A Hidden Markov model for Bayesian data fusion of multivariate signals
Féron, O; Feron, Olivier; Mohammad-Djafari, Ali
2004-01-01
In this work we propose a Bayesian framework for data fusion of multivariate signals which arises in imaging systems. More specifically, we consider the case where we have observed two images of the same object through two different imaging processes. The objective of this work is then to propose a coherent approach to combine these data sets to obtain a segmented image which can be considered as the fusion result of these two images. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently, with common hidden classification label variables which is modeled by the Potts Markov Random Field. We propose then an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.
Infinite Structured Hidden Semi-Markov Models
Huggins, Jonathan H.; Wood, Frank
2014-01-01
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian nonparametric hidden Markov models that enhance a posteriori state-persistence in particular. This paper also introduces a new Bayesian nonparametric framework for generating left-to-right and other structured, explicit-duration infinite hidden Markov models that we call the infinite structured hidden semi-Markov m...
Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks
Zhu, Shijia; Wang, Yadong
2015-12-01
Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.
Bayesian Inference in Hidden Markov Random Fields for Binary Data Defined on Large Lattices
Friel, N.; Pettitt, A.N.; Reeves, R.; Wit, E.
2009-01-01
Hidden Markov random fields represent a complex hierarchical model, where the hidden latent process is an undirected graphical structure. Performing inference for such models is difficult primarily because the likelihood of the hidden states is often unavailable. The main contribution of this articl
Bayesian Fine-Scale Mapping of Disease Loci, by Hidden Markov Models
Morris, A P; Whittaker, J C; Balding, D. J.
2000-01-01
We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers—in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain–Monte Carlo simulation methods, we obtain posterior distributions...
Fusion of Hidden Markov Random Field models and its Bayesian estimation.
Destrempes, François; Angers, Jean-François; Mignotte, Max
2006-10-01
In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence. PMID:17022259
Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework
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.
Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework
Directory of Open Access Journals (Sweden)
Humblot Fabrice
2006-01-01
Full Text Available 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.
Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rissanen, Jorma
1996-01-01
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where...
Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rissanen, Jorma
1996-01-01
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression wher...
Directory of Open Access Journals (Sweden)
William A Griffin
Full Text Available Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects-some good and some bad-on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM. Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes.
Griffin, William A; Li, Xun
2016-01-01
Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects-some good and some bad-on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes. PMID:27187319
Griffin, William A.; Li, Xun
2016-01-01
Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects—some good and some bad—on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes. PMID:27187319
Sensitivity of hidden Markov models
Mitrophanov, Alexander Yu.; Lomsadze, Alexandre; Borodovsky, Mark
2005-01-01
We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov ...
Adaptive Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rasmussen, Tage
1996-01-01
Partially Hidden Markov Models (PHMM) have recently been introduced. The transition and emission probabilities are conditioned on the past. In this report, the PHMM is extended with a multiple token version. The different versions of the PHMM are applied to bi-level image coding.......Partially Hidden Markov Models (PHMM) have recently been introduced. The transition and emission probabilities are conditioned on the past. In this report, the PHMM is extended with a multiple token version. The different versions of the PHMM are applied to bi-level image coding....
Hidden hybrid Markov/semi-Markov chains.
GUÉDON, YANN
2005-01-01
http://www.sciencedirect.com/science?ₒb=IssueURL&_tockey=%23TOC%235880%232005%23999509996%23596026%23FLA%23&ₐuth=y&view=c&ₐcct=C000056834&_version=1&_urlVersion=0&_userid=2292769&md5=87e7f8be94f92a8574da566c600ce631 International audience Models that combine Markovian states with implicit geometric state occupancy distributions and semi-Markovian states with explicit state occupancy distributions, are investigated. This type of model retains the flexibility of hidden semi-Markov chains ...
A Non-Parametric Bayesian Method for Inferring Hidden Causes
Wood, Frank; Griffiths, Thomas; Ghahramani, Zoubin
2012-01-01
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables. This makes it possible to use a Gibbs sampler to approximate the distribution over causal stru...
Building Simple Hidden Markov Models. Classroom Notes
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.
Coding with partially hidden Markov models
DEFF Research Database (Denmark)
Forchhammer, Søren; Rissanen, J.
Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general....... The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt...
Estimating hidden semi-Markov chains from discrete sequences.
Guédon, Yann
2003-01-01
International audience This article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences. A discrete hidden semi-Markov chain is composed of a nonobservable state process, which is a semi-Markov chain, and a discrete output process. Hidden semi-Markov chains generalize hidden Markov chains and enable the modeling of various durat...
Time series segmentation with shifting means hidden markov models
Directory of Open Access Journals (Sweden)
Ath. Kehagias
2006-01-01
Full Text Available 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.
Time series segmentation with shifting means hidden markov models
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.
Entropy Rate for Hidden Markov Chains with rare transitions
Peres, Yuval; Quas, Anthony
2010-01-01
We consider Hidden Markov Chains obtained by passing a Markov Chain with rare transitions through a noisy memoryless channel. We obtain asymptotic estimates for the entropy of the resulting Hidden Markov Chain as the transition rate is reduced to zero.
Detecting Structural Breaks using Hidden Markov Models
DEFF Research Database (Denmark)
Ntantamis, Christos
Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another...
Hidden Markov models estimation and control
Elliott, Robert J; Moore, John B
1995-01-01
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filte
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
Raffa, Jesse D; Dubin, Joel A
2015-09-01
Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. PMID:25761965
MCMC joint separation and segmentation of hidden Markov fields
Snoussi, H; Snoussi, Hichem; Mohammad-Djafari, Ali
2002-01-01
In this contribution, we consider the problem of the blind separation of noisy instantaneously mixed images. The images are modelized by hidden Markov fields with unknown parameters. Given the observed images, we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov Chain (MCMC) procedure. We separate the unknown variables into two categories: 1. The parameters of interest which are the mixing matrix, the noise covariance and the parameters of the sources distributions. 2. The hidden variables which are the unobserved sources and the unobserved pixels classification labels. The proposed algorithm provides in the stationary regime samples drawn from the posterior distributions of all the variables involved in the problem leading to a flexibility in the cost function choice. We discuss and characterize some problems of non identifiability and degeneracies of the parameters likelihood and the behavior of the MCMC algorithm in this case. F...
