PMID:17045614

  13. Active Inference for Binary Symmetric Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Allahverdyan, Armen E.; Galstyan, Aram

    2015-10-01

    We consider active maximum a posteriori (MAP) inference problem for hidden Markov models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states. We focus on the binary symmetric HMM, and employ its known mapping to 1d Ising model in random fields. From the statistical physics viewpoint, the active MAP inference problem reduces to analyzing the ground state of the 1d Ising model under modified external fields. We develop an analytical approach and obtain a closed form solution that relates the expected error reduction to model parameters under the specified active inference scheme. We then use this solution to determine most optimal active inference scheme in terms of error reduction, and examine the relation of those schemes to heuristic principles of uncertainty reduction and solution unicity.

  14. Markov modulated Poisson process models incorporating covariates for rainfall intensity.

    PubMed

    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.

  15. A hidden Markov model for space-time precipitation

    SciTech Connect

    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.

  16. Educational Aspirations: Markov and Poisson Models. Rural Industrial Development Project Working Paper Number 14, August 1971.

    ERIC Educational Resources Information Center

    Kayser, Brian D.

    The fit of educational aspirations of Illinois rural high school youths to 3 related one-parameter mathematical models was investigated. The models used were the continuous-time Markov chain model, the discrete-time Markov chain, and the Poisson distribution. The sample of 635 students responded to questionnaires from 1966 to 1969 as part of an…

  17. 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)

  18. A Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

    ERIC Educational Resources Information Center

    Bauer, Daniel J.

    2005-01-01

    To date, finite mixtures of structural equation models (SEMMs) have been developed and applied almost exclusively for the purpose of providing model-based cluster analyses. This type of analysis constitutes a direct application of the model wherein the estimated component distributions of the latent classes are thought to represent the…

  19. 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…

  20. The Effect of Sample Size on Latent Growth Models.

    ERIC Educational Resources Information Center

    Hamilton, Jennifer; Gagne, Phillip E.; Hancock, Gregory R.

    A Monte Carlo simulation approach was taken to investigate the effect of sample size on a variety of latent growth models. A fully balanced experimental design was implemented, with samples drawn from multivariate normal populations specified to represent 12 unique growth models. The models varied factorially by crossing number of time points,…

  1. Evaluating Latent Variable Growth Models through Ex Post Simulation.

    ERIC Educational Resources Information Center

    Kaplan, David; George, Rani

    1998-01-01

    The use of ex post (historical) simulation statistics as means of evaluating latent growth models is considered, and a variety of simulation quality statistics are applied to such models. Results illustrate the importance of using these measures as adjuncts to more traditional forms of model evaluation. (SLD)

  2. 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…

  3. Understanding eye movements in face recognition using hidden Markov models.

    PubMed

    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.

  4. Perspective: Markov models for long-timescale biomolecular dynamics

    SciTech Connect

    Schwantes, C. R.; McGibbon, R. T.; Pande, V. S.

    2014-09-07

    Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.

  5. Perspective: Markov models for long-timescale biomolecular dynamics

    PubMed Central

    Schwantes, C. R.; McGibbon, R. T.; Pande, V. S.

    2014-01-01

    Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics. PMID:25194354

  6. A coupled hidden Markov model for disease interactions.

    PubMed

    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. PMID:24223436

  7. A coupled hidden Markov model for disease interactions.

    PubMed

    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.

  8. 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.

  9. Phase unwrapping using region-based markov random field model.

    PubMed

    Dong, Ying; Ji, Jim

    2010-01-01

    Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method.

  10. Phase unwrapping using region-based markov random field model.

    PubMed

    Dong, Ying; Ji, Jim

    2010-01-01

    Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method. PMID:21096819

  11. Spatial Markov model of anomalous transport through random lattice networks.

    PubMed

    Kang, Peter K; Dentz, Marco; Le Borgne, Tanguy; Juanes, Ruben

    2011-10-28

    Flow through lattice networks with quenched disorder exhibits a strong correlation in the velocity field, even if the link transmissivities are uncorrelated. This feature, which is a consequence of the divergence-free constraint, induces anomalous transport of passive particles carried by the flow. We propose a Lagrangian statistical model that takes the form of a continuous time random walk with correlated velocities derived from a genuinely multidimensional Markov process in space. The model captures the anomalous (non-Fickian) longitudinal and transverse spreading, and the tail of the mean first-passage time observed in the Monte Carlo simulations of particle transport. We show that reproducing these fundamental aspects of transport in disordered systems requires honoring the correlation in the Lagrangian velocity.

  12. 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.

  13. Semi-Nonparametric Methods for Detecting Latent Non-Normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

    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,…

  14. Identifying Seismicity Levels via Poisson Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Orfanogiannaki, K.; Karlis, D.; Papadopoulos, G. A.

    2010-08-01

    Poisson Hidden Markov models (PHMMs) are introduced to model temporal seismicity changes. In a PHMM the unobserved sequence of states is a finite-state Markov chain and the distribution of the observation at any time is Poisson with rate depending only on the current state of the chain. Thus, PHMMs allow a region to have varying seismicity rate. We applied the PHMM to model earthquake frequencies in the seismogenic area of Killini, Ionian Sea, Greece, between period 1990 and 2006. Simulations of data from the assumed model showed that it describes quite well the true data. The earthquake catalogue is dominated by main shocks occurring in 1993, 1997 and 2002. The time plot of PHMM seismicity states not only reproduces the three seismicity clusters but also quantifies the seismicity level and underlies the degree of strength of the serial dependence of the events at any point of time. Foreshock activity becomes quite evident before the three sequences with the gradual transition to states of cascade seismicity. Traditional analysis, based on the determination of highly significant changes of seismicity rates, failed to recognize foreshocks before the 1997 main shock due to the low number of events preceding that main shock. Then, PHMM has better performance than traditional analysis since the transition from one state to another does not only depend on the total number of events involved but also on the current state of the system. Therefore, PHMM recognizes significant changes of seismicity soon after they start, which is of particular importance for real-time recognition of foreshock activities and other seismicity changes.

  15. Heat-transfer coefficients in agitated vessels. Latent heat models

    SciTech Connect

    Kumpinsky, E.

    1996-03-01

    Latent heat models were developed to calculate heat-transfer coefficients in agitated vessels for two cases: (1) heating with a condensable fluid flowing through coils and jackets; (2) vacuum reflux cooling with an overhead condenser. In either case the mathematical treatment, based on macroscopic balances, requires no iterative schemes. In addition to providing heat-transfer coefficients, the models predict flow rates of service fluid through the coils and jackets, estimate the percentage of heat transfer due to latent heat, and compute reflux rates.

  16. 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…

  17. Latent Partially Ordered Classification Models and Normal Mixtures

    ERIC Educational Resources Information Center

    Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith

    2013-01-01

    Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…

  18. Markov Model of Accident Progression at Fukushima Daiichi

    SciTech Connect

    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.

  19. Modeling Hubble Space Telescope flight data by Q-Markov cover identification

    NASA Astrophysics Data System (ADS)

    Liu, K.; Skelton, R. E.; Sharkey, J. P.

    1994-03-01

    This paper presents a state-space model for the Hubble Space Telescope under the influence of unknown disturbances in orbit. This model was obtained from flight data by applying the Q-Markov Covariance Equivalent Realization (Q-Markov Cover) identification algorithm. This state-space model guarantees the match of the first Q-Markov parameters and covariance parameters of the Hubble system. The flight data were partitioned into high- and low-frequency components for more efficient Q-Markov Cover modeling to reduce some computational difficulties of the Q-Markov Cover algorithm. This identification revealed more than 20 lightly damped modes within the bandwidth of the attitude control system. Comparisons with the analytical (TREETOPS) model are also included.

  20. Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

    NASA Astrophysics Data System (ADS)

    Andreão, Rodrigo Varejão; Boudy, Jérôme

    2006-12-01

    This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.

  1. Markov Chain Monte-Carlo Models of Starburst Clusters

    NASA Astrophysics Data System (ADS)

    Melnick, Jorge

    2015-01-01

    There are a number of stochastic effects that must be considered when comparing models to observations of starburst clusters: the IMF is never fully populated; the stars can never be strictly coeval; stars rotate and their photometric properties depend on orientation; a significant fraction of massive stars are in interacting binaries; and the extinction varies from star to star. The probability distributions of each of these effects are not a priori known, but must be extracted from the observations. Markov Chain Monte-Carlo methods appear to provide the best statistical approach. Here I present an example of stochastic age effects upon the upper mass limit of the IMF of the Arches cluster as derived from near-IR photometry.

  2. Vision-based road detection by hidden Markov model

    NASA Astrophysics Data System (ADS)

    Wang, Yanqing; Chen, Deyun; Tao, Liyuan; Shi, Chaoxia

    2009-07-01

    A novel vision-based road detection method was proposed in this paper to realize visual guiding navigation for ground mobile vehicles (GMV). The original image captured by single camera was first segmented into the road region and nonroad region by using an adaptive threshold segmentation algorithm named OTSU. Subsequently, the Canny edges extracted in grey images would be filtered in the road region so that the road boundary could be recognized accurately among those disturbances caused by other edges existed in the image. In order to improve the performance of road detection, the dynamics of GMV and the Hidden Markov Model (HMM) was taken into account to associate the possible road boundary at different time step. The method proposed in this paper was robust against strong shadows, surface dilapidation and illumination variations. It has been tested on real GMV and performed well in real road environments.

  3. 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.

  4. Nonparametric model validations for hidden Markov models with applications in financial econometrics

    PubMed Central

    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

  5. 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…

  6. Interactions of Latent Variables in Structural Equation Models.

    ERIC Educational Resources Information Center

    Bollen, Kenneth A.; Paxton, Pamela

    1998-01-01

    Provides a discussion of an alternative two-stage least squares (2SLS) technique to include interactions of latent variables in structural equation models. The method requires selection of instrumental variables, and rules for selection are presented. An empirical example and Statistical Analysis System programs are presented. (SLD)

  7. 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…

  8. Application of Latent Trait Models to Identifying Substantively Interesting Raters

    ERIC Educational Resources Information Center

    Wolfe, Edward W.; McVay, Aaron

    2012-01-01

    Historically, research focusing on rater characteristics and rating contexts that enable the assignment of accurate ratings and research focusing on statistical indicators of accurate ratings has been conducted by separate communities of researchers. This study demonstrates how existing latent trait modeling procedures can identify groups of…

  9. Identification and classification of conopeptides using profile Hidden Markov Models.

    PubMed

    Laht, Silja; Koua, Dominique; Kaplinski, Lauris; Lisacek, Frédérique; Stöcklin, Reto; Remm, Maido

    2012-03-01

    Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families. PMID:22244925

  10. Identification and classification of conopeptides using profile Hidden Markov Models.

    PubMed

    Laht, Silja; Koua, Dominique; Kaplinski, Lauris; Lisacek, Frédérique; Stöcklin, Reto; Remm, Maido

    2012-03-01

    Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families.

  11. Comparing quantum versus Markov random walk models of judgements measured by rating scales

    PubMed Central

    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

  12. Modeling age-of-onset: Cox model with latent major gene effects

    SciTech Connect

    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.

  13. Ensemble bayesian model averaging using markov chain Monte Carlo sampling

    SciTech Connect

    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.

  14. Incorporating dose-rate effects in Markov radiation cell survival models.

    PubMed

    Sachs, R K; Hlatky, L; Hahnfeldt, P; Chen, P L

    1990-11-01

    Markov models for the survival of cells subjected to ionizing radiation take stochastic fluctuations into account more systematically than do non-Markov counterparts. Albright's Markov RMR (repair-misrepair) model (Radiat. Res. 118, 1-20, 1989) and Curtis's Markov LPL (lethal-potentially lethal) model [in Quantitative Mathematical Models in Radiation Biology (J. Kiefer, Ed.), pp. 127-146. Springer, New York, 1989], which assume acute irradiation, are here generalized to finite dose rates. Instead of treating irradiation as an instantaneous event we introduce an irradiation period T and analyze processes during the interval T as well as afterward. Albright's RMR transition matrix is used throughout for computing the time development of repair and misrepair. During irradiation an additional matrix is added to describe the evolving radiation damage. Albright's and Curtis's Markov models are recovered as limiting cases by taking T----0 with total dose fixed; the opposite limit, of low dose rates, is also analyzed. Deviations from Poisson behavior in the statistical distributions of lesions are calculated. Other continuous-time Markov chain models ("compartmental models") are discussed briefly, for example, models which incorporate cell proliferation and saturable repair models. It is found that for low dose rates the Markov RMR and LPL models give lower survivals compared to the original non-Markov versions. For acute irradiation and high doses, the Markov models predict higher survivals. In general, theoretical extrapolations which neglect some random fluctuations have a systematic bias toward overoptimism when damage to irradiated tumors is compared with damage to surrounding tissues. PMID:2247602

  15. Accelerating Monte Carlo Markov chains with proxy and error models

    NASA Astrophysics Data System (ADS)

    Josset, Laureline; Demyanov, Vasily; Elsheikh, Ahmed H.; Lunati, Ivan

    2015-12-01

    In groundwater modeling, Monte Carlo Markov Chain (MCMC) simulations are often used to calibrate aquifer parameters and propagate the uncertainty to the quantity of interest (e.g., pollutant concentration). However, this approach requires a large number of flow simulations and incurs high computational cost, which prevents a systematic evaluation of the uncertainty in the presence of complex physical processes. To avoid this computational bottleneck, we propose to use an approximate model (proxy) to predict the response of the exact model. Here, we use a proxy that entails a very simplified description of the physics with respect to the detailed physics described by the "exact" model. The error model accounts for the simplification of the physical process; and it is trained on a learning set of realizations, for which both the proxy and exact responses are computed. First, the key features of the set of curves are extracted using functional principal component analysis; then, a regression model is built to characterize the relationship between the curves. The performance of the proposed approach is evaluated on the Imperial College Fault model. We show that the joint use of the proxy and the error model to infer the model parameters in a two-stage MCMC set-up allows longer chains at a comparable computational cost. Unnecessary evaluations of the exact responses are avoided through a preliminary evaluation of the proposal made on the basis of the corrected proxy response. The error model trained on the learning set is crucial to provide a sufficiently accurate prediction of the exact response and guide the chains to the low misfit regions. The proposed methodology can be extended to multiple-chain algorithms or other Bayesian inference methods. Moreover, FPCA is not limited to the specific presented application and offers a general framework to build error models.

  16. A hidden Markov model for multimodal biometrics score fusion

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng

    2011-05-01

    There are strong evidences of that multimodal biometric score fusion can significantly improve human identification performance. Score level fusion usually involves score normalization, score fusion, and fusion decision. There are several types of score fusion methods, direct combination of fusion scores, classifier-based fusion, and density-based fusion. The real applications require achieving greater reliability in determining or verifying person's identity. The goal of this research is to improve the accuracy and robustness of human identification by using multimodal biometrics score fusion. The accuracy means high verification rate if tested on a closed dataset, or a high genuine accept rate under low false accept rate if tested on an open dataset. While the robustness means the fusion performance is stable with variant biometric scores. We propose a hidden Markov model (HMM) for multiple score fusion, where the biometric scores include multimodal scores and multi-matcher scores. The state probability density functions in a HHM model are estimated by Gaussian mixture model. The proposed HMM model for multiple score fusion is accurate for identification, flexible and reliable with biometrics. The proposed HMM method are tested on three NIST-BSSR1 multimodal databases and on three face-score databases. The results show the HMM method is an excellent and reliable score fusion method.

  17. 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…

  18. A comparison of weighted ensemble and Markov state model methodologies.

    PubMed

    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.

  19. Decoding coalescent hidden Markov models in linear time

    PubMed Central

    Harris, Kelley; Sheehan, Sara; Kamm, John A.; Song, Yun S.

    2014-01-01

    In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent HMMs can quickly become prohibitive. Here we present a new algorithm for reducing the runtime of coalescent HMMs from quadratic in the number of hidden time states to linear, without making any additional approximations. Our algorithm can be incorporated into various coalescent HMMs, including the popular method PSMC for inferring variable effective population sizes. Here we implement this algorithm to speed up our demographic inference method diCal, which is equivalent to PSMC when applied to a sample of two haplotypes. We demonstrate that the linear-time method can reconstruct a population size change history more accurately than the quadratic-time method, given similar computation resources. We also apply the method to data from the 1000 Genomes project, inferring a high-resolution history of size changes in the European population. PMID:25340178

  20. Markov modeling of ion channels: implications for understanding disease.

    PubMed

    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.