Pruning Boltzmann networks and hidden Markov models
DEFF Research Database (Denmark)
Pedersen, Morten With; Stork, D.
1996-01-01
Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...
Hidden Markov Models for Human Genes
DEFF Research Database (Denmark)
Baldi, Pierre; Brunak, Søren; Chauvin, Yves; Krogh, Anders Stærmose
We analyse the sequential structure of human genomic DNA by hidden Markov models. We apply models of widely different design: conventional left-right constructs and models with a built-in periodic architecture. The models are trained on segments of DNA sequences extracted such that they cover...
Hidden Markov models for labeled sequences
DEFF Research Database (Denmark)
Krogh, Anders Stærmose
A hidden Markov model for labeled observations, called a class HMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model the statistics of the training sequences it is trained to optimize recognition. It resembles MMI...
Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series
Bulla, Jan
2006-01-01
Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs) provide flexible, general-purpose models for univariate and multivariate time series. Although interest in HMMs and HSMMs has continuously increased during the past years, and numerous articles on theoretical and practical aspects have been published, several gaps remain. This thesis addresses some of them, divided into three main topics. 1. Computational issues in parameter estimation of stationary HMMs. The parameters of ...
Zipf exponent of trajectory distribution in the hidden Markov model
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.
Zipf exponent of trajectory distribution in the hidden Markov model
International Nuclear Information System (INIS)
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
Infinite Factorial Unbounded-State Hidden Markov Model.
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
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential ex...
Context Tree Estimation in Variable Length Hidden Markov Models
Dumont, Thierry
2011-01-01
We address the issue of context tree estimation in variable length hidden Markov models. We propose an estimator of the context tree of the hidden Markov process which needs no prior upper bound on the depth of the context tree. We prove that the estimator is strongly consistent. This uses information-theoretic mixture inequalities in the spirit of Finesso and Lorenzo(Consistent estimation of the order for Markov and hidden Markov chains(1990)) and E.Gassiat and S.Boucheron (Optimal error exponents in hidden Markov model order estimation(2003)). We propose an algorithm to efficiently compute the estimator and provide simulation studies to support our result.
Phonocardiogram segmentation by using Hidden Markov Models
Lima, C. S.; Cardoso, Manuel J.
2007-01-01
This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models technology. Concerning to several heart pathologies the analysis of the intervals between the first and second heart sounds is of utmost importance. Such intervals are silent for a normal subject and the presence of murmurs indicate certain cardiovascular defects and diseases. While the first heart sound can easily be detected if the ECG is available, the second heart sound is much more diff...
A clustering approach for estimating parameters of a profile hidden Markov model.
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
Bayesian Smoothing Algorithms in Partially Observed Markov Chains
Ait-el-Fquih, Boujemaa; Desbouvries, François
2006-11-01
Let x = {xn}n∈N be a hidden process, y = {yn}n∈N an observed process and r = {rn}n∈N some auxiliary process. We assume that t = {tn}n∈N with tn = (xn, rn, yn-1) is a (Triplet) Markov Chain (TMC). TMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient restoration and parameter estimation algorithms. This paper is devoted to Bayesian smoothing algorithms for TMC. We first propose twelve algorithms for general TMC. In the Gaussian case, these smoothers reduce to a set of algorithms which include, among other solutions, extensions to TMC of classical Kalman-like smoothing algorithms (originally designed for HMC) such as the RTS algorithms, the Two-Filter algorithms or the Bryson and Frazier algorithm.
Directory of Open Access Journals (Sweden)
R.J. Boys
2002-01-01
Full Text Available This paper describes a Bayesian approach to determining the order of a finite state Markov chain whose transition probabilities are themselves governed by a homogeneous finite state Markov chain. It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. The method we describe uses a Markov chain Monte Carlo algorithm to obtain samples from the (posterior distribution for both the order of Markov dependence in the observed sequence and the other governing model parameters. These samples allow coherent inferences to be made straightforwardly in contrast to those which use information criteria. The methods are illustrated by their application to both simulated and real data sets.
Modeling electricity markets with hidden Markov model
International Nuclear Information System (INIS)
This paper proposes to model the movements of electricity markets as partially observable Markov processes driven by underlying economic forces. An electricity market is modeled as a dynamic system evolving over time according to Markov processes. At any time interval, the electricity market can be in one state and transition to another state in the next time interval. This paper models the states of an electricity market as partially observable, while each state has incomplete observations such as market-clearing price and quantity. The true market states are hidden from a market participant behind the incomplete observation. The hidden Markov model (HMM) is of a more fundamental approach and focuses on capturing the interaction of supply and demand forces on electricity markets. Such an approach is appropriate because the simultaneous production and consumption of electricity eliminates the storage sector, while limited transmission networks segment electricity markets. This model is shown to be able to link the fundamental drivers to the price behaviors; therefore, it provides forecast power for mid-term and long-term price movements. This work applies HMM to historical data from New York independent system operator (NYISO), and examples are given to illustrate the forecast power of HMM. (author)
Fast sampling from a Hidden Markov Model posterior for large data
DEFF Research Database (Denmark)
Bonnevie, Rasmus; Hansen, Lars Kai
2014-01-01
Hidden Markov Models are of interest in a broad set of applications including modern data driven systems involving very large data sets. However, approximate inference methods based on Bayesian averaging are precluded in such applications as each sampling step requires a full sweep over the data...... sets offering fast access to approximate posterior samples. In a specific example we see that the new scheme is a hundred times faster than conventional Markov Chain Monte Carlo sampling using the Forward-backward method....
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...
Hidden Markov models applications in computer vision
Bunke, H
2001-01-01
Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).
Genetic Algorithms Principles Towards Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-10-01
Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.
Binary hidden Markov models and varieties
Critch, Andrew J
2012-01-01
The technological applications of hidden Markov models have been extremely diverse and successful, including natural language processing, gesture recognition, gene sequencing, and Kalman filtering of physical measurements. HMMs are highly non-linear statistical models, and just as linear models are amenable to linear algebraic techniques, non-linear models are amenable to commutative algebra and algebraic geometry. This paper examines closely those HMMs in which all the random variables, called nodes, are binary. Its main contributions are (1) minimal defining equations for the 4-node model, comprising 21 quadrics and 29 cubics, which were computed using Gr\\"obner bases in the cumulant coordinates of Sturmfels and Zwiernik, and (2) a birational parametrization for every binary HMM, with an explicit inverse for recovering the hidden parameters in terms of observables. The new model parameters in (2) are hence rationally identifiable in the sense of Sullivant, Garcia-Puente, and Spielvogel, and each model's Zar...
Bayesian variable order Markov models: Towards Bayesian predictive state representations
C. Dimitrakakis
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more st
Epitope discovery with phylogenetic hidden Markov models.
LENUS (Irish Health Repository)
Lacerda, Miguel
2010-05-01
Existing methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.
An introduction to hidden Markov models for biological sequences
DEFF Research Database (Denmark)
Krogh, Anders Stærmose
A non-matematical tutorial on hidden Markov models (HMMs) plus a description of one of the applications of HMMs: gene finding.......A non-matematical tutorial on hidden Markov models (HMMs) plus a description of one of the applications of HMMs: gene finding....
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Rao, Vinayak
2012-01-01
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filtering-backward sampling algorithm over states at the set of extant and virtual jump times. We demonstrate significant computational benefits over a state-of-the-art Gibbs sampler on a number of continuous time Bayesian networks.
Entropy rate of continuous-state hidden Markov chains
Han, G; Marcus, B
2010-01-01
We prove that under mild positivity assumptions, the entropy rate of a continuous-state hidden Markov chain, observed when passing a finite-state Markov chain through a discrete-time continuous-output channel, is analytic as a function of the transition probabilities of the underlying Markov chain. We further prove that the entropy rate of a continuous-state hidden Markov chain, observed when passing a mixing finite-type constrained Markov chain through a discrete-time Gaussian channel, is sm...
Analyticity of entropy rate of hidden Markov chains
Han, G; Marcus, B
2006-01-01
We prove that under mild positivity assumptions the entropy rate of a hidden Markov chain varies analytically as a function of the underlying Markov chain parameters. A general principle to determine the domain of analyticity is stated. An example is given to estimate the radius of convergence for the entropy rate. We then show that the positivity assumptions can be relaxed, and examples are given for the relaxed conditions. We study a special class of hidden Markov chains in more detail: bin...
Policy Recognition in the Abstract Hidden Markov Model
Bui, H H; West, G; 10.1613/jair.839
2011-01-01
In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of p...
Hidden Markov model using Dirichlet process for de-identification.
Chen, Tao; Cullen, Richard M; Godwin, Marshall
2015-12-01
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance. PMID:26407642
Neuroevolution Mechanism for Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-12-01
Full Text Available Hidden Markov Model (HMM is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language
processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by
applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
Probabilistic Resilience in Hidden Markov Models
Panerati, Jacopo; Beltrame, Giovanni; Schwind, Nicolas; Zeltner, Stefan; Inoue, Katsumi
2016-05-01
Originally defined in the context of ecological systems and environmental sciences, resilience has grown to be a property of major interest for the design and analysis of many other complex systems: resilient networks and robotics systems other the desirable capability of absorbing disruption and transforming in response to external shocks, while still providing the services they were designed for. Starting from an existing formalization of resilience for constraint-based systems, we develop a probabilistic framework based on hidden Markov models. In doing so, we introduce two new important features: stochastic evolution and partial observability. Using our framework, we formalize a methodology for the evaluation of probabilities associated with generic properties, we describe an efficient algorithm for the computation of its essential inference step, and show that its complexity is comparable to other state-of-the-art inference algorithms.
Plume mapping via hidden Markov methods.
Farrell, J A; Pang, Shuo; Li, Wei
2003-01-01
This paper addresses the problem of mapping likely locations of a chemical source using an autonomous vehicle operating in a fluid flow. The paper reviews biological plume-tracing concepts, reviews previous strategies for vehicle-based plume tracing, and presents a new plume mapping approach based on hidden Markov methods (HMM). HMM provide efficient algorithms for predicting the likelihood of odor detection versus position, the likelihood of source location versus position, the most likely path taken by the odor to a given location, and the path between two points most likely to result in odor detection. All four are useful for solving the odor source localization problem using an autonomous vehicle. The vehicle is assumed to be capable of detecting above threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. The fluid flow is assumed to vary with space and time, and to have a high Reynolds number (Re>10). PMID:18238238
Hidden Markov Model for Stock Selection
Directory of Open Access Journals (Sweden)
Nguyet Nguyen
2015-10-01
Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.
Ground Plane Estimation using a Hidden Markov Model
Dragon, Ralf; Gool, Luc >
2014-01-01
Dragon R., Van Gool L., ''Ground plane estimation using a hidden Markov model'', 27th IEEE conference on computer vision and pattern recognition - CVPR 2014, pp. 4026-4033, June 23-28, 2014, Columbus, Ohio, USA.
Hidden Markov Models with Factored Gaussian Mixtures Densities
Institute of Scientific and Technical Information of China (English)
LI Hao-zheng; LIU Zhi-qiang; ZHU Xiang-hua
2004-01-01
We present a factorial representation of Gaussian mixture models for observation densities in Hidden Markov Models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.
Evaluation of relevance of stochastic parameters on Hidden Markov Models
Roblès, Bernard; Avila, Manuel; Duculty, Florent; Vrignat, Pascal; Kratz, Frédéric
2011-01-01
Prediction of physical particular phenomenon is based on knowledge of the phenomenon. This knowledge helps us to conceptualize this phenomenon around different models. Hidden Markov Models (HMM) can be used for modeling complex processes. This kind of models is used as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to evaluate relevance of Hidden Markov Models parameters, wit...
A hidden Markov Model for image fusion and their joint segmentation in medical image computing
Féron, O; Feron, Olivier; Mohammad-Djafari, Ali
2004-01-01
In this work we propose a Bayesian framework for fully automated image fusion and their joint segmentation. More specifically, we consider the case where we have observed images of the same object through different image processes or through different spectral bands. The objective of this work is then to propose a coherent approach to combine these data sets and obtain a segmented image which can be considered as the fusion result of these observations. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently, with common hidden classification label variables which are modeled by the Potts Markov Random Field. We propose an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.