  1. Recognition of surgical skills using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Speidel, Stefanie; Zentek, Tom; Sudra, Gunther; Gehrig, Tobias; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

    2009-02-01

    Minimally invasive surgery is a highly complex medical discipline and can be regarded as a major breakthrough in surgical technique. A minimally invasive intervention requires enhanced motor skills to deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To recognize and analyze the current situation for context-aware assistance, we need intraoperative sensor data and a model of the intervention. Characteristics of a situation are the performed activity, the used instruments, the surgical objects and the anatomical structures. Important information about the surgical activity can be acquired by recognizing the surgical gesture performed. Surgical gestures in minimally invasive surgery like cutting, knot-tying or suturing are here referred to as surgical skills. We use the motion data from the endoscopic instruments to classify and analyze the performed skill and even use it for skill evaluation in a training scenario. The system uses Hidden Markov Models (HMM) to model and recognize a specific surgical skill like knot-tying or suturing with an average recognition rate of 92%.

  2. 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.

  3. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    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.

  4. Ensemble hidden Markov models with application to landmine detection

    NASA Astrophysics Data System (ADS)

    Hamdi, Anis; Frigui, Hichem

    2015-12-01

    We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models' outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM. Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model's parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

  5. A clustering approach for estimating parameters of a profile hidden Markov model.

    PubMed

    Aghdam, Rosa; Pezeshk, Hamid; Malekpour, Seyed Amir; Shemehsavar, Soudabeh; Eslahchi, Changiz

    2013-01-01

    A Profile Hidden Markov Model (PHMM) is a standard form of a Hidden Markov Models used for modeling protein and DNA sequence families based on multiple alignment. In this paper, we implement Baum-Welch algorithm and the Bayesian Monte Carlo Markov Chain (BMCMC) method for estimating parameters of small artificial PHMM. In order to improve the prediction accuracy of the estimation of the parameters of the PHMM, we classify the training data using the weighted values of sequences in the PHMM then apply an algorithm for estimating parameters of the PHMM. The results show that the BMCMC method performs better than the Maximum Likelihood estimation. PMID:23865165

  6. 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.

  7. Mapping eQTL Networks with Mixed Graphical Markov Models

    PubMed Central

    Tur, Inma; Roverato, Alberto; Castelo, Robert

    2014-01-01

    Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. PMID:25271303

  8. Optical character recognition of handwritten Arabic using hidden Markov models

    SciTech Connect

    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.

  9. A Network of SCOP Hidden Markov Models and Its Analysis

    PubMed Central

    2011-01-01

    Background The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before. Results In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components. Conclusions The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs. PMID:21635719

  10. 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.

  11. Supervised learning of hidden Markov models for sequence discrimination

    SciTech Connect

    Mamitsuka, Hiroshi

    1997-12-01

    We present two supervised learning algorithms for hidden Markov models (HMMs) for sequence discrimination. When we model a class of sequences with an HMM, conventional learning algorithms for HMMs have trained the HMM with training examples belonging to the class, i.e. positive examples alone, while both of our methods allow us to use negative examples as well as positive examples. One of our algorithms minimizes a kind of distance between a target likelihood of a given training sequence and an actual likelihood of the sequence, which is obtained by a given HMM, using an additive type of parameter updating based on a gradient-descent learning. The other algorithm maximizes a criterion which represents a kind of ratio of the likelihood of a positive example to the likelihood of the total example, using a multiplicative type of parameter updating which is more efficient in actual computation time than the additive type one. We compare our two methods with two conventional methods on a type of cross-validation of actual motif classification experiments. Experimental results show that in terms of the average number of classification errors, our two methods out-perform the two conventional algorithms. 14 refs., 4 figs., 1 tab.

  12. 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…

  13. High-order hidden Markov model for piecewise linear processes and applications to speech recognition.

    PubMed

    Lee, Lee-Min; Jean, Fu-Rong

    2016-08-01

    The hidden Markov models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov model for piecewise linear processes is proposed to better approximate the behavior of a real process. A parameter estimation method based on the expectation-maximization algorithm was derived for the proposed model. Experiments on speech recognition of noisy Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate compared to a baseline hidden Markov model. PMID:27586781

  14. Modeling Hubble Space Telescope flight data by Q-Markov cover identification

    NASA Astrophysics Data System (ADS)

    Liu, K.; Skelton, R. E.; Sharkey, J. P.

    A state space model for the Hubble Space Telescope under the influence of unknown disturbances in orbit is presented. This model was obtained from flight data by applying the Q-Markov covariance equivalent realization identification algorithm. This state space model guarantees the match of the first Q-Markov parameters and covariance parameters of the Hubble system. The flight data were partitioned into high- and low-frequency components for more efficient Q-Markov cover modeling, to reduce some computational difficulties of the Q-Markov cover algorithm. This identification revealed more than 20 lightly damped modes within the bandwidth of the attitude control system. Comparisons with the analytical (TREETOPS) model are also included.

  15. Hidden Markov chain modeling for epileptic networks identification.

    PubMed

    Le Cam, Steven; Louis-Dorr, Valérie; Maillard, Louis

    2013-01-01

    The partial epileptic seizures are often considered to be caused by a wrong balance between inhibitory and excitatory interneuron connections within a focal brain area. These abnormal balances are likely to result in loss of functional connectivities between remote brain structures, while functional connectivities within the incriminated zone are enhanced. The identification of the epileptic networks underlying these hypersynchronies are expected to contribute to a better understanding of the brain mechanisms responsible for the development of the seizures. In this objective, threshold strategies are commonly applied, based on synchrony measurements computed from recordings of the electrophysiologic brain activity. However, such methods are reported to be prone to errors and false alarms. In this paper, we propose a hidden Markov chain modeling of the synchrony states with the aim to develop a reliable machine learning methods for epileptic network inference. The method is applied on a real Stereo-EEG recording, demonstrating consistent results with the clinical evaluations and with the current knowledge on temporal lobe epilepsy. PMID:24110697

  16. ENSO informed Drought Forecasting Using Nonhomogeneous Hidden Markov Chain Model

    NASA Astrophysics Data System (ADS)

    Kwon, H.; Yoo, J.; Kim, T.

    2013-12-01

    The study aims at developing a new scheme to investigate the potential use of ENSO (El Niño/Southern Oscillation) for drought forecasting. In this regard, objective of this study is to extend a previously developed nonhomogeneous hidden Markov chain model (NHMM) to identify climate states associated with drought that can be potentially used to forecast drought conditions using climate information. As a target variable for forecasting, SPI(standardized precipitation index) is mainly utilized. This study collected monthly precipitation data over 56 stations that cover more than 30 years and K-means cluster analysis using drought properties was applied to partition regions into mutually exclusive clusters. In this study, six main clusters were distinguished through the regionalization procedure. For each cluster, the NHMM was applied to estimate the transition probability of hidden states as well as drought conditions informed by large scale climate indices (e.g. SOI, Nino1.2, Nino3, Nino3.4, MJO and PDO). The NHMM coupled with large scale climate information shows promise as a technique for forecasting drought scenarios. A more detailed explanation of large scale climate patterns associated with the identified hidden states will be provided with anomaly composites of SSTs and SLPs. Acknowledgement This research was supported by a grant(11CTIPC02) from Construction Technology Innovation Program (CTIP) funded by Ministry of Land, Transport and Maritime Affairs of Korean government.

  17. 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.

  18. Modeling healthcare data using multiple-channel latent Dirichlet allocation.

    PubMed

    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.

  19. Modeling healthcare data using multiple-channel latent Dirichlet allocation.

    PubMed

    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. PMID:26898516

  20. Technical manual for basic version of the Markov chain nest productivity model (MCnest)

    EPA Science Inventory

    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...

  1. User’s manual for basic version of MCnest Markov chain nest productivity model

    EPA Science Inventory

    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...

  2. The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

    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…

  3. Modeling Coordination in Multiple Simultaneous Latent Change Scores

    PubMed Central

    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

  4. Latent Hierarchical Model of Temporal Structure for Complex Activity Classification.

    PubMed

    Wang, Limin; Qiao, Yu; Tang, Xiaoou

    2014-02-01

    Modeling the temporal structure of sub-activities is an important yet challenging problem in complex activity classification. This paper proposes a latent hierarchical model (LHM) to describe the decomposition of complex activity into sub-activities in a hierarchical way. The LHM has a tree-structure, where each node corresponds to a video segment (sub-activity) at certain temporal scale. The starting and ending time points of each sub-activity are represented by two latent variables, which are automatically determined during the inference process. We formulate the training problem of the LHM in a latent kernelized SVM framework and develop an efficient cascade inference method to speed up classification. The advantages of our methods come from: 1) LHM models the complex activity with a deep structure, which is decomposed into sub-activities in a coarse-to-fine manner and 2) the starting and ending time points of each segment are adaptively determined to deal with the temporal displacement and duration variation of sub-activity. We conduct experiments on three datasets: 1) the KTH; 2) the Hollywood2; and 3) the Olympic Sports. The experimental results show the effectiveness of the LHM in complex activity classification. With dense features, our LHM achieves the state-of-the-art performance on the Hollywood2 dataset and the Olympic Sports dataset.

  5. Detecting Mixtures from Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

    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…

  6. Latent log-linear models for handwritten digit classification.

    PubMed

    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.

  7. Group association test using a hidden Markov model.

    PubMed

    Cheng, Yichen; Dai, James Y; Kooperberg, Charles

    2016-04-01

    In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome. PMID:26420797

  8. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis.

    PubMed

    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.

  9. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis.

    PubMed

    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. PMID:23085467

  10. Latent sensitization: a model for stress-sensitive chronic pain.

    PubMed

    Marvizon, Juan Carlos; Walwyn, Wendy; Minasyan, Ani; Chen, Wenling; Taylor, Bradley K

    2015-01-01

    Latent sensitization is a rodent model of chronic pain that reproduces both its episodic nature and its sensitivity to stress. It is triggered by a wide variety of injuries ranging from injection of inflammatory agents to nerve damage. It follows a characteristic time course in which a hyperalgesic phase is followed by a phase of remission. The hyperalgesic phase lasts between a few days to several months, depending on the triggering injury. Injection of μ-opioid receptor inverse agonists (e.g., naloxone or naltrexone) during the remission phase induces reinstatement of hyperalgesia. This indicates that the remission phase does not represent a return to the normal state, but rather an altered state in which hyperalgesia is masked by constitutive activity of opioid receptors. Importantly, stress also triggers reinstatement. Here we describe in detail procedures for inducing and following latent sensitization in its different phases in rats and mice. PMID:25829356

  11. Latent Sensitization: a model for stress-sensitive chronic pain

    PubMed Central

    Marvizon, Juan Carlos; Walwyn, Wendy; Minasyan, Ani; Chen, Wenling; Taylor, Bradley K.

    2015-01-01

    Latent sensitization is a rodent model of chronic pain that reproduces both its episodic nature and its sensitivity to stress. It is triggered by a wide variety of injuries ranging from injection of inflammatory agents to nerve damage. It follows a characteristic time course in which a hyperalgesic phase is followed by a phase of remission. The hyperalgesic phase lasts between a few days to several months, depending of the triggering injury. Injection of μ-opioid receptor inverse agonists (i.e., naloxone, naltrexone) during the remission phase induces reinstatement of hyperalgesia. This indicates that the remission phase does not represent a return to the normal state, but rather an altered state in which hyperalgesia is masked by constitutive activity of opioid receptors. Importantly, stress also triggers reinstatement. Here we describe in detail the procedures to induce and follow latent sensitization in its different phases in rats and mice. PMID:25829356

  12. Markov Model of Severe Accident Progression and Management

    SciTech Connect

    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.

  13. Redefining CpG islands using hidden Markov models.

    PubMed

    Wu, Hao; Caffo, Brian; Jaffee, Harris A; Irizarry, Rafael A; Feinberg, Andrew P

    2010-07-01

    The DNA of most vertebrates is depleted in CpG dinucleotide: a C followed by a G in the 5' to 3' direction. CpGs are the target for DNA methylation, a chemical modification of cytosine (C) heritable during cell division and the most well-characterized epigenetic mechanism. The remaining CpGs tend to cluster in regions referred to as CpG islands (CGI). Knowing CGI locations is important because they mark functionally relevant epigenetic loci in development and disease. For various mammals, including human, a readily available and widely used list of CGI is available from the UCSC Genome Browser. This list was derived using algorithms that search for regions satisfying a definition of CGI proposed by Gardiner-Garden and Frommer more than 20 years ago. Recent findings, enabled by advances in technology that permit direct measurement of epigenetic endpoints at a whole-genome scale, motivate the need to adapt the current CGI definition. In this paper, we propose a procedure, guided by hidden Markov models, that permits an extensible approach to detecting CGI. The main advantage of our approach over others is that it summarizes the evidence for CGI status as probability scores. This provides flexibility in the definition of a CGI and facilitates the creation of CGI lists for other species. The utility of this approach is demonstrated by generating the first CGI lists for invertebrates, and the fact that we can create CGI lists that substantially increases overlap with recently discovered epigenetic marks. A CGI list and the probability scores, as a function of genome location, for each species are available at http://www.rafalab.org. PMID:20212320

  14. A Markov regression random-effects model for remission of functional disability in patients following a first stroke: a Bayesian approach.

    PubMed

    Pan, Shin-Liang; Wu, Hui-Min; Yen, Amy Ming-Fang; Chen, Tony Hsiu-Hsi

    2007-12-20

    Few attempts have been made to model the dynamics of stroke-related disability. It is possible though, using panel data and multi-state Markov regression models that incorporate measured covariates and latent variables (random effects). This study aimed to model a series of functional transitions (following a first stroke) using a three-state Markov model with or without considering random effects. Several proportional hazards parameterizations were considered. A Bayesian approach that utilizes the Markov Chain Monte Carlo (MCMC) and Gibbs sampling functionality of WinBUGS (a Windows-based Bayesian software package) was developed to generate the marginal posterior distributions of the various transition parameters (e.g. the transition rates and transition probabilities). Model building and comparisons was guided by reference to the deviance information criteria (DIC). Of the four proportional hazards models considered, exponential regression was preferred because it led to the smallest deviances. Adding random effects further improved the model fit. Of the covariates considered, only age, infarct size, and baseline functional status were significant. By using our final model we were able to make individual predictions about functional recovery in stroke patients. PMID:17676712

  15. Using Cox cluster processes to model latent pulse location patterns in hormone concentration data.

    PubMed

    Carlson, Nichole E; Grunwald, Gary K; Johnson, Timothy D

    2016-04-01

    Many hormones, including stress hormones, are intermittently secreted as pulses. The pulsatile location process, describing times when pulses occur, is a regulator of the entire stress system. Characterizing the pulse location process is particularly difficult because the pulse locations are latent; only hormone concentration at sampled times is observed. In addition, for stress hormones the process may change both over the day and relative to common external stimuli. This potentially results in clustering in pulse locations across subjects. Current approaches to characterizing the pulse location process do not capture subject-to-subject clustering in locations. Here we show how a Bayesian Cox cluster process may be adapted as a model of the pulse location process. We show that this novel model of pulse locations is capable of detecting circadian rhythms in pulse locations, clustering of pulse locations between subjects, and identifying exogenous controllers of pulse events. We integrate our pulse location process into a model of hormone concentration, the observed data. A spatial birth-and-death Markov chain Monte Carlo algorithm is used for estimation. We exhibit the strengths of this model on simulated data and adrenocorticotropic and cortisol data collected to study the stress axis in depressed and non-depressed women. PMID:26553914

  16. Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data.

    PubMed

    Bermingham, Mairead L; Handel, Ian G; Glass, Elizabeth J; Woolliams, John A; de Clare Bronsvoort, B Mark; McBride, Stewart H; Skuce, Robin A; Allen, Adrian R; McDowell, Stanley W J; Bishop, Stephen C

    2015-07-07

    Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

  17. Computationally tractable stochastic image modeling based on symmetric Markov mesh random fields.

    PubMed

    Yousefi, Siamak; Kehtarnavaz, Nasser; Cao, Yan

    2013-06-01

    In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced image model comprises the product of several local 1D density and 2D joint density functions of pixels in an image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for image modeling purposes compared to the conventional Markov random field models.

  18. Prediction of User's Web-Browsing Behavior: Application of Markov Model.

    PubMed

    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. PMID:22394580

  19. MARKOV Model Application to Proliferation Risk Reduction of an Advanced Nuclear System

    SciTech Connect

    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.