Kaijser, Thomas
2013-01-01
A Hidden Markov Model generates two basic stochastic processes, a Markov chain, which is hidden, and an observation sequence. The filtering process of a Hidden Markov Model is, roughly speaking, the sequence of conditional distributions of the hidden Markov chain that is obtained as new observations are received. It is well-known, that the filtering process itself, is also a Markov chain. A classical, theoretical problem is to find conditions which implies that the distributions of the filter...
Hidden Markov Model of atomic quantum jump dynamics in an optically probed cavity
DEFF Research Database (Denmark)
Gammelmark, S.; Molmer, K.; Alt, W.;
2014-01-01
We analyze the quantum jumps of an atom interacting with a cavity field. The strong atom- field interaction makes the cavity transmission depend on the time dependent atomic state, and we present a Hidden Markov Model description of the atomic state dynamics which is conditioned in a Bayesian...... manner on the detected signal. We suggest that small variations in the observed signal may be due to spatial motion of the atom within the cavity, and we represent the atomic system by a number of hidden states to account for both the small variations and the internal state jump dynamics. In our theory......, the atomic state is determined in a Bayesian manner from the measurement data, and we present an iterative protocol, which determines both the atomic state and the model parameters. As a new element in the treatment of observed quantum systems, we employ a Bayesian approach that conditions the atomic...
A Novel Method for Decoding Any High-Order Hidden Markov Model
Fei Ye; Yifei Wang
2014-01-01
This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal stat...
Barbu, Vlad
2008-01-01
Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. This book concerns with the estimation of discrete-time semi-Markov and hidden semi-Markov processes
Hidden Markov Model Application to Transfer The Trader Online Forex Brokers
Farida Suharleni; Agus Widodo; Endang Wahyu H
2012-01-01
Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, cate...
Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model
DelRose, Michael; Frederick, Philip; 10.5121/ijaia.2011.2101
2011-01-01
The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. The intelligence of current computer algorithms has not reached this level of complexity, but there are several research efforts that are working towards it. With the number of classification algorithms available, it is hard to determine which algorithm works best for a particular situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their variants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a big downfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability to summarize the actions, and thus determine, with pretty good accuracy, the intention of the person performing the action. These visual cues and linkages are important...
Riboswitch Detection Using Profile Hidden Markov Models
Directory of Open Access Journals (Sweden)
Krishnamachari A
2009-10-01
Full Text Available Abstract Background Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM. Our method exploits the high degree of sequence conservation that characterizes the aptamer domain. Results Our method can detect riboswitches in genomic databases rapidly and accurately. Its sensitivity is comparable to the method based on the Covariance Model (CM. For six out of ten riboswitch classes, our method detects more than 99.5% of the candidates identified by the much slower CM method while being several hundred times faster. For three riboswitch classes, our method detects 97-99% of the candidates relative to the CM method. Our method works very well for those classes of riboswitches that are characterized by distinct and conserved sequence motifs. Conclusion Riboswitches play a crucial role in controlling the expression of several prokaryotic genes involved in metabolism and transport processes. As more and more new classes of riboswitches are being discovered, it is important to understand the patterns of their intra and inter genomic distribution. Understanding such patterns will enable us to better understand the evolutionary history of these genetic regulatory elements. However, a complete picture of the distribution pattern of riboswitches will emerge only after accurate identification of riboswitches across genomes
Credit Card Fraud Detection Using Hidden Markov Model
Directory of Open Access Journals (Sweden)
SHAILESH S. DHOK
2012-03-01
Full Text Available The most accepted payment mode is credit card forboth online and offline in today’s world, it provides cashlessshopping at every shop in all countries. It will be the mostconvenient way to do online shopping, paying bills etc. Hence,risks of fraud transaction using credit card has also beenincreasing. In the existing credit card fraud detection businessprocessing system, fraudulent transaction will be detected aftertransaction is done. It is difficult to find out fraudulent andregarding loses will be barred by issuing authorities. HiddenMarkov Model is the statistical tools for engineer and scientiststo solve various problems. In this paper, it is shown that creditcard fraud can be detected using Hidden Markov Model duringtransactions. Hidden Markov Model helps to obtain a highfraud coverage combined with a low false alarm rate.
MODELING PAVEMENT DETERIORATION PROCESSES BY POISSON HIDDEN MARKOV MODELS
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.
Limit Theorems for the Sample Entropy of Hidden Markov Chains
Han, Guangyue
2011-01-01
The Shannon-McMillan-Breiman theorem asserts that the sample entropy of a stationary and ergodic stochastic process converges to the entropy rate of the same process almost surely. In this paper, we focus our attention on the convergence behavior of the sample entropy of a hidden Markov chain. Under certain positivity assumption, we prove that a central limit theorem (CLT) with some Berry-Esseen bound for the sample entropy of a hidden Markov chain, and we use this CLT to establish a law of iterated logarithm (LIL) for the sample entropy.
ACTIVITY ANALYSIS WITH HIDDEN MARKOV MODEL FOR AMBIENT ASSISTED LIVING
Directory of Open Access Journals (Sweden)
Dietmar Bruckner
2012-06-01
Full Text Available In an Ambient Assisted Living (AAL project the activities of the user will be analyzed. The raw data is from a motion detector. Through data processing the huge amount of dynamic raw data was translated to state data. With hidden Markov model, forward algorithm to analyze these state data the daily activity model of the user was built. Thirdly by comparing the model with observed activity sequences, and finding out the similarities between them, defined the best adapt routine in the model. Furthermore an activity routine net was built and used to compare with the hidden Markov model.
Detecting Faults By Use Of Hidden Markov Models
Smyth, Padhraic J.
1995-01-01
Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).
Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R
DEFF Research Database (Denmark)
O'Connell, Jarad Michael; Højsgaard, Søren
2011-01-01
This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov...
Engineering of Algorithms for Hidden Markov models and Tree Distances
DEFF Research Database (Denmark)
Sand, Andreas
computing distance measures between phylogenetic trees. Hidden Markov models is a class of probabilistic models that is used in a number of core applications in bioinformatics such as modeling of proteins, gene finding and reconstruction of species and population histories. I show how a relatively simple...
Characterization of prokaryotic and eukaryotic promoters using hidden Markov models
DEFF Research Database (Denmark)
Pedersen, Anders Gorm; Baldi, P.; Chauvin, Y.;
1996-01-01
In this paper we utilize hidden Markov models (HMMs) and information theory to analyze prokaryotic and eukaryotic promoters. We perform this analysis with special emphasis on the fact that promoters are divided into a number of different classes, depending on which polymerase-associated factors...