  20. Reliability analysis and prediction of mixed mode load using Markov Chain Model

    NASA Astrophysics Data System (ADS)

    Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.

    2014-06-01

    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.

  1. Reliability analysis and prediction of mixed mode load using Markov Chain Model

    SciTech Connect

    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.

  2. 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.

  3. Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework

    NASA Astrophysics Data System (ADS)

    Humblot, Fabrice; Mohammad-Djafari, Ali

    2006-12-01

    This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.

  4. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor.

    PubMed

    van den Hout, Ardo; Fox, Jean-Paul; Klein Entink, Rinke H

    2015-12-01

    Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991-2005). PMID:22080595

  5. Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor

    PubMed Central

    Fox, Jean-Paul; Klein Entink, Rinke H

    2015-01-01

    Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005). PMID:22080595

  6. 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…

  7. Dimensionality of the Latent Structure and Item Selection via Latent Class Multidimensional IRT Models

    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…

  8. Projected metastable Markov processes and their estimation with observable operator models

    SciTech Connect

    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.

  9. Projected metastable Markov processes and their estimation with observable operator models

    NASA Astrophysics Data System (ADS)

    Wu, Hao; Prinz, Jan-Hendrik; Noé, Frank

    2015-10-01

    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.

  10. A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation.

    PubMed

    van Rosmalen, Joost; Toy, Mehlika; O'Mahony, James F

    2013-08-01

    Markov models are a simple and powerful tool for analyzing the health and economic effects of health care interventions. These models are usually evaluated in discrete time using cohort analysis. The use of discrete time assumes that changes in health states occur only at the end of a cycle period. Discrete-time Markov models only approximate the process of disease progression, as clinical events typically occur in continuous time. The approximation can yield biased cost-effectiveness estimates for Markov models with long cycle periods and if no half-cycle correction is made. The purpose of this article is to present an overview of methods for evaluating Markov models in continuous time. These methods use mathematical results from stochastic process theory and control theory. The methods are illustrated using an applied example on the cost-effectiveness of antiviral therapy for chronic hepatitis B. The main result is a mathematical solution for the expected time spent in each state in a continuous-time Markov model. It is shown how this solution can account for age-dependent transition rates and discounting of costs and health effects, and how the concept of tunnel states can be used to account for transition rates that depend on the time spent in a state. The applied example shows that the continuous-time model yields more accurate results than the discrete-time model but does not require much computation time and is easily implemented. In conclusion, continuous-time Markov models are a feasible alternative to cohort analysis and can offer several theoretical and practical advantages. PMID:23715464

  11. Stochastic model of homogeneous coding and latent periodicity in DNA sequences.

    PubMed

    Chaley, Maria; Kutyrkin, Vladimir

    2016-02-01

    The concept of latent triplet periodicity in coding DNA sequences which has been earlier extensively discussed is confirmed in the result of analysis of a number of eukaryotic genomes, where latent periodicity of a new type, called profile periodicity, is recognized in the CDSs. Original model of Stochastic Homogeneous Organization of Coding (SHOC-model) in textual string is proposed. This model explains the existence of latent profile periodicity and regularity in DNA sequences. PMID:26656186

  12. 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.

  13. Ancestral population genomics using coalescence hidden Markov models and heuristic optimisation algorithms.

    PubMed

    Cheng, Jade Yu; Mailund, Thomas

    2015-08-01

    With full genome data from several closely related species now readily available, we have the ultimate data for demographic inference. Exploiting these full genomes, however, requires models that can explicitly model recombination along alignments of full chromosomal length. Over the last decade a class of models, based on the sequential Markov coalescence model combined with hidden Markov models, has been developed and used to make inference in simple demographic scenarios. To move forward to more complex demographic modelling we need better and more automated ways of specifying these models and efficient optimisation algorithms for inferring the parameters in complex and often high-dimensional models. In this paper we present a framework for building such coalescence hidden Markov models for pairwise alignments and present results for using heuristic optimisation algorithms for parameter estimation. We show that we can build more complex demographic models than our previous frameworks and that we obtain more accurate parameter estimates using heuristic optimisation algorithms than when using our previous gradient based approaches. Our new framework provides a flexible way of constructing coalescence hidden Markov models almost automatically. While estimating parameters in more complex models is still challenging we show that using heuristic optimisation algorithms we still get a fairly good accuracy.

  14. Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

    None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

  15. Extended GT-STAF information indices based on Markov approximation models

    NASA Astrophysics Data System (ADS)

    Barigye, Stephen J.; Marrero-Ponce, Yovani; Alfonso-Reguera, Vitalio; Pérez-Giménez, Facundo

    2013-05-01

    A series of novel information theory-based molecular parameters derived from the insight of a molecular structure as a chemical communication system were recently presented and usefully employed in QSAR/QSPRs (J. Comp. Chem, 2013, 34, 259; SAR and QSAR in Environ. Res. 2013, 24). This approach permitted the application of Shannon's source and channel coding entropic measures to a chemical information source comprised of molecular 'fragments', using the zero-order Markov approximation model (atom-based approach). This report covers the theoretical aspects of the extensions of this approach to higher-order models, introducing the first, second and generalized-order Markov approximation models.

  16. A Bayesian method for construction of Markov models to describe dynamics on various time-scales

    NASA Astrophysics Data System (ADS)

    Rains, Emily K.; Andersen, Hans C.

    2010-10-01

    The dynamics of many biological processes of interest, such as the folding of a protein, are slow and complicated enough that a single molecular dynamics simulation trajectory of the entire process is difficult to obtain in any reasonable amount of time. Moreover, one such simulation may not be sufficient to develop an understanding of the mechanism of the process, and multiple simulations may be necessary. One approach to circumvent this computational barrier is the use of Markov state models. These models are useful because they can be constructed using data from a large number of shorter simulations instead of a single long simulation. This paper presents a new Bayesian method for the construction of Markov models from simulation data. A Markov model is specified by (τ,P,T), where τ is the mesoscopic time step, P is a partition of configuration space into mesostates, and T is an NP×NP transition rate matrix for transitions between the mesostates in one mesoscopic time step, where NP is the number of mesostates in P. The method presented here is different from previous Bayesian methods in several ways. (1) The method uses Bayesian analysis to determine the partition as well as the transition probabilities. (2) The method allows the construction of a Markov model for any chosen mesoscopic time-scale τ. (3) It constructs Markov models for which the diagonal elements of T are all equal to or greater than 0.5. Such a model will be called a "consistent mesoscopic Markov model" (CMMM). Such models have important advantages for providing an understanding of the dynamics on a mesoscopic time-scale. The Bayesian method uses simulation data to find a posterior probability distribution for (P,T) for any chosen τ. This distribution can be regarded as the Bayesian probability that the kinetics observed in the atomistic simulation data on the mesoscopic time-scale τ was generated by the CMMM specified by (P,T). An optimization algorithm is used to find the most probable

  17. Latent Tuberculosis Infection: Myths, Models, and Molecular Mechanisms

    PubMed Central

    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

  18. Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models.

    PubMed

    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.

  19. Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models.

    PubMed

    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. PMID:23237214

  20. 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.

  1. 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.

  2. Enhancement of Markov chain model by integrating exponential smoothing: A case study on Muslims marriage and divorce

    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.

  3. Modelling Faculty Replacement Strategies Using a Time-Dependent Finite Markov-Chain Process.

    ERIC Educational Resources Information Center

    Hackett, E. Raymond; Magg, Alexander A.; Carrigan, Sarah D.

    1999-01-01

    Describes the use of a time-dependent Markov-chain model to develop faculty-replacement strategies within a college at a research university. The study suggests that a stochastic modelling approach can provide valuable insight when planning for personnel needs in the immediate (five-to-ten year) future. (MSE)

  4. 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…

  5. 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,…

  6. Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

    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…

  7. 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…

  8. 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.

  9. Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management.

    PubMed

    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.

  10. The algebra of the general Markov model on phylogenetic trees and networks.

    PubMed

    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.

  11. Markov Modeling of Component Fault Growth over a Derived Domain of Feasible Output Control Effort Modifications

    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.

  12. Reliability Analysis of the Electrical Control System of Subsea Blowout Preventers Using Markov Models

    PubMed Central

    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

  13. Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

    PubMed Central

    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. PMID:25013937

  14. Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

    PubMed

    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.

  15. (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.

  16. 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…

  17. Using Design-Based Latent Growth Curve Modeling with Cluster-Level Predictor to Address Dependency

    ERIC Educational Resources Information Center

    Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L.

    2014-01-01

    The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…

  18. 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…

  19. Recent Changes Leading to Subsequent Changes: Extensions of Multivariate Latent Difference Score Models

    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 bivariate…

  20. Software for the Application of Discrete Latent Structure Models to Item Response Data.

    ERIC Educational Resources Information Center

    Haertel, Edward H.

    These FORTRAN programs and MATHEMATICA routines were developed in the course of a research project titled "Achievement and Assessment in School Science: Modeling and Mapping Ability and Performance." Their use is described in other publications from that project, including "Latent Traits or Latent States? The Role of Discrete Models for Ability…

  1. 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…

  2. A Latent Model for Prioritization of SNPs for Functional Studies

    PubMed Central

    Fridley, Brooke L.; Iversen, Ed; Tsai, Ya-Yu; Jenkins, Gregory D.; Goode, Ellen L.; Sellers, Thomas A.

    2011-01-01

    One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial/time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model (BLVM) for incorporating “features” about a SNP to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study (GWAS). In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases/traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2nd and 7th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value (which ranked 197th for invasive case analysis), was ranked 8th based on the posterior probability of being in the top 5 markers (0.13). In summary, the application of the BLVM allows for the systematic integration of multiple SNP “features” for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking. PMID:21687685

  3. Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Deni, Sayang Mohd; Jemain, Abdul Aziz; Ibrahim, Kamarulzaman

    2009-06-01

    The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.

  4. An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

    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…

  5. Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules.

    PubMed

    Noé, Frank; Wu, Hao; Prinz, Jan-Hendrik; Plattner, Nuria

    2013-11-14

    Markov state models (MSMs) have been successful in computing metastable states, slow relaxation timescales and associated structural changes, and stationary or kinetic experimental observables of complex molecules from large amounts of molecular dynamics simulation data. However, MSMs approximate the true dynamics by assuming a Markov chain on a clusters discretization of the state space. This approximation is difficult to make for high-dimensional biomolecular systems, and the quality and reproducibility of MSMs has, therefore, been limited. Here, we discard the assumption that dynamics are Markovian on the discrete clusters. Instead, we only assume that the full phase-space molecular dynamics is Markovian, and a projection of this full dynamics is observed on the discrete states, leading to the concept of Projected Markov Models (PMMs). Robust estimation methods for PMMs are not yet available, but we derive a practically feasible approximation via Hidden Markov Models (HMMs). It is shown how various molecular observables of interest that are often computed from MSMs can be computed from HMMs/PMMs. The new framework is applicable to both, simulation and single-molecule experimental data. We demonstrate its versatility by applications to educative model systems, a 1 ms Anton MD simulation of the bovine pancreatic trypsin inhibitor protein, and an optical tweezer force probe trajectory of an RNA hairpin. PMID:24320261

  6. Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules

    NASA Astrophysics Data System (ADS)

    Noé, Frank; Wu, Hao; Prinz, Jan-Hendrik; Plattner, Nuria

    2013-11-01

    Markov state models (MSMs) have been successful in computing metastable states, slow relaxation timescales and associated structural changes, and stationary or kinetic experimental observables of complex molecules from large amounts of molecular dynamics simulation data. However, MSMs approximate the true dynamics by assuming a Markov chain on a clusters discretization of the state space. This approximation is difficult to make for high-dimensional biomolecular systems, and the quality and reproducibility of MSMs has, therefore, been limited. Here, we discard the assumption that dynamics are Markovian on the discrete clusters. Instead, we only assume that the full phase-space molecular dynamics is Markovian, and a projection of this full dynamics is observed on the discrete states, leading to the concept of Projected Markov Models (PMMs). Robust estimation methods for PMMs are not yet available, but we derive a practically feasible approximation via Hidden Markov Models (HMMs). It is shown how various molecular observables of interest that are often computed from MSMs can be computed from HMMs/PMMs. The new framework is applicable to both, simulation and single-molecule experimental data. We demonstrate its versatility by applications to educative model systems, a 1 ms Anton MD simulation of the bovine pancreatic trypsin inhibitor protein, and an optical tweezer force probe trajectory of an RNA hairpin.

  7. Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models.

    PubMed

    Shepard, Samuel S; McSweeny, Andrew; Serpen, Gursel; Fedorov, Alexei

    2012-06-01

    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

  8. 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…

  9. 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)

  10. Treatment-based Markov chain models clarify mechanisms of invasion in an invaded grassland community.

    PubMed

    Nelis, Lisa Castillo; Wootton, J Timothy

    2010-02-22

    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.

  11. Using higher-order Markov models to reveal flow-based communities in networks

    PubMed Central

    Salnikov, Vsevolod; Schaub, Michael T.; Lambiotte, Renaud

    2016-01-01

    Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where we can uncover communities shaped by the temporal correlations in the system. Finally, we discuss relations of the framework of second order Markov processes and the recently proposed formalism of using non-backtracking matrices for community detection. PMID:27029508

  12. Treatment-based Markov chain models clarify mechanisms of invasion in an invaded grassland community

    PubMed Central

    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

  13. 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...

  14. Avian life history profiles for use in the Markov chain nest productivity model (MCnest)

    EPA Science Inventory

    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...

  15. A systematic review of Markov models evaluating multicomponent disease management programs in diabetes.

    PubMed

    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. PMID:26535605

  16. A systematic review of Markov models evaluating multicomponent disease management programs in diabetes.

    PubMed

    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.

  17. Joint modeling of ChIP-seq data via a Markov random field model.

    PubMed

    Bao, Yanchun; Vinciotti, Veronica; Wit, Ernst; 't Hoen, Peter A C

    2014-04-01

    Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.

  18. Identifying gene regulatory network rewiring using latent differential graphical models.

    PubMed

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-09-30

    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.

  19. Identifying gene regulatory network rewiring using latent differential graphical models

    PubMed Central

    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

  20. Model Selection Information Criteria for Non-Nested Latent Class Models.

    ERIC Educational Resources Information Center

    Lin, Ting Hsiang; Dayton, C. Mitchell

    1997-01-01

    The use of these three model selection information criteria for latent class models was studied for nonnested models: (1) Akaike's information criterion (H. Akaike, 1973) (AIC); (2) the Schwarz information (G. Schwarz, 1978) (SIC) criterion; and (3) the Bozdogan version of the AIC (CAIC) (H. Bozdogan, 1987). Situations in which each is preferable…

  1. On the Relation between the Linear Factor Model and the Latent Profile Model

    ERIC Educational Resources Information Center

    Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

    2011-01-01

    The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

  2. Ancestry inference in complex admixtures via variable-length Markov chain linkage models.

    PubMed

    Rodriguez, Jesse M; Bercovici, Sivan; Elmore, Megan; Batzoglou, Serafim

    2013-03-01

    Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral haplotypes. In this paper we introduce ALLOY, an efficient method that incorporates generalized, but highly expressive, linkage disequilibrium models. ALLOY applies a factorial hidden Markov model to capture the parallel process producing the maternal and paternal admixed haplotypes, and models the background linkage disequilibrium in the ancestral populations via an inhomogeneous variable-length Markov chain. We test ALLOY in a broad range of scenarios ranging from recent to ancient admixtures with up to four ancestral populations. We show that ALLOY outperforms the previous state of the art, and is robust to uncertainties in model parameters. PMID:23421795

  3. Bayesian Methods for Analyzing Structural Equation Models with Covariates, Interaction, and Quadratic Latent Variables

    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…

  4. 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…

  5. 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…

  6. 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…

  7. Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects

    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…

  8. Longitudinal Data Analysis with Latent Growth Modeling: An Introduction and Illustration for Higher Education Researchers

    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…

  9. 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…

  10. The optimum order of a Markov chain model for daily rainfall in Nigeria

    NASA Astrophysics Data System (ADS)

    Jimoh, O. D.; Webster, P.

    1996-11-01

    Markov type models are often used to describe the occurrence of daily rainfall. Although models of Order 1 have been successfully employed, there remains uncertainty concerning the optimum order for such models. This paper is concerned with estimation of the optimum order of Markov chains and, in particular, the use of objective criteria of the Akaike and Bayesian Information Criteria (AIC and BIC, respectively). Using daily rainfall series for five stations in Nigeria, it has been found that the AIC and BIC estimates vary with month as well as the value of the rainfall threshold used to define a wet day. There is no apparent system to this variation, although AIC estimates are consistently greater than or equal to BIC estimates, with values of the latter limited to zero or unity. The optimum order is also investigated through generation of synthetic sequences of wet and dry days using the transition matrices of zero-, first- and second-order Markov chains. It was found that the first-order model is superior to the zero-order model in representing the characteristics of the historical sequence as judged using frequency duration curves. There was no discernible difference between the model performance for first- and second-order models. There was no seasonal varation in the model performance, which contrasts with the optimum models identified using AIC and BIC estimates. It is concluded that caution is needed with the use of objective criteria for determining the optimum order of the Markov model and that the use of frequency duration curves can provide a robust alternative method of model identification. Comments are also made on the importance of record length and non-stationarity for model identification

  11. Entity Relation Detection with Factorial Hidden Markov Models and Maximum Entropy Discriminant Latent Dirichlet Allocations

    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…

  12. Modeling HIV persistence, the latent reservoir, and viral blips

    PubMed Central

    Rong, Libin; Perelson, Alan S.

    2009-01-01

    HIV-1 eradication from infected individuals has not been achieved with the use of highly active antiretroviral therapy (HAART) for a prolonged period of time. The cellular reservoir for HIV-1 in resting memory CD4+ T cells remains a major obstacle to viral elimination. The reservoir does not decay significantly over long periods of time but is able to release replication-competent HIV-1 upon cell activation. Residual ongoing viral replication may likely occur in many patients because low levels of virus can be detected in plasma by sensitive assays and transient episodes of viremia, or HIV-1 blips, are often observed in patients even with successful viral suppression for many years. Here we review our current knowledge of the factors contributing to viral persistence, the latent reservoir, and blips, and mathematical models developed to explore them and their relationships. We show how mathematical modeling can help improve our understanding of HIV-1 dynamics in patients on HAART and of the quantitative events underlying HIV-1 latency, reservoir stability, low-level viremic persistence, and emergence of intermittent viral blips. We also discuss treatment implications related to these studies. PMID:19539630

  13. 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

  14. Preserving spatial linear correlations between neighboring stations in simulating daily precipitation using extended Markov models

    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.

  15. Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models.

    PubMed

    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.

  16. Markov models and the ensemble Kalman filter for estimation of sorption rates.

    SciTech Connect

    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.

  17. Segmentation of brain tumors in 4D MR images using the hidden Markov model.

    PubMed

    Solomon, Jeffrey; Butman, John A; Sood, Arun

    2006-12-01

    Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone. PMID:17050032

  18. Transition dynamics of HIV disease in a cohort of African prostitutes: a Markov model approach.

    PubMed

    Nagelkerke, N J; Plummer, F A; Holton, D; Anzala, A O; Manji, F; Ngugi, E N; Moses, S

    1990-08-01

    The progression of HIV-related disease from infection to death is represented as a staged Markov model. Transitions between stages are considered reversible. The model is fitted to data from a cohort of African prostitutes by means of maximum likelihood. It appears that the progression to symptomatic disease (Centers for Disease Control stage IV) in this population is considerably more rapid than that reported from studies in Western countries. PMID:2175619

  19. Transition dynamics of HIV disease in a cohort of African prostitutes: a Markov model approach.

    PubMed

    Nagelkerke, N J; Plummer, F A; Holton, D; Anzala, A O; Manji, F; Ngugi, E N; Moses, S

    1990-08-01

    The progression of HIV-related disease from infection to death is represented as a staged Markov model. Transitions between stages are considered reversible. The model is fitted to data from a cohort of African prostitutes by means of maximum likelihood. It appears that the progression to symptomatic disease (Centers for Disease Control stage IV) in this population is considerably more rapid than that reported from studies in Western countries.

  20. Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirus.

    PubMed

    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. PMID

  1. A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models.

    PubMed

    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.

  2. Characterization of Caenorhabditis elegans behavior in response to chemical stress by using hidden Markov model

    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.

  3. Hidden Markov models and other machine learning approaches in computational molecular biology

    SciTech Connect

    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.

  4. Hidden Markov Model analysis of force/torque information in telemanipulation

    SciTech Connect

    Hannaford, B. ); Lee, P. )

    1991-10-01

    A new model is developed for prediction and analysis of sensor information recorded during robotic performance of tasks by telemanipulation. The model uses the Hidden Markov Model (stochastic functions of Markov nets; HMM) to describe the task structure, the operator or intelligent controller's goal structure, and the sensor signals such as forces and torques arising from interaction with the environment. The Markov process portion encodes the task sequence/subgoal structure, and the observation densities associated with each subgoal state encode the expected sensor signals associated with carrying out that subgoal. Methodology is described for construction of the model parameters based on engineering knowledge of the task. The Viterbi algorithm is used for model based analysis of force signals measured during experimental teleoperation and achieves excellent segmentation of the data into subgoal phases. The Baum-Welch algorithm is used to identify the most likely HMM from a given experiment. The HMM achieves a structured, knowledge-base model with explicit uncertainties and mature, optimal identification algorithms.

  5. 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

  6. 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.

  7. Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

    PubMed Central

    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

  8. Estimation of the occurrence rate of strong earthquakes based on hidden semi-Markov models

    NASA Astrophysics Data System (ADS)

    Votsi, I.; Limnios, N.; Tsaklidis, G.; Papadimitriou, E.

    2012-04-01

    The present paper aims at the application of hidden semi-Markov models (HSMMs) in an attempt to reveal key features for the earthquake generation, associated with the actual stress field, which is not accessible to direct observation. The models generalize the hidden Markov models by considering the hidden process to form actually a semi-Markov chain. Considering that the states of the models correspond to levels of actual stress fields, the stress field level at the occurrence time of each strong event is revealed. The dataset concerns a well catalogued seismically active region incorporating a variety of tectonic styles. More specifically, the models are applied in Greece and its surrounding lands, concerning a complete data sample with strong (M≥ 6.5) earthquakes that occurred in the study area since 1845 up to present. The earthquakes that occurred are grouped according to their magnitudes and the cases of two and three magnitude ranges for a corresponding number of states are examined. The parameters of the HSMMs are estimated and their confidence intervals are calculated based on their asymptotic behavior. The rate of the earthquake occurrence is introduced through the proposed HSMMs and its maximum likelihood estimator is calculated. The asymptotic properties of the estimator are studied, including the uniformly strongly consistency and the asymptotical normality. The confidence interval for the proposed estimator is given. We assume the state space of both the observable and the hidden process to be finite, the hidden Markov chain to be homogeneous and stationary and the observations to be conditionally independent. The hidden states at the occurrence time of each strong event are revealed and the rate of occurrence of an anticipated earthquake is estimated on the basis of the proposed HSMMs. Moreover, the mean time for the first occurrence of a strong anticipated earthquake is estimated and its confidence interval is calculated.

  9. Synthesis of cervical tissue second harmonic generation images using Markov random field modeling.

    PubMed

    Yousefi, S; Kehtarnavaz, N; Gholipour, A

    2011-01-01

    This paper presents a statistical image modeling approach based on Markov random field to synthesize cervical tissue second harmonic generation (SHG) images. Binary images representing fiber and pore areas of the cervix tissue are first obtained from SHG images using an image processing pipeline consisting of noise removal, contrast enhancement and optimal thresholding. These binary images are modeled using a Markov random field whose parameters are estimated via the least squares method. The parameters are then used to synthesize fiber and pore areas of cervical tissue in the form of binary images. The effectiveness of the synthesis is demonstrated by reporting the classification outcome for two classes of cervical SHG images collected from mice at two different stages of normal pregnancy. The developed synthesis allows generation of realistic fiber and pore area binary images for cervical tissue studies.

  10. 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.

  11. Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

    PubMed Central

    Heath, S C

    1997-01-01

    A new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits. PMID:9326339

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

    PubMed

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

    2014-02-01

    In many biological applications it is necessary to cluster DNA sequences into groups that represent underlying organismal units, such as named species or genera. In metagenomics this grouping needs typically to be achieved on the basis of relatively short sequences which contain different types of errors, making the use of a statistical modeling approach desirable. Here we introduce a novel method for this purpose by developing a stochastic partition model that clusters Markov chains of a given order. The model is based on a Dirichlet process prior and we use conjugate priors for the Markov chain parameters which enables an analytical expression for comparing the marginal likelihoods of any two partitions. To find a good candidate for the posterior mode in the partition space, we use a hybrid computational approach which combines the EM-algorithm with a greedy search. This is demonstrated to be faster and yield highly accurate results compared to earlier suggested clustering methods for the metagenomics application. Our model is fairly generic and could also be used for clustering of other types of sequence data for which Markov chains provide a reasonable way to compress information, as illustrated by experiments on shotgun sequence type data from an Escherichia coli strain. PMID:24246289

  13. Regularized Deterministic Annealing Hidden Markov Models for Identificationand Analysis of Seismic and Aseismic events.

    NASA Astrophysics Data System (ADS)

    Granat, R. A.; Clayton, R.; Kedar, S.; Kaneko, Y.

    2003-12-01

    We employ a robust hidden Markov model (HMM) based technique to perform statistical pattern analysis of suspected seismic and aseismic events in the poorly explored period band of minutes to hours. The technique allows us to classify known events and provides a statistical basis for finding and cataloging similar events represented elsewhere in the observations. In this work, we focus on data collected by the Southern California TriNet system. The hidden Markov model (HMM) approach assumes that the observed data has been generated by an unobservable dynamical statistical process. The process is of a particular form such that each observation is coincident with the system being in a particular discrete state. The dynamics are the model are constructed so that the next state is directly dependent only on the current state -- it is a first order Markov process. The model is completely described by a set of parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probability distribution of observable outputs associated with each state. Application of the model to data involves optimizing these model parameters with respect to some function of the observations, typically the likelihood of the observations given the model. Our work focused on the fact that this objective function has a number of local maxima that is exponential in the model size (the number of states). This means that not only is it very difficult to discover the global maximum, but also that results can vary widely between applications of the model. For some domains which employ HMMs for such purposes, such as speech processing, sufficient a priori information about the system is available to avoid this problem. However, for seismic data in general such a priori information is not available. Our approach involves analytical location of sub-optimal local maxima; once the locations of these maxima have been found, then we can employ a

  14. Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin

    2010-10-01

    In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

  15. 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.

  16. A method of hidden Markov model optimization for use with geophysical data sets

    NASA Technical Reports Server (NTRS)

    Granat, R. A.

    2003-01-01

    Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

  17. Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models.

    PubMed

    Ding, Cherng G; Jane, Ten-Der

    2012-09-01

    In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified.

  18. A new approach to simulating stream isotope dynamics using Markov switching autoregressive models

    NASA Astrophysics Data System (ADS)

    Birkel, Christian; Paroli, Roberta; Spezia, Luigi; Dunn, Sarah M.; Tetzlaff, Doerthe; Soulsby, Chris

    2012-09-01

    In this study we applied Markov switching autoregressive models (MSARMs) as a proof-of-concept to analyze the temporal dynamics and statistical characteristics of the time series of two conservative water isotopes, deuterium (δ2H) and oxygen-18 (δ18O), in daily stream water samples over two years in a small catchment in eastern Scotland. MSARMs enabled us to explicitly account for the identified non-linear, non-Normal and non-stationary isotope dynamics of both time series. The hidden states of the Markov chain could also be associated with meteorological and hydrological drivers identifying the short (event) and longer-term (inter-event) transport mechanisms for both isotopes. Inference was based on the Bayesian approach performed through Markov Chain Monte Carlo algorithms, which also allowed us to deal with a high rate of missing values (17%). Although it is usually assumed that both isotopes are conservative and exhibit similar dynamics, δ18O showed somewhat different time series characteristics. Both isotopes were best modelled with two hidden states, but δ18O demanded autoregressions of the first order, whereas δ2H of the second. Moreover, both the dynamics of observations and the hidden states of the two isotopes were explained by two different sets of covariates. Consequently use of the two tracers for transit time modelling and hydrograph separation may result in different interpretations on the functioning of a catchment system.

  19. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

    NASA Astrophysics Data System (ADS)

    Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

    2014-09-01

    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.

  20. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

    SciTech Connect

    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.

  1. 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…

  2. 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…

  3. A model for the latent heat of melting in free standing metal nanoparticles

    SciTech Connect

    Shin, Jeong-Heon; Deinert, Mark R.

    2014-04-28

    Nanoparticles of many metals are known to exhibit scale dependent latent heats of melting. Analytical models for this phenomenon have so far failed to completely capture the observed phenomena. Here we present a thermodynamic analysis for the melting of metal nanoparticles in terms of their internal energy and a scale dependent surface tension proposed by Tolman. The resulting model predicts the scale dependence of the latent heat of melting and is confirmed using published data for tin and aluminum.

  4. 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.

  5. Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

    PubMed

    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.

  6. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

    PubMed Central

    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

  7. Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

    PubMed

    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

  8. A Markov Model for Assessing the Reliability of a Digital Feedwater Control System

    SciTech Connect

    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.

  9. Separable Markov random field model and its applications in low level vision.

    PubMed

    Sun, Jian; Tappen, Marshall F

    2013-01-01

    This brief proposes a continuously-valued Markov random field (MRF) model with separable filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF modeling. In this framework, we design a novel gradient-based discriminative learning method to learn the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and fast image demosaicing with high-quality results.

  10. 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.

  11. 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,…

  12. Protein modeling with hybrid Hidden Markov Model/Neurel network architectures

    SciTech Connect

    Baldi, P.; Chauvin, Y.

    1995-12-31

    Hidden Markov Models (HMMs) are useful in a number of tasks in computational molecular biology, and in particular to model and align protein families. We argue that HMMs are somewhat optimal within a certain modeling hierarchy. Single first order HMMs, however, have two potential limitations: a large number of unstructured parameters, and a built-in inability to deal with long-range dependencies. Hybrid HMM/Neural Network (NN) architectures attempt to overcome these limitations. In hybrid HMM/NN, the HMM parameters are computed by a NN. This provides a reparametrization that allows for flexible control of model complexity, and incorporation of constraints. The approach is tested on the immunoglobulin family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs. To capture dependencies, however, one must resort to a larger hybrid model class, where the data is modeled by multiple HMMs. The parameters of the HMMs, and their modulation as a function of input or context, is again calculated by a NN.

  13. An amino acid model for latent fingerprints on porous surfaces.

    PubMed

    Schwarz, Lothar

    2009-11-01

    Analytical standards are needed in latent fingerprint detection for research and development as well as for quality control in routine work because normal fingerprints are too varied for comparison studies and tests. One way is to create latent fingerprints. For the amino acid sensitive detection method this can be achieved by coating test items with an amino acid solution using a modified commercial office bubble jet printer. Besides low costs, fast and easy preparation, the main advantage of a bubble jet printer is that the amino acid loading per area on the test item can be calculated by weighing the cartridge on a balance. This opens the possibility to determine the deviation for every printing series. The reproducibility of prints in a printing series made by one cartridge has a deviation of 2-16% and of prints made by different cartridges 20-25%.

  14. 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…

  15. Bayesian Modeling of Time Trends in Component Reliability Data via Markov Chain Monte Carlo Simulation

    SciTech Connect

    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.

  16. A segmentation model using compound Markov random fields based on a boundary model.

    PubMed

    Wu, Jue; Chung, Albert C S

    2007-01-01

    Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

  17. A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology

    PubMed Central

    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

  18. A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology.

    PubMed

    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.

  19. Markov-Tree model of intrinsic transport in Hamiltonian systems

    NASA Technical Reports Server (NTRS)

    Meiss, J. D.; Ott, E.

    1985-01-01

    A particle in a chaotic region of phase space can spend a long time near the boundary of a regular region since transport there is slow. This 'stickiness' of regular regions is thought to be responsible for previous observations in numerical experiments of a long-time algebraic decay of the particle survival probability, i.e., survival probability approximately t to the (-z) power for large t. This paper presents a global model for transport in such systems and demonstrates the essential role of the infinite hierarchy of small islands interspersed in the chaotic region. Results for z are discussed.