Approximation of stationary processes by hidden Markov models
L. Finesso; A. Grassi; P. Spreij
2010-01-01
Stochastic realization is still an open problem for the class of hidden Markov models (HMM): given the law Q of an HMM find a finite parametric description of it. Fifty years after the introduction of HMMs, no computationally effective realization algorithm has been proposed. In this paper we direct
Recognizing Strokes in Tennis Videos Using Hidden Markov Models
Petkovic, M.; Jonker, W.; Zivkovic, Z.
2001-01-01
This paper addresses content-based video retrieval with an emphasis on recognizing events in tennis game videos. In particular, we aim at recognizing different classes of tennis strokes using automatic learning capability of Hidden Markov Models. Driven by our domain knowledge, a robust player segme
Unsupervised Segmentation of Hidden Semi-Markov Non Stationary Chains
Lapuyade-Lahorgue, Jérôme; Pieczynski, Wojciech
2006-11-01
In the classical hidden Markov chain (HMC) model we have a hidden chain X, which is a Markov one and an observed chain Y. HMC are widely used; however, in some situations they have to be replaced by the more general "hidden semi-Markov chains" (HSMC) which are particular "triplet Markov chains" (TMC) T = (X, U, Y), where the auxiliary chain U models the semi-Markovianity of X. Otherwise, non stationary classical HMC can also be modeled by a triplet Markov stationary chain with, as a consequence, the possibility of parameters' estimation. The aim of this paper is to use simultaneously both properties. We consider a non stationary HSMC and model it as a TMC T = (X, U1, U2, Y), where U1 models the semi-Markovianity and U2 models the non stationarity. The TMC T being itself stationary, all parameters can be estimated by the general "Iterative Conditional Estimation" (ICE) method, which leads to unsupervised segmentation. We present some experiments showing the interest of the new model and related processing in image segmentation area.
Markov Model of Wind Power Time Series UsingBayesian Inference of Transition Matrix
Chen, Peiyuan; Berthelsen, Kasper Klitgaard; Bak-Jensen, Birgitte; Chen, Zhe
2009-01-01
This paper proposes to use Bayesian inference of transition matrix when developing a discrete Markov model of a wind speed/power time series and 95% credible interval for the model verification. The Dirichlet distribution is used as a conjugate prior for the transition matrix. Three discrete Markov models are compared, i.e. the basic Markov model, the Bayesian Markov model and the birth-and-death Markov model. The proposed Bayesian Markov model shows the best accuracy in modeling the autocorr...
A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction
Directory of Open Access Journals (Sweden)
Chongben Tao
2013-01-01
Full Text Available To achieve Human-Robot Interaction (HRI by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance.
Sequential Tracking of a Hidden Markov Chain Using Point Process Observations
Bayraktar, Erhan
2007-01-01
We study finite horizon optimal switching problems for hidden Markov chain models under partially observable Poisson processes. The controller possesses a finite range of strategies and attempts to track the state of the unobserved state variable using Bayesian updates over the discrete observations. Such a model has applications in economic policy making, staffing under variable demand levels and generalized Poisson disorder problems. We show regularity of the value function and explicitly characterize an optimal strategy. We also provide an efficient numerical scheme and illustrate our results with several computational examples.
Hidden Markov modelling of movement data from fish
DEFF Research Database (Denmark)
Pedersen, Martin Wæver
and application of hidden Markov models (HMMs) for analysis of movement data from sh. The main contributions are represented by six scientific publications. Estimation of animal location from uncertain and possibly indirect observations is the starting point of most movement data analyses. In this work a discrete...... approximated. This furthermore enables accurate probability densities of location to be computed. Finally, the performance of the HMM approach in analysing nonlinear state space models is compared with two alternatives: the AD Model Builder framework and BUGS, which relies on Markov chain Monte Carlo...
Hidden Markov processes theory and applications to biology
Vidyasagar, M
2014-01-01
This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are t
Multiple testing for neuroimaging via hidden Markov random field.
Shu, Hai; Nan, Bin; Koeppe, Robert
2015-09-01
Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three-dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation-maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer's or another dementia, and normal controls in the FDG-PET imaging study of the Alzheimer's Disease Neuroimaging Initiative. PMID:26012881
A Hidden Markov Approach to Modeling Interevent Earthquake Times
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.
Inference in Hidden Markov Models with Explicit State Duration Distributions
Dewar, Michael; Wiggins, Chris; Wood, Frank
2012-01-01
In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting of both discrete state-indicator and discrete state-duration random variables. In contrast to the implicit geometric state duration distribution possessed by the standard HMM, ...
Specialized Hidden Markov Model Databases for Microbial Genomics
Martin Gollery
2003-01-01
As hidden Markov models (HMMs) become increasingly more important in the analysis of biological sequences, so too have databases of HMMs expanded in size, number and importance. While the standard paradigm a short while ago was the analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databas...
Topic Information Collection based on the Hidden Markov Model
Hai-yan Jiang; Xing-ce Wang; Zhong-ke Wu; Ming-Quan Zhou; Xue-Song Wang
2013-01-01
Specific-subject oriented information collection is one of the key technologies of vertical search engines, which directly affects the speed and relevance of search results. The topic information collection algorithm is widely used for its accuracy. The Hidden Markov Model (HMM) is used to learn and judge the relevance between the Uniform Resource Locator (URL) and the topic information. The Rocchio method is used to construct the prototype vectors relevant to the topic information, and the H...
Driver's behaviour modelling using the Hidden MarkovModel formalism
Dapzol, N.; TATTEGRAIN-VESTE, H
2005-01-01
In this paper, we propose to model the evolution of data sensors during the driving situation encountered by a driver, using the hidden Markov Model formalism. We then use this modeling to identify in real time the current driver's aim. We tested the capacity of this modeling in a first experiment where we were able to categorize with an 80% success rate the driver's actions from their initial preparatory movements. Moreover, this formalism could give us information on the driver's behavior i...
Analysis of animal accelerometer data using hidden Markov models
Leos-Barajas, Vianey; Photopoulou, Theoni; Langrock, Roland; Patterson, Toby A; Watanabe, Yuuki; Murgatroyd, Megan; Papastamatiou, Yannis P.