  20. On the use of hidden Markov models for gaze pattern modeling

    NASA Astrophysics Data System (ADS)

    Mannaru, Pujitha; Balasingam, Balakumar; Pattipati, Krishna; Sibley, Ciara; Coyne, Joseph

    2016-05-01

    Some of the conventional metrics derived from gaze patterns (on computer screens) to study visual attention, engagement and fatigue are saccade counts, nearest neighbor index (NNI) and duration of dwells/fixations. Each of these metrics has drawbacks in modeling the behavior of gaze patterns; one such drawback comes from the fact that some portions on the screen are not as important as some other portions on the screen. This is addressed by computing the eye gaze metrics corresponding to important areas of interest (AOI) on the screen. There are some challenges in developing accurate AOI based metrics: firstly, the definition of AOI is always fuzzy; secondly, it is possible that the AOI may change adaptively over time. Hence, there is a need to introduce eye-gaze metrics that are aware of the AOI in the field of view; at the same time, the new metrics should be able to automatically select the AOI based on the nature of the gazes. In this paper, we propose a novel way of computing NNI based on continuous hidden Markov models (HMM) that model the gazes as 2D Gaussian observations (x-y coordinates of the gaze) with the mean at the center of the AOI and covariance that is related to the concentration of gazes. The proposed modeling allows us to accurately compute the NNI metric in the presence of multiple, undefined AOI on the screen in the presence of intermittent casual gazing that is modeled as random gazes on the screen.

  1. Markov-switching multifractal models as another class of random-energy-like models in one-dimensional space

    NASA Astrophysics Data System (ADS)

    Saakian, David B.

    2012-03-01

    We map the Markov-switching multifractal model (MSM) onto the random energy model (REM). The MSM is, like the REM, an exactly solvable model in one-dimensional space with nontrivial correlation functions. According to our results, four different statistical physics phases are possible in random walks with multifractal behavior. We also introduce the continuous branching version of the model, calculate the moments, and prove multiscaling behavior. Different phases have different multiscaling properties.

  2. A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

    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

  3. 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.

  4. Estimating parameters of hidden Markov models based on marked individuals: use of robust design data

    USGS Publications Warehouse

    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).

  5. Estimating parameters of hidden Markov models based on marked individuals: use of robust design data.

    PubMed

    Kendall, William L; White, Gary C; Hines, James E; Langtimm, Catherine A; Yoshizaki, Jun

    2012-04-01

    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 20 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 have also implemented these models in program MARK. This general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within-primary-period parameters (e.g., state structure) and between-primary-period parameters (e.g., state transition probabilities). PMID:22690641

  6. Multivariate Markov processes for stochastic systems with delays: application to the stochastic Gompertz model with delay.

    PubMed

    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.

  7. Alignment of multiple proteins with an ensemble of Hidden Markov Models

    PubMed Central

    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

  8. [Succession caused by beaver (Castor fiber L.) life activity: II. A refined Markov model].

    PubMed

    Logofet; Evstigneev, O I; Aleinikov, A A; Morozova, A O

    2015-01-01

    The refined Markov model of cyclic zoogenic successions caused by beaver (Castor fiber L.) life activity represents a discrete chain of the following six states: flooded forest, swamped forest, pond, grassy swamp, shrubby swamp, and wet forest, which correspond to certain stages of succession. Those stages are defined, and a conceptual scheme of probable transitions between them for one time step is constructed from the knowledge of beaver behaviour in small river floodplains of "Bryanskii Les" Reserve. We calibrated the corresponding matrix of transition probabilities according to the optimization principle: minimizing differences between the model outcome and reality; the model generates a distribution of relative areas corresponding to the stages of succession, that has to be compared to those gained from case studies in the Reserve during 2002-2006. The time step is chosen to equal 2 years, and the first-step data in the sum of differences are given various weights, w (between 0 and 1). The value of w = 0.2 is selected due to its optimality and for some additional reasons. By the formulae of finite homogeneous Markov chain theory, we obtained the main results of the calibrated model, namely, a steady-state distribution of stage areas, indexes of cyclicity, and the mean durations (M(j)) of succession stages. The results of calibration give an objective quantitative nature to the expert knowledge of the course of succession and get a proper interpretation. The 2010 data, which are not involved in the calibration procedure, enabled assessing the quality of prediction by the homogeneous model in short-term (from the 2006 situation): the error of model area distribution relative to the distribution observed in 2010 falls into the range of 9-17%, the best prognosis being given by the least optimal matrices (rejected values of w). This indicates a formally heterogeneous nature of succession processes in time. Thus, the refined version of the homogeneous Markov chain

  9. Robust phase sensitive inversion recovery imaging using a Markov random field model.

    PubMed

    Garach, Ravindra M; Ji, Jim X; Ying, Lei; Ma, Jingfei

    2004-01-01

    This paper presents a novel method for phase sensitive inversion recovery (PSIR) imaging for improved T/sub 1/ contrast. This method models the phase of the complex magnetic resonance image using a statistical model based on Markov random fields. A computationally efficient optimization method is developed. Computer simulations and in-vivo brain imaging experiments show that the proposed method can produce PSIR images with enhanced T/sub 1/ contrast and it is robust against high levels of data noise even when rapid phase variations are presented.

  10. A Markov chain probability model to describe wet and dry patterns of weather at Colombo

    NASA Astrophysics Data System (ADS)

    Sonnadara, D. U. J.; Jayewardene, D. R.

    2015-01-01

    The hypothesis that the wet and dry patterns of daily precipitation observed in Colombo can be modeled by a first order Markov chain model was tested using daily rainfall data for a 60-year period (1941-2000). The probability of a day being wet or dry was defined with respect to the status of the previous day. Probabilities were assumed to be stationary within a given month. Except for isolated single events, the model is shown to describe the observed sequence of wet and dry spells satisfactorily depending on the season. The accuracy of modeling wet spells is high compared to dry spells. When the model-predicted mean length of wet spells for each month was compared with the estimated values from the data set, a reasonable agreement between the model prediction and estimation is seen (within ±0.1). In general, the data show a higher disagreement for the months having longer dry spells. The mean annual duration of wet spells is 2.6 days while the mean annual duration of dry spells is 3.8 days. It is shown that the model can be used to explore the return periods of long wet and dry spells. We conclude from the study that the Markov chain of order 1 is adequate to describe wet and dry patterns of weather in Colombo.

  11. PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models.

    PubMed

    Scherer, Martin K; Trendelkamp-Schroer, Benjamin; Paul, Fabian; Pérez-Hernández, Guillermo; Hoffmann, Moritz; Plattner, Nuria; Wehmeyer, Christoph; Prinz, Jan-Hendrik; Noé, Frank

    2015-11-10

    Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the open-source Python package PyEMMA ( http://pyemma.org ) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA.

  12. Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach.

    PubMed

    Proust-Lima, Cécile; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

    2016-02-10

    Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g., quality of life, functional dependency, and cognitive level). These multivariate and longitudinal data also usually have nonstandard distributions (discrete, asymmetric, bounded, etc.). We propose a joint model based on a latent process and latent classes to analyze simultaneously such multiple longitudinal markers of different natures, and multiple causes of progression. A latent process model describes the latent trait of interest and links it to the observed longitudinal outcomes using flexible measurement models adapted to different types of data, and a latent class structure links the longitudinal and cause-specific survival models. The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of progression based on the repeated marker data are derived. The methodology is validated in a simulation study and applied on real data about cognitive aging obtained from a large population-based study. The aim is to predict the risk of dementia by accounting for the competing death according to the profiles of semantic memory measured by two asymmetric psychometric tests.

  13. 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…

  14. 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…

  15. 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…

  16. A comparative study of Gaussian geostatistical models and Gaussian Markov random field models1

    PubMed Central

    Song, Hae-Ryoung; Fuentes, Montserrat; Ghosh, Sujit

    2008-01-01

    Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GM-RFs) are two distinct approaches commonly used in spatial models for modeling point referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed: (i) the Kullback-Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method. PMID:19337581

  17. High range resolution radar target identification using the Prony model and hidden Markov models

    NASA Astrophysics Data System (ADS)

    Dewitt, Mark R.

    1992-12-01

    Fully polarized Xpatch signatures are transformed to two left circularly polarized signals. These two signals are then filtered by a linear FM pulse compression ('chirp') transfer function, corrupted by AWGN, and filtered by a filter matched to the 'chirp' transfer function. The bandwidth of the 'chirp' radar is about 750 MHz. Range profile feature extraction is performed using the TLS Prony Model parameter estimation technique developed at Ohio State University. Using the Prony Model, each scattering center is described by a polarization ellipse, relative energy, frequency response, and range. This representation of the target is vector quantized using a K-means clustering algorithm. Sequences of vector quantized scattering centers as well as sequences of vector quantized range profiles are used to synthesize target specific Hidden Markov Models (HMM's). The identification decision is made by determining which HMM has the highest probability of generating the unknown sequence. The data consist of synthesized Xpatch signatures of two targets which have been difficult to separate with other RTI algorithms. The RTI algorithm developed is clearly able to separate these two targets over a 10 by 10 degree (1 degree granularity) aspect angle window off the nose for SNR's as low as 0 dB. The classification rate is 100 percent for SNR's of 5 - 20 dB, 95 percent for a SNR of 0 dB and it drops rapidly for SNR's lower than 0 dB.

  18. Automatically inferred Markov network models for classification of chromosomal band pattern structures.

    PubMed

    Granum, E; Thomason, M G

    1990-01-01

    A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard

  19. 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.

  20. Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

    PubMed

    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

  1. 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.

  2. Study of behavior and determination of customer lifetime value(CLV) using Markov chain model

    SciTech Connect

    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.

  3. A new method of automatic processing of seismic waves: waveform modeling by using Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Kodera, Y.; Sakai, S.

    2012-12-01

    Development of a method of automatic processing of seismic waves is needed since there are limitations to manually picking out earthquake events from seismograms. However, there is no practical method to automatically detect arrival times of P and S waves in seismograms. One typical example of previously proposed methods is automatic detection by using AR model (e.g. Kitagawa et al., 2004). This method seems not to be effective for seismograms contaminated with spike noise, because it cannot distinguish non-stationary signals generated by earthquakes from those generated by noise. The difficulty of distinguishing the signals is caused by the fact that the automatic detection system has a lack of information on time series variation of seismic waves. We expect that an automatic detection system that includes the information on seismic waves is more effective for seismograms contaminated with noise. So we try to adapt Hidden Markov Model (HMM) to construct seismic wave models and establish a new automatic detection method. HMM has been widely used in many fields such as voice recognition (e.g. Bishop, 2006). With the use of HMM, P- or S-waveform models that include envelops can be constructed directly and semi-automatically from lots of observed waveform data of P or S waves. These waveform models are expected to become more robust if the quantity of observation data increases. We have constructed seismic wave models based on HMM from seismograms observed in Ashio, Japan. By using these models, we have tried automatic detection of arrival times of earthquake events in Ashio. Results show that automatic detection based on HMM is more effective for seismograms contaminated with noise than that based on AR model.

  4. Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

    PubMed Central

    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

  5. Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution.

    PubMed

    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

  6. 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.

  7. Comparison of the kinetics of different Markov models for ligand binding under varying conditions

    SciTech Connect

    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.

  8. 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.

  9. Modeling of stratigraphic columns using Markov Chains and Gibbs sampling algorithms, Campo Oritupano, Venezuela.

    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.

  10. Distinguishing between Latent Classes and Continuous Factors with Categorical Outcomes: Class Invariance of Parameters of Factor Mixture Models

    ERIC Educational Resources Information Center

    Lubke, Gitta; Neale, Michael

    2008-01-01

    Factor mixture models are latent variable models with categorical and continuous latent variables that can be used as a model-based approach to clustering. A previous article covered the results of a simulation study showing that in the absence of model violations, it is usually possible to choose the correct model when fitting a series of models…

  11. Nakagami Markov random field as texture model for ultrasound RF envelope image.

    PubMed

    Bouhlel, N; Sevestre-Ghalila, S

    2009-06-01

    The aim of this paper is to propose a new Markov random field (MRF) model for the backscattered ultrasonic echo in order to get information about backscatter characteristics, such as the scatterer density, amplitude and spacing. The model combines the Nakagami distribution that describes the envelope of backscattered echo with spatial interaction using MRF. In this paper, the parameters of the model and the estimation parameter method are introduced. Computer simulation using ultrasound radio-frequency (RF) simulator and experiments on choroidal malignant melanoma have been undertaken to test the validity of the model. The relationship between the parameters of MRF model and the backscatter characteristics has been established. Furthermore, the ability of the model to distinguish between normal and abnormal tissue has been proved. All the results can show the success of the model.

  12. 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.

  13. Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

    NASA Astrophysics Data System (ADS)

    Bui, Lam Thu; Barlow, Michael

    We propose a methodology for employing memetics (local search) within the framework of evolutionary algorithms to optimize parameters of hidden markov models. With this proposal, the rate and frequency of using local search are automatically changed over time either at a population or individual level. At the population level, we allow the rate of using local search to decay over time to zero (at the final generation). At the individual level, each individual is equipped with information of when it will do local search and for how long. This information evolves over time alongside the main elements of the chromosome representing the individual.

  14. 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.

  15. An experimental validation of the Gauss-Markov model for nonuniformity noise in infrared focal plane array sensors

    NASA Astrophysics Data System (ADS)

    Zapata, Octavio; Pedreros, Felipe; Torres, Sergio N.

    2012-06-01

    The aim of this research is to experimentally validate a Gauss-Markov model, previously developed by our group, for the non-uniformity parameters of infrared (IR) focal plane arrays (FPAs). The Gauss-Markov model assumed that both, the gain and the offset parameters at each detector, are random state-variables modeled by a recursive discrete-time process. For simplicity, however, we have regarded here the gain parameter as a constant and assumed that solely the offset parameter follows a Gauss-Markov model. Experiments have been conducted at room temperature and IR data was collected from black-body radiator sources using microbolometer-based IR cameras operating in the 8 to 12 μm. Next, well-known statistical techniques were used to analyze the offset time series and determinate whether the Gauss-Markov model truly fits the temporal dynamics of the offset. The validity of the Gauss-Markov model for the offset parameter was tested at two time scales: seconds and minutes. It is worth mentioning that the statistical analysis conducted in this work is a key in providing mechanisms for capturing the drift in the fixed pattern noise parameters.

  16. Interlocking directorates in Irish companies using a latent space model for bipartite networks.

    PubMed

    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. PMID:27247395

  17. Interlocking directorates in Irish companies using a latent space model for bipartite networks.

    PubMed

    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.

  18. Interlocking directorates in Irish companies using a latent space model for bipartite networks

    PubMed Central

    Friel, Nial; Rastelli, Riccardo; Wyse, Jason; Raftery, Adrian E.

    2016-01-01

    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. PMID:27247395

  19. Reciprocal Markov modeling of feedback mechanisms between emotion and dietary choice using experience sampling data

    PubMed Central

    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

  20. A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

    PubMed Central

    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

  1. A Markov model to estimate Salmonella morbidity, mortality, illness duration, and cost.

    PubMed

    Herrick, Robert L; Buchberger, Steven G; Clark, Robert M; Kupferle, Margaret; Murray, Regan; Succop, Paul

    2012-10-01

    Approximately 690000-1790000 Salmonella cases, 20000 hospitalizations, and 400 deaths occur in the USA annually, costing approximately $2.6bn. Existing models estimate morbidity, mortality, and cost solely from incidence. They do not estimate illness duration or use time as an independent cost predictor. Existing models may underestimate physician visits, hospitalizations, deaths, and associated costs. We developed a Markov chain Monte Carlo model to estimate illness duration, physician/emergency room visits, inpatient hospitalizations, mortality, and resultant costs for a given Salmonella incidence. Interested parties include society, third-party payers, health providers, federal, state and local governments, businesses, and individual patients and their families. The marginal approach estimates individual disease behavior for every patient, explicitly estimates disease duration and calculates separate time-dependent costs. The aggregate approach is a Markov equivalent of the existing models; it assumes average disease behavior and cost for a given morbidity/mortality. Transition probabilities were drawn from a meta-analysis of 53 Salmonella studies. Both approaches were tested using the 1993 Salmonella typhimurium outbreak in Gideon, Missouri. This protocol can be applied to estimate morbidity, mortality and cost of specific outbreaks, provide better national Salmonella burden estimates, and estimate the benefits of reducing Salmonella risk.

  2. A Markov model for sequences of ordinal data from a relapsing-remitting disease.

    PubMed

    Albert, P S

    1994-03-01

    Many chronic diseases follow a course with multiple relapses into periods with severe symptoms alternating with periods of remission; experimental allergic encephalomyelitis, the animal model for multiple sclerosis, is an example of such a disease. A finite Markov chain is proposed as a model for analyzing sequences of ordinal data from a relapsing-remitting disease. The proposed model is one in which the state space is expanded to include information about the relapsing-remitting status as well as the ordinal severity score, and a reparameterization is suggested that reduces the number of parameters needed to be estimated. The Markov model allows for a wide range of relapsing-remitting behavior, provides an understanding of the stochastic nature of the disease process, and allows for efficient estimation of important characteristics of the disease course (such as mean first passage times, occupation times, and steady-state probabilities). These methods are applied to data from a study of the effect of a treatment (transforming growth factor-beta 1) on experimental allergic encephalomyelitis. PMID:8086615

  3. Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain.

    PubMed

    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. PMID:21735134

  4. A Model of Young Children's Social Cognition: Linkages Between Latent Structures and Discrete Processing

    ERIC Educational Resources Information Center

    Meece, Darrell

    1999-01-01

    This study proposes a model of associations between young children's social cognition and their social behavior with peers. In this model, two latent structures -children's representations of peer relationships and emotion regulation -- predict children's competent, prosocial, withdrawn, and aggressive behavior. Moreover, the model proposes that…

  5. Structural Equation Models of Latent Interactions: An Appropriate Standardized Solution and Its Scale-Free Properties

    ERIC Educational Resources Information Center

    Wen, Zhonglin; Marsh, Herbert W.; Hau, Kit-Tai

    2010-01-01

    Standardized parameter estimates are routinely used to summarize the results of multiple regression models of manifest variables and structural equation models of latent variables, because they facilitate interpretation. Although the typical standardization of interaction terms is not appropriate for multiple regression models, straightforward…

  6. Multi-aspect target discrimination using hidden Markov models and neural networks.

    PubMed

    Robinson, Marc; Azimi-Sadjadi, Mahmood R; Salazar, Jaime

    2005-03-01

    This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.

  7. 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.

  8. Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.

    PubMed

    de Uña-Álvarez, Jacobo; Meira-Machado, Luís

    2015-06-01

    Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov. Several non-Markov estimators have been proposed in the recent literature, and their superiority with respect to the Aalen-Johansen estimator has been proved in situations in which the Markov condition is strongly violated. However, the existing estimators have the drawback of requiring that the support of the censoring distribution contains the support of the lifetime distribution, which is not often the case. In this article, we propose two new methods for estimating the transition probabilities in the progressive illness-death model. Some asymptotic results are derived. The proposed estimators are consistent regardless the Markov condition and the referred assumption about the censoring support. We explore the finite sample behavior of the estimators through simulations. The main conclusion of this piece of research is that the proposed estimators are much more efficient than the existing non-Markov estimators in most cases. An application to a clinical trial on colon cancer is included. Extensions to progressive processes beyond the three-state illness-death model are discussed.

  9. Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm

    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…

  10. Latent Regression Analysis.

    PubMed

    Tarpey, Thaddeus; Petkova, Eva

    2010-07-01

    Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice distinct sub-populations do not actually exist. For example, disease severity (e.g. depression) may vary continuously and therefore, a distinction of diseased and not-diseased may not be based on the existence of distinct sub-populations. Thus, what is needed is a generalization of the finite mixture's discrete latent predictor to a continuous latent predictor. We cast the finite mixture model as a regression model with a latent Bernoulli predictor. A latent regression model is proposed by replacing the discrete Bernoulli predictor by a continuous latent predictor with a beta distribution. Motivation for the latent regression model arises from applications where distinct latent classes do not exist, but instead individuals vary according to a continuous latent variable. The shapes of the beta density are very flexible and can approximate the discrete Bernoulli distribution. Examples and a simulation are provided to illustrate the latent regression model. In particular, the latent regression model is used to model placebo effect among drug treated subjects in a depression study. PMID:20625443

  11. Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model

    PubMed Central

    Zhang, Yu-Chen; Zhang, Shao-Wu; Liu, Lian; Liu, Hui; Zhang, Lin; Cui, Xiaodong; Huang, Yufei; Meng, Jia

    2015-01-01

    With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset. PMID:26301253

  12. Markov models of breast tumor progression: some age-specific results.

    PubMed

    Duffy, S W; Day, N E; Tabár, L; Chen, H H; Smith, T C

    1997-01-01

    Researchers have noted that mammographic screening has a reduced effect on breast cancer mortality in women in their forties compared to older women. Explanations for this include poorer sensitivity in younger women due to denser breast tissue, as well as more rapid tumor progression, giving a shorter mean sojourn time (the average duration of the preclinical screen-detectable period). To test these hypotheses, we developed a series of Markov-chain models to estimate tumor progression rates and sensitivity. Parameters were estimated using tumor data from the Swedish two-county trial of mammographic screening for breast cancer. The mean sojourn time was shorter in women aged 40-49 compared to women aged 50-59 and 60-69 (2.44, 3.70, and 4.17 years, respectively). Sensitivity was lower in the 40-49 age group compared to the two older groups (83%, 100%, and 100%, respectively). Thus, both rapid progression and poorer sensitivity are associated with the 40-49 age group. We also modeled tumor size, node status, and malignancy grade together with subsequent breast cancer mortality and found that, to achieve a reduction in mortality commensurate with that in women over 50, the interscreening interval for women in their forties should be less than two years. We conclude that Markov models and the use of tumor size, node status, and malignancy grade as surrogates for mortality can be useful in design and analysis of future studies of breast cancer screening.

  13. 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.

  14. Improved Hidden Markov Models for Molecular Motors, Part 2: Extensions and Application to Experimental Data

    PubMed Central

    Syed, Sheyum; Müllner, Fiona E.; Selvin, Paul R.; Sigworth, Fred J.

    2010-01-01

    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. PMID:21112294

  15. Markov models of breast tumor progression: some age-specific results.

    PubMed

    Duffy, S W; Day, N E; Tabár, L; Chen, H H; Smith, T C

    1997-01-01

    Researchers have noted that mammographic screening has a reduced effect on breast cancer mortality in women in their forties compared to older women. Explanations for this include poorer sensitivity in younger women due to denser breast tissue, as well as more rapid tumor progression, giving a shorter mean sojourn time (the average duration of the preclinical screen-detectable period). To test these hypotheses, we developed a series of Markov-chain models to estimate tumor progression rates and sensitivity. Parameters were estimated using tumor data from the Swedish two-county trial of mammographic screening for breast cancer. The mean sojourn time was shorter in women aged 40-49 compared to women aged 50-59 and 60-69 (2.44, 3.70, and 4.17 years, respectively). Sensitivity was lower in the 40-49 age group compared to the two older groups (83%, 100%, and 100%, respectively). Thus, both rapid progression and poorer sensitivity are associated with the 40-49 age group. We also modeled tumor size, node status, and malignancy grade together with subsequent breast cancer mortality and found that, to achieve a reduction in mortality commensurate with that in women over 50, the interscreening interval for women in their forties should be less than two years. We conclude that Markov models and the use of tumor size, node status, and malignancy grade as surrogates for mortality can be useful in design and analysis of future studies of breast cancer screening. PMID:9709283

  16. Markov chain modelling of reliability analysis and prediction under mixed mode loading

    NASA Astrophysics Data System (ADS)

    Singh, Salvinder; Abdullah, Shahrum; Nik Mohamed, Nik Abdullah; Mohd Noorani, Mohd Salmi

    2015-03-01

    The reliability assessment for an automobile crankshaft provides an important understanding in dealing with the design life of the component in order to eliminate or reduce the likelihood of failure and safety risks. The failures of the crankshafts are considered as a catastrophic failure that leads towards a severe failure of the engine block and its other connecting subcomponents. The reliability of an automotive crankshaft under mixed mode loading using the Markov Chain Model is studied. The Markov Chain is modelled by using a two-state condition to represent the bending and torsion loads that would occur on the crankshaft. The automotive crankshaft represents a good case study of a component under mixed mode loading due to the rotating bending and torsion stresses. An estimation of the Weibull shape parameter is used to obtain the probability density function, cumulative distribution function, hazard and reliability rate functions, the bathtub curve and the mean time to failure. The various properties of the shape parameter is used to model the failure characteristic through the bathtub curve is shown. Likewise, an understanding of the patterns posed by the hazard rate onto the component can be used to improve the design and increase the life cycle based on the reliability and dependability of the component. The proposed reliability assessment provides an accurate, efficient, fast and cost effective reliability analysis in contrast to costly and lengthy experimental techniques.

  17. Assessing convergence of Markov chain Monte Carlo simulations in hierarchical Bayesian models for population pharmacokinetics.

    PubMed

    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

  18. Statistical identification with hidden Markov models of large order splitting strategies in an equity market

    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.

  19. Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models

    PubMed Central

    2014-01-01

    Background Logos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position. Results We present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position. Conclusion Skylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign’s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org. PMID:24410852

  20. HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test.

    PubMed

    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. PMID:26854292

  1. Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models

    NASA Astrophysics Data System (ADS)

    Lindner, Benjamin; Yi, Zheng; Prinz, Jan-Hendrik; Smith, Jeremy C.; Noé, Frank

    2013-11-01

    The dynamics of complex molecules can be directly probed by inelastic neutron scattering experiments. However, many of the underlying dynamical processes may exist on similar timescales, which makes it difficult to assign processes seen experimentally to specific structural rearrangements. Here, we show how Markov models can be used to connect structural changes observed in molecular dynamics simulation directly to the relaxation processes probed by scattering experiments. For this, a conformational dynamics theory of dynamical neutron and X-ray scattering is developed, following our previous approach for computing dynamical fingerprints of time-correlation functions [F. Noé, S. Doose, I. Daidone, M. Löllmann, J. Chodera, M. Sauer, and J. Smith, Proc. Natl. Acad. Sci. U.S.A. 108, 4822 (2011)]. Markov modeling is used to approximate the relaxation processes and timescales of the molecule via the eigenvectors and eigenvalues of a transition matrix between conformational substates. This procedure allows the establishment of a complete set of exponential decay functions and a full decomposition into the individual contributions, i.e., the contribution of every atom and dynamical process to each experimental relaxation process.

  2. Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna

    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.

  3. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

    PubMed Central

    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

  4. Modeling strategic use of human computer interfaces with novel hidden Markov models.

    PubMed

    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.

  5. Modeling strategic use of human computer interfaces with novel hidden Markov models

    PubMed Central

    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

  6. A Markov track model for simulating Typhoon Tracks in North-Western Pacific Ocean

    NASA Astrophysics Data System (ADS)

    Song, B.; Rajagopalan, B.; Lim, G. H.

    2014-12-01

    Typhoons typically occur during Jul-Sep, and the average number of occurrences per year in North Pacific and land falling over South Korea is ~25 and ~3, respectively. They cause enormous damage to infrastructure and lives and more so to developing countries in the region. Hence, to manage and mitigate this natural hazard, robust estimates of typhoon risks are necessary. Historical data is limited in length and incomplete in its variability. Thus, a methodology to generate a rich variety of realistic typhoon scenarios and the associated land fall risk along the coastal regions of their impact is important. To this end, we propose a spatial Markov track simulation model. In this, the domain is divided into 5x5 grids, and a typhoon in a grid box has ten states to transit to in the following 6-hour period - they are moving into one of the 8 neighboring grids, staying or fizzling out in the same grid. Based on historical 6-hourly typhoon track data (for the period 1977 - 2013) transition probabilities (i.e. probability of transitioning to one of the aforementioned ten states from the current 6-hour period to the following 6-hour period) are compute. Typhoon origination probabilities are also computed for each grid box. A track is initiated from one of the grid boxes based on the origination probabilities and it is propagated by the spatial Markov transition probabilities. A large number of synthetic tracks are generated for the spatial Markov probabilities from which estimates of land falling risks can be computed for different coastal segments. Wind speed magnitudes at each time step are generated by K-nearest neighbor resampling of the speeds of the historical tracks within each grid box based on the speed of the previous time step. A statistical distribution such as Weibull, can also be fitted for each neighborhood sample to simulate wind speeds. The Markov probabilities can also be estimated conditioned on large scale climate features that impact typhoon tracks to

  7. "Markov at the bat": a model of cognitive processing in baseball batters.

    PubMed

    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. PMID:12430839

  8. An estimator of the survival function based on the semi-Markov model under dependent censorship.

    PubMed

    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.

  9. A TWO-STATE MIXED HIDDEN MARKOV MODEL FOR RISKY TEENAGE DRIVING BEHAVIOR

    PubMed Central

    Jackson, John C.; Albert, Paul S.; Zhang, Zhiwei

    2016-01-01

    This paper proposes a joint model for longitudinal binary and count outcomes. We apply the model to a unique longitudinal study of teen driving where risky driving behavior and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and predicting crash and near crash outcomes. We propose a two-state mixed hidden Markov model whereby the hidden state characterizes the mean for the joint longitudinal crash/near crash outcomes and elevated g-force events which are a proxy for risky driving. Heterogeneity is introduced in both the conditional model for the count outcomes and the hidden process using a shared random effect. An estimation procedure is presented using the forward–backward algorithm along with adaptive Gaussian quadrature to perform numerical integration. The estimation procedure readily yields hidden state probabilities as well as providing for a broad class of predictors.

  10. An estimator of the survival function based on the semi-Markov model under dependent censorship.

    PubMed

    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. PMID:15938546

  11. "Markov at the bat": a model of cognitive processing in baseball batters.

    PubMed

    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.

  12. A two-state Markov mixture model for a time series of epileptic seizure counts.

    PubMed

    Albert, P S

    1991-12-01

    This paper discusses a model for a time series of epileptic seizure counts in which the mean of a Poisson distribution changes according to an underlying two-state Markov chain. The EM algorithm (Dempster, Laird, and Rubin, 1977, Journal of the Royal Statistical Society, Series B 39, 1-38) is used to compute maximum likelihood estimators for the parameters of this two-state mixture model and extensions are made allowing for nonstationarity. The model is illustrated using daily seizure counts for patients with intractable epilepsy and results are compared with a simple Poisson distribution and Poisson regressions. Some simulation results are also presented to demonstrate the feasibility of this model. PMID:1786324

  13. Hidden Markov model approach to skill learning and its application to telerobotics

    SciTech Connect

    Yang, J. . Robotics Inst. Univ. of Akron, OH . Dept. of Electrical Engineering); Xu, Y. . Robotics Inst.); Chen, C.S. . Dept. of Electrical Engineering)

    1994-10-01

    In this paper, the authors discuss the problem of how human skill can be represented as a parametric model using a hidden Markov model (HMM), and how an HMM-based skill model can be used to learn human skill. HMM is feasible to characterize a doubly stochastic process--measurable action and immeasurable mental states--that is involved in the skill learning. The authors formulated the learning problem as a multidimensional HMM and developed a testbed for a variety of skill learning applications. Based on ''the most likely performance'' criterion, the best action sequence can be selected from all previously measured action data by modeling the skill as an HMM. The proposed method has been implemented in the teleoperation control of a space station robot system, and some important implementation issues have been discussed. The method allows a robot to learn human skill certain tasks and to improve motion performance.

  14. Developmental Relations between Vocabulary Knowledge and Reading Comprehension: A Latent Change Score Modeling Study

    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…

  15. Constant Latent Odds-Ratios Models and the Mantel-Haenszel Null Hypothesis

    ERIC Educational Resources Information Center

    Hessen, David J.

    2005-01-01

    In the present paper, a new family of item response theory (IRT) models for dichotomous item scores is proposed. Two basic assumptions define the most general model of this family. The first assumption is local independence of the item scores given a unidimensional latent trait. The second assumption is that the odds-ratios for all item-pairs are…

  16. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    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…

  17. The Latent Curve ARMA (P, Q) Panel Model: Longitudinal Data Analysis in Educational Research and Evaluation

    ERIC Educational Resources Information Center

    Sivo, Stephen; Fan, Xitao

    2008-01-01

    Autocorrelated residuals in longitudinal data are widely reported as common to longitudinal data. Yet few, if any, researchers modeling growth processes evaluate a priori whether their data have this feature. Sivo, Fan, and Witta (2005) found that not modeling autocorrelated residuals present in longitudinal data severely biases latent curve…

  18. 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…

  19. 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…

  20. 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…

  1. Three Approaches to Using Lengthy Ordinal Scales in Structural Equation Models: Parceling, Latent Scoring, and Shortening Scales

    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…

  2. Incorporating teleconnection information into reservoir operating policies using Stochastic Dynamic Programming and a Hidden Markov Model

    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

  3. Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.