2016-01-01
Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data there is a natural dependence between observations of movement or behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (H...
Hidden Markov Models and their Applications in Biological Sequence Analysis
Yoon, Byung-Jun
2009-01-01
Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and ma...
Hidden Markov Modeling for humpback whale (Megaptera novaeangliae) call classification
PACE, Federica; White, Paul; Adam, Olivier
2012-01-01
International audience This study proposes a new approach for the classification of the calls detected in the songs with the use of Hidden Markov Models (HMMs) based on the concept of subunits as building blocks. HMMs have been used once before for such task but in an unsupervised algorithm with promising results, and they are used extensively in speech recognition and in few bioacoustics studies. Their flexibility suggests that they may be suitable for the analysis of the varied repertoir...
A Dependent Hidden Markov Model of Credit Quality
Directory of Open Access Journals (Sweden)
Małgorzata Wiktoria Korolkiewicz
2012-01-01
Full Text Available We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.
Hidden Markov models for prediction of protein features
DEFF Research Database (Denmark)
Bystroff, Christopher; Krogh, Anders
2008-01-01
Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein s...... algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction....... structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard......Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein...
Permutation Complexity and Coupling Measures in Hidden Markov Models
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Taichi Haruna
2013-09-01
Full Text Available Recently, the duality between values (words and orderings (permutations has been proposed by the authors as a basis to discuss the relationship between information theoretic measures for finite-alphabet stationary stochastic processes and their permutatio nanalogues. It has been used to give a simple proof of the equality between the entropy rate and the permutation entropy rate for any finite-alphabet stationary stochastic process and to show some results on the excess entropy and the transfer entropy for finite-alphabet stationary ergodic Markov processes. In this paper, we extend our previous results to hidden Markov models and show the equalities between various information theoretic complexity and coupling measures and their permutation analogues. In particular, we show the following two results within the realm of hidden Markov models with ergodic internal processes: the two permutation analogues of the transfer entropy, the symbolic transfer entropy and the transfer entropy on rank vectors, are both equivalent to the transfer entropy if they are considered as the rates, and the directed information theory can be captured by the permutation entropy approach.
Bayesian internal dosimetry calculations using Markov Chain Monte Carlo
International Nuclear Information System (INIS)
A new numerical method for solving the inverse problem of internal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioassay data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time. (author)
Fast MCMC sampling for hidden markov models to determine copy number variations
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Mahmud Md Pavel
2011-11-01
Full Text Available Abstract Background Hidden Markov Models (HMM are often used for analyzing Comparative Genomic Hybridization (CGH data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems. Results We propose an approximate sampling technique, inspired by compression of discrete sequences in HMM computations and by kd-trees to leverage spatial relations between data points in typical data sets, to speed up the MCMC sampling. Conclusions We test our approximate sampling method on simulated and biological ArrayCGH datasets and high-density SNP arrays, and demonstrate a speed-up of 10 to 60 respectively 90 while achieving competitive results with the state-of-the art Bayesian approaches. Availability: An implementation of our method will be made available as part of the open source GHMM library from http://ghmm.org.
Efficient Parallel Learning of Hidden Markov Chain Models on SMPs
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.
Introducing Busy Customer Portfolio Using Hidden Markov Model
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Sepideh Emam
2011-09-01
Full Text Available Due to the effective role of Markov models in customer relationship management (CRM, there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models in CRM. Forty four articles were selected and categorized on two major subclasses: articles which had used Markov chain models (MCM in CRM and those which had applied hidden Markov models (HMM in CRM. Findings of this paper indicate that applying HMM in CRM is approximately rare, since it contains 27.2% of the total number of published articles. To complete investigation a two-step framework has been suggested for using HMM in busy customer portfolio management. It is for the first time that two important concepts (busy customer and HMM are used to achieve a common goal. Also the model parameters have been estimated in order to analyze a real firm‟s data.
AIRWAY LABELING USING A HIDDEN MARKOV TREE MODEL
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
Evolving the Topology of Hidden Markov Models using Evolutionary Algorithms
DEFF Research Database (Denmark)
Thomsen, Réne
2002-01-01
Hidden Markov models (HMM) are widely used for speech recognition and have recently gained a lot of attention in the bioinformatics community, because of their ability to capture the information buried in biological sequences. Usually, heuristic algorithms such as Baum-Welch are used to estimate...... the model parameters. However, Baum-Welch has a tendency to stagnate on local optima. Furthermore, designing an optimal HMM topology usually requires a priori knowledge from a field expert and is usually found by trial-and-error. In this study, we present an evolutionary algorithm capable of evolving...
Moving Toward High Precision Dynamical Modelling in Hidden Markov Models
Gagnon, Sébastien; Rouat, Jean
2016-01-01
Hidden Markov Model (HMM) is often regarded as the dynamical model of choice in many fields and applications. It is also at the heart of most state-of-the-art speech recognition systems since the 70's. However, from Gaussian mixture models HMMs (GMM-HMM) to deep neural network HMMs (DNN-HMM), the underlying Markovian chain of state-of-the-art models did not changed much. The "left-to-right" topology is mostly always employed because very few other alternatives exist. In this paper, we propose...
HIDDEN MARKOV MODEL APPROACH TOWARDS EMOTION DETECTION FROM SPEECH SIGNAL
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K.Sathiyamurthy
2015-03-01
Full Text Available Emotions carry the token indicating a human’s mental state. Understanding the emotion exhibited becomes difficult for people suffering from autism and alexithymia. Assessment of emotions can also be beneficial in interactions involving a human and a machine. A system is developed to recognize the universally accepted emotions such as happy, anger, sad, disgust, fear and surprise. The gender of the speaker helps to obtain better clarity for identifying the emotion. Hidden Markov Model serves the purpose of gender identification.
A Constraint Model for Constrained Hidden Markov Models
DEFF Research Database (Denmark)
Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp; Petit, Matthieu
2009-01-01
A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we extend HMMs with constraints and show how the familiar Viterbi algorithm can be generalized, based on constraint solving...... methods. HMMs with constraints have advantages over traditional ones in terms of more compact expressions as well as opportunities for pruning during Viterbi computations. We exemplify this by an enhancement of a simple prokaryote gene finder given by an HMM....