    PubMed

    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. PMID:27442595

  4. Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

    PubMed Central

    Anderson, John R.

    2011-01-01

    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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second “model discovery” application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. PMID:21820455

  5. Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions.

    PubMed

    McCallum, Kenneth Jordan; Wang, Ji-Ping

    2013-07-01

    Copy number variations (CNVs) are a significant source of genetic variation and have been found frequently associated with diseases such as cancers and autism. High-throughput sequencing data are increasingly being used to detect and quantify CNVs; however, the distributional properties of the data are not fully understood. A hidden Markov model (HMM) is proposed using inhomogeneous emission distributions based on negative binomial regression to account for the sequencing biases. The model is tested on the whole genome sequencing data and simulated data sets. An algorithm for CNV detection is implemented in the R package CNVfinder. The model based on negative binomial regression is shown to provide a good fit to the data and provides competitive performance compared with methods based on normalization of read counts. PMID:23428932

  6. Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

    PubMed

    Anderson, John R

    2012-03-01

    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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving.

  7. Integrating hidden Markov model and PRAAT: a toolbox for robust automatic speech transcription

    NASA Astrophysics Data System (ADS)

    Kabir, A.; Barker, J.; Giurgiu, M.

    2010-09-01

    An automatic time-aligned phone transcription toolbox of English speech corpora has been developed. Especially the toolbox would be very useful to generate robust automatic transcription and able to produce phone level transcription using speaker independent models as well as speaker dependent models without manual intervention. The system is based on standard Hidden Markov Models (HMM) approach and it was successfully experimented over a large audiovisual speech corpus namely GRID corpus. One of the most powerful features of the toolbox is the increased flexibility in speech processing where the speech community would be able to import the automatic transcription generated by HMM Toolkit (HTK) into a popular transcription software, PRAAT, and vice-versa. The toolbox has been evaluated through statistical analysis on GRID data which shows that automatic transcription deviates by an average of 20 ms with respect to manual transcription.

  8. A discrete Markov metapopulation model for persistence and extinction of species.

    PubMed

    Thompson, Colin J; Shtilerman, Elad; Stone, Lewi

    2016-09-01

    A simple discrete generation Markov metapopulation model is formulated for studying the persistence and extinction dynamics of a species in a given region which is divided into a large number of sites or patches. Assuming a linear site occupancy probability from one generation to the next we obtain exact expressions for the time evolution of the expected number of occupied sites and the mean-time to extinction (MTE). Under quite general conditions we show that the MTE, to leading order, is proportional to the logarithm of the initial number of occupied sites and in precise agreement with similar expressions for continuous time-dependent stochastic models. Our key contribution is a novel application of generating function techniques and simple asymptotic methods to obtain a second order asymptotic expression for the MTE which is extremely accurate over the entire range of model parameter values.

  9. Autoregressive hidden Markov models for the early detection of neonatal sepsis.

    PubMed

    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.

  10. Hand Gesture Spotting Based on 3D Dynamic Features Using Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Elmezain, Mahmoud; Al-Hamadi, Ayoub; Michaelis, Bernd

    In this paper, we propose an automatic system that handles hand gesture spotting and recognition simultaneously in stereo color image sequences without any time delay based on Hidden Markov Models (HMMs). Color and 3D depth map are used to segment hand regions. The hand trajectory will determine in further step using Mean-shift algorithm and Kalman filter to generate 3D dynamic features. Furthermore, k-means clustering algorithm is employed for the HMMs codewords. To spot meaningful gestures accurately, a non-gesture model is proposed, which provides confidence limit for the calculated likelihood by other gesture models. The confidence measures are used as an adaptive threshold for spotting meaningful gestures. Experimental results show that the proposed system can successfully recognize isolated gestures with 98.33% and meaningful gestures with 94.35% reliability for numbers (0-9).

  11. Simulating the peptide folding kinetic related spectra based on the Markov State Model.

    PubMed

    Song, Jian; Zhuang, Wei

    2014-01-01

    Optical spectroscopic tools are used to monitor protein folding/unfolding dynamics after a fast triggering such as the laser induced temperature jump. These techniques provide new opportunities for comparison between theory and simulations and atom-level understanding protein folding mechanism. However, the direct comparison still face two main challenges: a gap between folding relevant timescales (microseconds or above) and length of molecular dynamics simulations (typically tens to hundreds of nanoseconds), and difficulty in directly calculating spectroscopic observables from simulation configurations. Markov State Model (MSM) approach is one of the most powerful means which can increase simulations timescale up to microsecond or even millisecond. We address progress on modeling infrared and fluorescence spectroscopic signals of temperature jump induced unfolding dynamics for a few small proteins. The harmoniousness between experiment and theoretical can both improve our understanding of protein folding mechanisms and provide direct validation of those theoretical models. PMID:24446363

  12. Automatic segmentation of breast MR images through a Markov random field statistical model.

    PubMed

    Ribes, S; Didierlaurent, D; Decoster, N; Gonneau, E; Risser, L; Feillel, V; Caselles, O

    2014-10-01

    An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.

  13. Brief Communication: Earthquake sequencing: analysis of time series constructed from the Markov chain model

    NASA Astrophysics Data System (ADS)

    Cavers, M. S.; Vasudevan, K.

    2015-10-01

    Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, derived from the time series using the EEMD, to a detailed analysis to draw information content of the time series. Also, we investigate the influence of random noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behaviour. Here, we extend the Fano factor and Allan factor analysis to the time series of state-to-state transition frequencies of a Markov chain. Our results support not only the usefulness of the intrinsic mode functions in understanding the time series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

  14. Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model.

    PubMed

    Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Norton, Larry; Kuhn, Peter

    2013-05-01

    The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, although not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as "spreaders" or "sponges" and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multidirectional aspects of cancer progression. We quantify three types of multidirectional mechanisms of progression: (i) self-seeding of the primary tumor, (ii) reseeding of the primary tumor from a metastatic site (primary reseeding), and (iii) reseeding of metastatic tumors (metastasis reseeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease. PMID:23447576

  15. Inferring Sequential Order of Somatic Mutations during Tumorgenesis based on Markov Chain Model.

    PubMed

    Kang, Hao; Cho, Kwang-Hyun; Zhang, Xiaohua Douglas; Zeng, Tao; Chen, Luonan

    2015-01-01

    Tumors are developed and worsen with the accumulated mutations on DNA sequences during tumorigenesis. Identifying the temporal order of gene mutations in cancer initiation and development is a challenging topic. It not only provides a new insight into the study of tumorigenesis at the level of genome sequences but also is an effective tool for early diagnosis of tumors and preventive medicine. In this paper, we develop a novel method to accurately estimate the sequential order of gene mutations during tumorigenesis from genome sequencing data based on Markov chain model as TOMC (Temporal Order based on Markov Chain), and also provide a new criterion to further infer the order of samples or patients, which can characterize the severity or stage of the disease. We applied our method to the analysis of tumors based on several high-throughput datasets. Specifically, first, we revealed that tumor suppressor genes (TSG) tend to be mutated ahead of oncogenes, which are considered as important events for key functional loss and gain during tumorigenesis. Second, the comparisons of various methods demonstrated that our approach has clear advantages over the existing methods due to the consideration on the effect of mutation dependence among genes, such as co-mutation. Third and most important, our method is able to deduce the ordinal sequence of patients or samples to quantitatively characterize their severity of tumors. Therefore, our work provides a new way to quantitatively understand the development and progression of tumorigenesis based on high throughput sequencing data. PMID:26451822

  16. Markov random field model for segmenting large populations of lipid vesicles from micrographs.

    PubMed

    Zupanc, Jernej; Drobne, Damjana; Ster, Branko

    2011-12-01

    Giant unilamellar lipid vesicles, artificial replacements for cell membranes, are a promising tool for in vitro assessment of interactions between products of nanotechnologies and biological membranes. However, the effect of nanoparticles can not be derived from observations on a single specimen, vesicle populations should be observed instead. We propose an adaptation of the Markov random field image segmentation model which allows detection and segmentation of numerous vesicles in micrographs. The reliability of this model with different lighting, blur, and noise characteristics of micrographs is examined and discussed. Moreover, the automatic segmentation is tested on micrographs with thousands of vesicles and the result is compared to that of manual segmentation. The segmentation step presented is part of a methodology we are developing for bio-nano interaction assessment studies on lipid vesicles.

  17. The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach

    PubMed Central

    Ficz, Gabriella; Wolf, Verena; Walter, Jörn

    2016-01-01

    DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation by replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of 5mC. Several alternative mechanisms have been proposed suggesting that 5hmC influences either replication dependent maintenance of DNA methylation or replication independent processes of active demethylation. Using high resolution hairpin oxidative bisulfite sequencing data, we precisely determine the amount of 5mC and 5hmC and model the contribution of 5hmC to processes of demethylation in mouse ESCs. We develop an extended hidden Markov model capable of accurately describing the regional contribution of 5hmC to demethylation dynamics. Our analysis shows that 5hmC has a strong impact on replication dependent demethylation, mainly by impairing methylation maintenance. PMID:27224554

  18. Laser Doppler vibrometry measurements of the carotid pulse: biometrics using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Kaplan, Alan D.; O'Sullivan, Joseph A.; Sirevaag, Erik J.; Rohrbaugh, John W.

    2009-05-01

    Small movements of the skin overlying the carotid artery, arising from pressure pulse changes in the carotid during the cardiac cycle, can be detected using the method of Laser Doppler Vibrometry (LDV). Based on the premise that there is a high degree of individuality in cardiovascular function, the pulse-related movements were modeled for biometric use. Short time variations in the signal due to physiological factors are described and these variations are shown to be informative for identity verification and recognition. Hidden Markov models (HMMs) are used to exploit the dependence between the pulse signals over successive cardiac cycles. The resulting biometric classification performance confirms that the LDV signal contains information that is unique to the individual.

  19. FOAM (Functional Ontology Assignments for Metagenomes): A Hidden Markov Model (HMM) database with environmental focus

    SciTech Connect

    Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; Ta , Neslihan; Lamendella, Regina; Dvornik, Jill; Mackelprang, Rachel; Myrold, David D.; Jumpponen, Ari; Tringe, Susannah G.; Holman, Elizabeth; Mavromatis, Konstantinos; Jansson, Janet K.

    2014-09-26

    A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.

  20. Business cycles and monetary policy asymmetry: An investigation using Markov-switching models

    NASA Astrophysics Data System (ADS)

    Tan, Siow-Hooi; Habibullah, Muzafar Shah

    2007-07-01

    This study assesses empirically the effects of monetary policy on four ASEAN economies in different states. The idea of asymmetry is being examined by using the relatively popular technique of non-linear modeling-Hamilton's Markov regime-switching model. The findings confirmed the existence of two-regimes in all economies under study. Additionally, the null hypothesis of symmetry had been rejected in the case of the four economies and to a great extent, monetary policy was confirmed to have had larger effects during recessions. These findings, thus, may imply the important role that credit market imperfections have on a firm's investment behavior, which in turn suggests that the financial accelerator is a relevant mechanism underscoring the observed asymmetry.

  1. The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach.

    PubMed

    Giehr, Pascal; Kyriakopoulos, Charalampos; Ficz, Gabriella; Wolf, Verena; Walter, Jörn

    2016-05-01

    DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation by replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of 5mC. Several alternative mechanisms have been proposed suggesting that 5hmC influences either replication dependent maintenance of DNA methylation or replication independent processes of active demethylation. Using high resolution hairpin oxidative bisulfite sequencing data, we precisely determine the amount of 5mC and 5hmC and model the contribution of 5hmC to processes of demethylation in mouse ESCs. We develop an extended hidden Markov model capable of accurately describing the regional contribution of 5hmC to demethylation dynamics. Our analysis shows that 5hmC has a strong impact on replication dependent demethylation, mainly by impairing methylation maintenance. PMID:27224554

  2. An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

    PubMed

    Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng; Wang, Hao; Jia, Wenyan; Burke, Lora E; Baranowski, Thomas; Sun, Mingui

    2015-05-01

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

  3. FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus.

    PubMed

    Prestat, Emmanuel; David, Maude M; Hultman, Jenni; Taş, Neslihan; Lamendella, Regina; Dvornik, Jill; Mackelprang, Rachel; Myrold, David D; Jumpponen, Ari; Tringe, Susannah G; Holman, Elizabeth; Mavromatis, Konstantinos; Jansson, Janet K

    2014-10-29

    A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. 'profiles') were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.

  4. A novel approach for global lung registration using 3D Markov-Gibbs appearance model.

    PubMed

    El-Baz, Ayman; Khalifa, Fahmi; Elnakib, Ahmed; Nitzken, Matthew; Soliman, Ahmed; McClure, Patrick; Abou El-Ghar, Mohamed; Gimel'farb, Georgy

    2012-01-01

    A new approach to align 3D CT data of a segmented lung object with a given prototype (reference lung object) using an affine transformation is proposed. Visual appearance of the lung from CT images, after equalizing their signals, is modeled with a new 3D Markov-Gibbs random field (MGRF) with pairwise interaction model. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of voxel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by a gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.

  5. Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao

    2014-11-01

    A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television

  6. Statistical Inference in Hidden Markov Models Using k-Segment Constraints

    PubMed Central

    Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher

    2016-01-01

    Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward–backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. PMID:27226674

  7. A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

    NASA Astrophysics Data System (ADS)

    Roth, John; Tummala, Murali; McEachen, John

    2016-09-01

    This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.

  8. Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment

    NASA Astrophysics Data System (ADS)

    Jiang, Huiming; Chen, Jin; Dong, Guangming

    2016-05-01

    Hidden Markov model (HMM) has been widely applied in bearing performance degradation assessment. As a machine learning-based model, its accuracy, subsequently, is dependent on the sensitivity of the features used to estimate the degradation performance of bearings. It's a big challenge to extract effective features which are not influenced by other qualities or attributes uncorrelated with the bearing degradation condition. In this paper, a bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. NAP can filter out the effect of nuisance attributes in feature space through projection. The new feature space projected by NAP is more sensitive to bearing health changes and barely influenced by other interferences occurring in operation condition. To verify the effectiveness of the proposed method, two different experimental databases are utilized. The results show that the combination of HMM and NAP can effectively improve the accuracy and robustness of the bearing performance degradation assessment system.

  9. Damage evaluation by a guided wave-hidden Markov model based method

    NASA Astrophysics Data System (ADS)

    Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin

    2016-02-01

    Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

  10. Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods

    SciTech Connect

    Liao, T. W.; Hua, G; Qu, Jun; Blau, Peter Julian

    2006-01-01

    Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.

  11. Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation

    NASA Astrophysics Data System (ADS)

    Wan, Yiwen; Duraisamy, Prakash; Alam, Mohammad S.; Buckles, Bill

    2012-06-01

    Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.

  12. Mixture Item Response Theory-MIMIC Model: Simultaneous Estimation of Differential Item Functioning for Manifest Groups and Latent Classes

    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…

  13. Fitting complex population models by combining particle filters with Markov chain Monte Carlo.

    PubMed

    Knape, Jonas; de Valpine, Perry

    2012-02-01

    We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.

  14. Fitting complex population models by combining particle filters with Markov chain Monte Carlo.

    PubMed

    Knape, Jonas; de Valpine, Perry

    2012-02-01

    We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm. PMID:22624307

  15. 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…

  16. Daily evapotranspiration estimates by scaling instantaneous latent heat flux derived from a two-source model

    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...

  17. 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…

  18. Longitudinal Changes in Physical Fitness Performance in Youth: A Multilevel Latent Growth Curve Modeling Approach

    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…

  19. 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…

  20. Applications of Latent-Variable Models in Educational Psychology: The Need for Methodological-Substantive Synergies

    ERIC Educational Resources Information Center

    Marsh, Herbert W.; Hau, Kit-Tai

    2007-01-01

    As emphasized in the call for papers by Jonna Kulikowich and Gregory Hancock, the primary goal of this special issue of "Contemporary Educational Psychology" is to assemble a collection of illustrative empirical studies in educational psychology that utilize one or more state-of-the-art latent variable modeling procedures. Distinguishing these…

  1. 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…

  2. 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…

  3. Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

    ERIC Educational Resources Information Center

    Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

    2007-01-01

    Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

  4. 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,…

  5. 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…

  6. Applying a Weighted Maximum Likelihood Latent Trait Estimator to the Generalized Partial Credit Model

    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…

  7. On Fitting Nonlinear Latent Curve Models to Multiple Variables Measured Longitudinally

    ERIC Educational Resources Information Center

    Blozis, Shelley A.