Imputing unknown competitor marketing activity with a Hidden Markov Chain
Haughton, Dominique; Hua, Guangying; Jin, Danny; Lin, John; Wei, Qizhi; Zhang, Changan
2014-01-01
We demonstrate on a case study with two competing products at a bank how one can use a Hidden Markov Chain (HMC) to estimate missing information on a competitor's marketing activity. The idea is that given time series with sales volumes for products A and B and marketing expenditures for product A, as well as suitable predictors of sales for products A and B, we can infer at each point in time whether it is likely or not that marketing activities took place for product B. The method is succes...
Hidden-Markov-Model Analysis Of Telemanipulator Data
Hannaford, Blake; Lee, Paul
1991-01-01
Mathematical model and procedure based on hidden-Markov-model concept undergoing development for use in analysis and prediction of outputs of force and torque sensors of telerobotic manipulators. In model, overall task broken down into subgoals, and transition probabilities encode ease with which operator completes each subgoal. Process portion of model encodes task-sequence/subgoal structure, and probability-density functions for forces and torques associated with each state of manipulation encode sensor signals that one expects to observe at subgoal. Parameters of model constructed from engineering knowledge of task.
Fault diagnosis of nuclear facilities based on Hidden Markov Model
International Nuclear Information System (INIS)
Due to the complex structure of nuclear facilities in a high irradiation environment, people are hard to approach it. In view of these situations, a fault diagnosis method based on HMM (Hidden Markov Model) of capturing the audio signal while the nuclear facilities are operating is proposed. With the strong modeling ability, HMM can be applied to analyzing such as audio signal non-stationary time signal. By using this method, the original mechanical structures of nuclear facilities are not destroyed. The proposed sensors are needed as few as possible by the whole diagnosis system and which has a simple structure, low cost structure. The fault diagnosis rate is high. (authors)
A Bayesian Markov geostatistical model for estimation of hydrogeological properties
International Nuclear Information System (INIS)
A geostatistical methodology based on Markov-chain analysis and Bayesian statistics was developed for probability estimations of hydrogeological and geological properties in the siting process of a nuclear waste repository. The probability estimates have practical use in decision-making on issues such as siting, investigation programs, and construction design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distributions, as is often the case for classified information. Data do not need to meet the requirements on additivity and normality as with the geostatistical methods based on regionalized variable theory, e.g., kriging. The methodology also has a formal way for incorporating professional judgments through the use of Bayesian statistics, which allows for updating of prior estimates to posterior probabilities each time new information becomes available. A Bayesian Markov Geostatistical Model (BayMar) software was developed for implementation of the methodology in two and three dimensions. This paper gives (1) a theoretical description of the Bayesian Markov Geostatistical Model; (2) a short description of the BayMar software; and (3) an example of application of the model for estimating the suitability for repository establishment with respect to the three parameters of lithology, hydraulic conductivity, and rock quality designation index (RQD) at 400--500 meters below ground surface in an area around the Aespoe Hard Rock Laboratory in southeastern Sweden
MRI segmentation study based on wavelet-domain hidden Markov models
International Nuclear Information System (INIS)
Full text.The wavelet's transform has emerged as exciting new tool for statistical image processing. The wavelet domain provides a natural setting for many applications in medical imaging and tele medicine area. The interesting properties of wavelet transform have led to a powerful image processing technique based on a simple transformation of individual wavelet coefficient as thought it were dependent of all others. By exploiting the dependencies between wavelet coefficients, a new wavelet domain probability models have been developed based on the hidden Markov probability models. The Wavelet-domain hidden Markov (HMM) models have recently been introduced and successfully applied in image processing area and in particular the Hidden Markov tree (HMT) models. The HMT models can characterize the joint statistics of wavelet coefficients across scales. these models are tree-structured probabilistic graph that captures statistical properties of the coefficient of wavelet transform. Since the HMT is particularly well suited to image containing singularities like edge and ridge, it provides a good classifier for distinguishing between textures of image. Using the inherent tree structure of the wavelet HMT and it fast training and likelihood algorithms, the texture classification at range of different scales. We then fuse these multi scale classifications using Bayesian probabilistic graph to obtain reliable final segmentations. Finally, the compressed image can be segmented directly. In our work, we have applied these models for texture segmenting of compressed MRI images by using the HMT models. By concisely modeling and fusing the statistical behavior of textures at multiple scales, the algorithm developed on HTM models produces an accurate segmentation of texture images yielding a range of segmentation at different scales. One of the most important results is capability of segmenting compressed image without re-expanding, this create a framework for developing joint
Self-Organizing Hidden Markov Model Map (SOHMMM).
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
Colonoscopy video quality assessment using hidden Markov random fields
Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby
2011-03-01
With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.
Generalized Hidden Markov Models To Handwritten Devanagari Word Recognition
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Mr. Pradeep Singh Thakur
2012-06-01
Full Text Available Hidden Markov Models (HMM have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, Hidden Markov Models are increasingly being used to model substrokes of characters. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. In this effort, a data-driven HMM-based handwritten word recognition system for Hindi, an Indic script, is proposed. Though Devanagari is the script for Hindi, which is the official language of India, its character and word recognition pose great challenges due to large variety of symbols and their proximity in appearance. The accuracies obtained ranged from 30�0to 60�0with lexicon. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.
Limits of performance for the model reduction problem of hidden Markov models
Kotsalis, Georgios
2015-12-15
We introduce system theoretic notions of a Hankel operator, and Hankel norm for hidden Markov models. We show how the related Hankel singular values provide lower bounds on the norm of the difference between a hidden Markov model of order n and any lower order approximant of order n̂ < n.
Asymptotics of Entropy Rate in Special Families of Hidden Markov Chains
Han, G; Marcus, BH
2008-01-01
We derive an asymptotic formula for entropy rate of a hidden Markov chain under certain parameterizations. We also discuss applications of the asymptotic formula to the asymptotic behaviors of entropy rate of hidden Markov chains as outputs of certain channels, such as binary symmetric channel, binary erasure channel, and some special Gilbert-Elliot channel. © 2006 IEEE.