    2007-01-01

    This article shows how nonlinear latent curve models may be fitted for simultaneous analysis of multiple variables measured longitudinally using Mx statistical software. Longitudinal studies often involve observation of several variables across time with interest in the associations between change characteristics of different variables measured…

  8. 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…

  9. 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…

  10. 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…

  11. 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…

  12. 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…

  13. Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

    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…

  14. A Direct Latent Variable Modeling Based Method for Point and Interval Estimation of Coefficient Alpha

    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…

  15. 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.…

  16. A Program Implementing Mardia's Multivariate Normality Test for Use in Structural Equation Modeling with Latent Variables.

    ERIC Educational Resources Information Center

    MacIntosh, Randall

    1997-01-01

    Presents KANT, a FORTRAN 77 software program that tests assumptions of multivariate normality in a data set. Based on the test developed by M. V. Mardia (1985), the KANT program is useful for those engaged in structural equation modeling with latent variables. (SLD)

  17. 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…

  18. Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling

    ERIC Educational Resources Information Center

    Cheong, JeeWon

    2011-01-01

    The latent growth curve modeling (LGCM) approach has been increasingly utilized to investigate longitudinal mediation. However, little is known about the accuracy of the estimates and statistical power when mediation is evaluated in the LGCM framework. A simulation study was conducted to address these issues under various conditions including…

  19. 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)

  20. A stochastic Markov chain model to describe lung cancer growth and metastasis.

    PubMed

    Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter

    2012-01-01

    A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.

  1. Characterising the Transmission Dynamics of Acinetobacter baumannii in Intensive Care Units Using Hidden Markov Models.

    PubMed

    Doan, Tan N; Kong, David C M; Marshall, Caroline; Kirkpatrick, Carl M J; McBryde, Emma S

    2015-01-01

    Little is known about the transmission dynamics of Acinetobacter baumannii in hospitals, despite such information being critical for designing effective infection control measures. In the absence of comprehensive epidemiological data, mathematical modelling is an attractive approach to understanding transmission process. The statistical challenge in estimating transmission parameters from infection data arises from the fact that most patients are colonised asymptomatically and therefore the transmission process is not fully observed. Hidden Markov models (HMMs) can overcome this problem. We developed a continuous-time structured HMM to characterise the transmission dynamics, and to quantify the relative importance of different acquisition sources of A. baumannii in intensive care units (ICUs) in three hospitals in Melbourne, Australia. The hidden states were the total number of patients colonised with A. baumannii (both detected and undetected). The model input was monthly incidence data of the number of detected colonised patients (observations). A Bayesian framework with Markov chain Monte Carlo algorithm was used for parameter estimations. We estimated that 96-98% of acquisition in Hospital 1 and 3 was due to cross-transmission between patients; whereas most colonisation in Hospital 2 was due to other sources (sporadic acquisition). On average, it takes 20 and 31 days for each susceptible individual in Hospital 1 and Hospital 3 to become colonised as a result of cross-transmission, respectively; whereas it takes 17 days to observe one new colonisation from sporadic acquisition in Hospital 2. The basic reproduction ratio (R0) for Hospital 1, 2 and 3 was 1.5, 0.02 and 1.6, respectively. Our study is the first to characterise the transmission dynamics of A. baumannii using mathematical modelling. We showed that HMMs can be applied to sparse hospital infection data to estimate transmission parameters despite unobserved events and imperfect detection of the organism

  2. A stochastic Markov chain model to describe lung cancer growth and metastasis.

    PubMed

    Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter

    2012-01-01

    A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model. PMID:22558094

  3. Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index

    NASA Astrophysics Data System (ADS)

    Liu, Yufang; Zhang, Weiguo; Fu, Junhui

    2016-11-01

    This paper presents the Binomial Markov-switching Multifractal (BMSM) model of asset returns with Skewed t innovations (BMSM-Skewed t for short), which considers the fat tails, skewness and multifractality in asset returns simultaneously. The parameters of BMSM-Skewed t model can be estimated by Maximum Likelihood (ML) methods, and volatility forecasting can be accomplished via Bayesian updating. In order to evaluate the performance of BMSM-Skewed t model, BMSM model with Normal innovations (BMSM-N), BMSM model with Student-t innovations (BMSM-t) and GARCH(1,1) models (GARCH-N, GARCH-t and GARCH-Skewed t) are chosen for comparison. Through empirical studies on Shanghai Stock Exchange Composite Index (SSEC), we find that for sample estimation, BMSM models outperform the GARCH(1,1) models through BIC and AIC rules, and BMSM-Skewed t performs the best among all the models due to its fat tails, skewness and multifractality. In addition, BMSM-Skewed t model dominates other models at most forecasting horizons for out-of-sample volatility forecasts in terms of MSE, MAE and SPA test.

  4. Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach

    NASA Astrophysics Data System (ADS)

    Albora, A. Muhittin; Ucan, Osman N.; Aydogan, Davut

    2007-05-01

    In this study, a Markov Random Field (MRF) approach is used to locate source boundary positions which are difficult to identify from Bouguer gravity and magnetic maps. As a generalized form of Markov Chains, the MRF approach is an unsupervised statistical model based algorithm and is applied to the analysis of images, particularly in the detection of visual patterns or textures. Here, we present a dynamic programming based on the MRF approach for boundary detection of noisy and super-positioned potential anomalies, which are produced by various geological structures. In the MRF method, gravity and magnetic maps are considered as two-dimensional (2-D) images with a matrix composed of N 1 × N 2 pixels. Each pixel value of the matrix is optimized in real time with no a priori processing by using two parameter sets; average steering vector (θ) and quantization level (M). They carry information about the correlation of neighboring pixels and the locality of their connections. We have chosen MRF as a processing approach for geophysical data since it is an unsupervised, efficient model for image enhancement, border detection and separation of 2-D potential anomalies. The main benefit of MRF is that an average steering vector and a quantization level are enough in evaluation of the potential anomaly maps. We have compared the MRF method to noise implemented synthetic potential field anomalies. After satisfactory results were found, the method has been applied to gravity and magnetic anomaly maps of Gelibolu Peninsula in Western Turkey. Here, we have observed Anafartalar thrust fault and another parallel fault northwest of Anafartalar thrust fault. We have modeled a geological structure including a lateral fault, which results in a higher susceptibility and anomaly amplitude increment. We have shown that the MRF method is effective to detect the broad-scale geological structures in the Gelibolu Peninsula, and thus to delineate the complex tectonic structure of Gelibolu

  5. Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

    PubMed

    Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

    2013-01-01

    The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

  6. Modeling Change in Learning Strategies throughout Higher Education: A Multi-Indicator Latent Growth Perspective

    PubMed Central

    Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

    2013-01-01

    The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain. PMID:23844112

  7. Network-based genomic discovery: application and comparison of Markov random field models.

    PubMed

    Wei, Peng; Pan, Wei

    2010-01-01

    As biological knowledge accumulates rapidly, gene networks encoding genome-wide gene-gene interactions have been constructed. As an improvement over the standard mixture model that tests all the genes iid a priori, Wei and Li (2007) and Wei and Pan (2008) proposed modeling a gene network as a Discrete- or Gaussian-Markov random field (DMRF or GMRF) respectively in a mixture model to analyze genomic data. However, how these methods compare in practical applications in not well understood and this is the aim here. We also propose two novel constraints in prior specifications for the GMRF model and a fully Bayesian approach to the DMRF model. We assess the accuracy of estimating the False Discovery Rate (FDR) by posterior probabilities in the context of MRF models. Applications to a ChIP-chip data set and simulated data show that the modified GMRF models has superior performance as compared with other models, while both MRF-based mixture models, with reasonable robustness to misspecified gene networks, outperform the standard mixture model.

  8. Application of Gray Markov SCGM(1,1)c Model to Prediction of Accidents Deaths in Coal Mining

    PubMed Central

    Lan, Jian-yi; Zhou, Ying

    2014-01-01

    The prediction of mine accident is the basis of aviation safety assessment and decision making. Gray prediction is suitable for such kinds of system objects with few data, short time, and little fluctuation, and Markov chain theory is just suitable for forecasting stochastic fluctuating dynamic process. Analyzing the coal mine accident human error cause, combining the advantages of both Gray prediction and Markov theory, an amended Gray Markov SCGM(1,1)c model is proposed. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along the tendency. Finally, the new model is applied to forecast the mine safety accident deaths from 1990 to 2010 in China, and, 2011–2014 coal accidents deaths were predicted. The results show that the new model not only discovers the trend of the mine human error accident death toll but also overcomes the random fluctuation of data affecting precision. It possesses stronger engineering application. PMID:27419203

  9. Automatic prone to supine haustral fold matching in CT colonography using a Markov random field model.

    PubMed

    Hampshire, Thomas; Roth, Holger; Hu, Mingxing; Boone, Darren; Slabaugh, Greg; Punwani, Shonit; Halligan, Steve; Hawkes, David

    2011-01-01

    CT colonography is routinely performed with the patient prone and supine to differentiate fixed colonic pathology from mobile faecal residue. We propose a novel method to automatically establish correspondence. Haustral folds are detected using a graph cut method applied to a surface curvature-based metric, where image patches are generated using endoluminal CT colonography surface rendering. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints, are used with a Markov Random Field (MRF) model to estimate the fold labelling assignment. The method achieved fold matching accuracy of 83.1% and 88.5% with and without local colonic collapse. Moreover, it improves an existing surface-based registration algorithm, decreasing mean registration error from 9.7mm to 7.7mm in cases exhibiting collapse.

  10. Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models

    PubMed Central

    Mehta, Pankaj; Schwab, David J.; Sengupta, Anirvan M.

    2011-01-01

    Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the “inverse” statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it. PMID:22851788

  11. Identifying bubble collapse in a hydrothermal system using hiddden Markov models

    USGS Publications Warehouse

    Dawson, Phillip B.; Benitez, M.C.; Lowenstern, Jacob B.; Chouet, Bernard A.

    2012-01-01

    Beginning in July 2003 and lasting through September 2003, the Norris Geyser Basin in Yellowstone National Park exhibited an unusual increase in ground temperature and hydrothermal activity. Using hidden Markov model theory, we identify over five million high-frequency (>15 Hz) seismic events observed at a temporary seismic station deployed in the basin in response to the increase in hydrothermal activity. The source of these seismic events is constrained to within ~100 m of the station, and produced ~3500–5500 events per hour with mean durations of ~0.35–0.45 s. The seismic event rate, air temperature, hydrologic temperatures, and surficial water flow of the geyser basin exhibited a marked diurnal pattern that was closely associated with solar thermal radiance. We interpret the source of the seismicity to be due to the collapse of small steam bubbles in the hydrothermal system, with the rate of collapse being controlled by surficial temperatures and daytime evaporation rates.

  12. A nonstationary Markov transition model for computing the relative risk of dementia before death.

    PubMed

    Yu, Lei; Griffith, William S; Tyas, Suzanne L; Snowdon, David A; Kryscio, Richard J

    2010-03-15

    This paper investigates the long-term behavior of the k-step transition probability matrix for a nonstationary discrete-time Markov chain in the context of modeling transitions from intact cognition to dementia with mild cognitive impairment and global impairment as intervening cognitive states. The authors derive formulas for the following absorption statistics: (1) the relative risk of absorption between competing absorbing states and (2) the mean and variance of the number of visits among the transient states before absorption. As absorption is not guaranteed, sufficient conditions are discussed to ensure that the substochastic matrix associated with transitions among transient states converges to zero in limit. Results are illustrated with an application to the Nun Study, a cohort of 678 participants, 75-107 years of age, followed longitudinally with up to 10 cognitive assessments over a 15-year period.

  13. Application of Markov chain model to daily maximum temperature for thermal comfort in Malaysia

    SciTech Connect

    Nordin, Muhamad Asyraf bin Che; Hassan, Husna

    2015-10-22

    The Markov chain’s first order principle has been widely used to model various meteorological fields, for prediction purposes. In this study, a 14-year (2000-2013) data of daily maximum temperatures in Bayan Lepas were used. Earlier studies showed that the outdoor thermal comfort range based on physiologically equivalent temperature (PET) index in Malaysia is less than 34°C, thus the data obtained were classified into two state: normal state (within thermal comfort range) and hot state (above thermal comfort range). The long-run results show the probability of daily temperature exceed TCR will be only 2.2%. On the other hand, the probability daily temperature within TCR will be 97.8%.

  14. Integrating Decision Tree and Hidden Markov Model (HMM) for Subtype Prediction of Human Influenza A Virus

    NASA Astrophysics Data System (ADS)

    Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing

    Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.

  15. 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., 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.

  16. Cost-effectiveness of Chagas disease interventions in latin america and the Caribbean: Markov models.

    PubMed

    Wilson, Leslie S; Strosberg, Arthur M; Barrio, Kimberly

    2005-11-01

    Chagas disease is a parasitic disease in Latin America. Despite vector control programs that have reduced incidence by 70%, there are at least 12-14 million prevalent cases. We used a Markov model to examine strategies for control and treatment of Chagas disease that compared annual costs, life expectancies, and cost-effectiveness of three vector control and drug treatment strategies. Vector control programs alone and vector control plus drug treatment are dominant over no vector control (i.e., less costly and save more lives), and vector control plus drug is highly cost-effective compared with vector control alone. We demonstrated expected changes in deaths over time resulting from various prevention approaches. Vector control affects primarily incidence, not decreasing deaths and prevalence for 30 years, while drug treatment affects prevalence and deaths immediately. The best strategy to combat Chagas disease is combinations of vector control and a potential new drug.

  17. Complexity of Kronecker Operations on Sparse Matrices with Applications to the Solution of Markov Models

    NASA Technical Reports Server (NTRS)

    Buchholz, Peter; Ciardo, Gianfranco; Donatelli, Susanna; Kemper, Peter

    1997-01-01

    We present a systematic discussion of algorithms to multiply a vector by a matrix expressed as the Kronecker product of sparse matrices, extending previous work in a unified notational framework. Then, we use our results to define new algorithms for the solution of large structured Markov models. In addition to a comprehensive overview of existing approaches, we give new results with respect to: (1) managing certain types of state-dependent behavior without incurring extra cost; (2) supporting both Jacobi-style and Gauss-Seidel-style methods by appropriate multiplication algorithms; (3) speeding up algorithms that consider probability vectors of size equal to the "actual" state space instead of the "potential" state space.

  18. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.

    PubMed

    Wiedenhoeft, John; Brugel, Eric; Schliep, Alexander

    2016-05-01

    By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings. PMID:27177143

  19. Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models

    PubMed Central

    Plattner, Nuria; Noé, Frank

    2015-01-01

    Understanding the structural mechanisms of protein–ligand binding and their dependence on protein sequence and conformation is of fundamental importance for biomedical research. Here we investigate the interplay of conformational change and ligand-binding kinetics for the serine protease Trypsin and its competitive inhibitor Benzamidine with an extensive set of 150 μs molecular dynamics simulation data, analysed using a Markov state model. Seven metastable conformations with different binding pocket structures are found that interconvert at timescales of tens of microseconds. These conformations differ in their substrate-binding affinities and binding/dissociation rates. For each metastable state, corresponding solved structures of Trypsin mutants or similar serine proteases are contained in the protein data bank. Thus, our wild-type simulations explore a space of conformations that can be individually stabilized by adding ligands or making suitable changes in protein sequence. These findings provide direct evidence of conformational plasticity in receptors. PMID:26134632

  20. HMM-DM: identifying differentially methylated regions using a hidden Markov model.

    PubMed

    Yu, Xiaoqing; Sun, Shuying

    2016-03-01

    DNA methylation is an epigenetic modification involved in organism development and cellular differentiation. Identifying differential methylations can help to study genomic regions associated with diseases. Differential methylation studies on single-CG resolution have become possible with the bisulfite sequencing (BS) technology. However, there is still a lack of efficient statistical methods for identifying differentially methylated (DM) regions in BS data. We have developed a new approach named HMM-DM to detect DM regions between two biological conditions using BS data. This new approach first uses a hidden Markov model (HMM) to identify DM CG sites accounting for spatial correlation across CG sites and variation across samples, and then summarizes identified sites into regions. We demonstrate through a simulation study that our approach has a superior performance compared to BSmooth. We also illustrate the application of HMM-DM using a real breast cancer dataset. PMID:26887041