Error statistics of hidden Markov model and hidden Boltzmann model results
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Newberg Lee A
2009-07-01
Full Text Available Abstract Background Hidden Markov models and hidden Boltzmann models are employed in computational biology and a variety of other scientific fields for a variety of analyses of sequential data. Whether the associated algorithms are used to compute an actual probability or, more generally, an odds ratio or some other score, a frequent requirement is that the error statistics of a given score be known. What is the chance that random data would achieve that score or better? What is the chance that a real signal would achieve a given score threshold? Results Here we present a novel general approach to estimating these false positive and true positive rates that is significantly more efficient than are existing general approaches. We validate the technique via an implementation within the HMMER 3.0 package, which scans DNA or protein sequence databases for patterns of interest, using a profile-HMM. Conclusion The new approach is faster than general naïve sampling approaches, and more general than other current approaches. It provides an efficient mechanism by which to estimate error statistics for hidden Markov model and hidden Boltzmann model results.
PELACAKAN DAN PENGENALAN WAJAH MENGGUNAKAN METODE EMBEDDED HIDDEN MARKOV MODELS
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Arie Wirawan Margono
2004-01-01
Full Text Available Tracking and recognizing human face becomes one of the important research subjects nowadays, where it is applicable in security system like room access, surveillance, as well as searching for person identity in police database. Because of applying in security case, it is necessary to have robust system for certain conditions such as: background influence, non-frontal face pose of male or female in different age and race. The aim of this research is to develop software which combines human face tracking using CamShift algorithm and face recognition system using Embedded Hidden Markov Models. The software uses video camera (webcam for real-time input, video AVI for dynamic input, and image file for static input. The software uses Object Oriented Programming (OOP coding style with C++ programming language, Microsoft Visual C++ 6.0® compiler, and assisted by some libraries of Intel Image Processing Library (IPL and Intel Open Source Computer Vision (OpenCV. System testing shows that object tracking based on skin complexion using CamShift algorithm comes out well, for tracking of single or even two face objects at once. Human face recognition system using Embedded Hidden Markov Models method has reach accuracy percentage of 82.76%, using 341 human faces in database that consists of 31 individuals with 11 poses and 29 human face testers. Abstract in Bahasa Indonesia : Pelacakan dan pengenalan wajah manusia merupakan salah satu bidang yang cukup berkembang dewasa ini, dimana aplikasi dapat diterapkan dalam bidang keamanan (security system seperti ijin akses masuk ruangan, pengawasan lokasi (surveillance, maupun pencarian identitas individu pada database kepolisian. Karena diterapkan dalam kasus keamanan, dibutuhkan sistem yang handal terhadap beberapa kondisi, seperti: pengaruh latar belakang, pose wajah non-frontal terhadap pria maupun wanita dalam perbedaan usia dan ras. Tujuan penelitiam ini adalah untuk membuat perangkat lunak yang menggabungkan
The Consensus String Problem and the Complexity of Comparing Hidden Markov Models
DEFF Research Database (Denmark)
Lyngsø, Rune Bang; Pedersen, Christian Nørgaard Storm
2002-01-01
The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing...... the probability of generating a given string, or computing the most likely path generating a given string. In this paper we consider the problem of computing the most likely string, or consensus string, generated by a given model, and its implications on the complexity of comparing hidden Markov...... models. We show that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models. Furthermore, we establish the NP-hardness of comparing two hidden Markov models...
ENSO informed Drought Forecasting Using Nonhomogeneous Hidden Markov Chain Model
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.
Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome
Directory of Open Access Journals (Sweden)
Silva Cibele Q. da
2003-01-01
Full Text Available Dependencies in DNA sequences are frequently modeled using Markov models. However, Markov chains cannot account for heterogeneity that may be present in different regions of the same DNA sequence. Hidden Markov models are more realistic than Markov models since they allow for the identification of heterogeneous regions of a DNA sequence. In this study we present an application of hidden Markov models to a subsequence of the Xylella fastidiosa DNA data. We found that a three-state model provides a good description for the data considered.
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Everitt, Richard G
2012-01-01
Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian comput...
Hidden Markov Models for Detecting Aseismic Events in Southern California
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
Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
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Jérôme Boudy
2007-01-01
Full Text Available 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.
Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
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.
Application of Hidden Markov Model in Credit Card Fraud Detection
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V. Bhusari
2011-12-01
Full Text Available In modern retail market environment, electronic commerce has rapidly gained a lot of attention and alsoprovides instantaneous transactions. In electronic commerce, credit card has become the most importantmeans of payment due to fast development in information technology around the world. As the usage ofcredit card increases in the last decade, rate of fraudulent practices is also increasing every year.Existing fraud detection system may not be so much capable to reduce fraud transaction rate.Improvement in fraud detection practices has become essential to maintain existence of payment system.In this paper, we show how Hidden Markov Model (HMM is used to detect credit card fraud transactionwith low false alarm. An HMM based system is initially studied spending profile of the card holder andfollowed by checking an incoming transaction against spending behavior of the card holder, if it is notaccepted by our proposed HMM with sufficient probability, then it would be a fraudulent transaction.
Motion Imitation and Recognition using Parametric Hidden Markov Models
DEFF Research Database (Denmark)
Herzog, Dennis; Ude, Ales; Krüger, Volker
2008-01-01
extend the classical HMMs by introducing a joint parameterization of the observation densities, to simultaneously solve the problems of action recognition, parameterization of the observed actions, and action synthesis. The proposed approach was fully implemented on a humanoid robot HOAP-3. To evaluate......The recognition and synthesis of parametric movements play an important role in human-robot interaction. To understand the whole purpose of an arm movement of a human agent, both its recognition (e.g., pointing or reaching) as well as its parameterization (i.e., where the agent is pointing at) are...... important. Only together they convey the whole meaning of an action. Similarly, to imitate a movement, the robot needs to select the proper action and parameterize it, e.g., by the relative position of the object that needs to be grasped. We propose to utilize parametric hidden Markov models (PHMMs), which...
Topic Information Collection based on the Hidden Markov Model
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Hai-yan Jiang
2013-02-01
Full Text Available Specific-subject oriented information collection is one of the key technologies of vertical search engines, which directly affects the speed and relevance of search results. The topic information collection algorithm is widely used for its accuracy. The Hidden Markov Model (HMM is used to learn and judge the relevance between the Uniform Resource Locator (URL and the topic information. The Rocchio method is used to construct the prototype vectors relevant to the topic information, and the HMM is used to learn the preferred browsing paths. The concept maps including the semantics of the webpage are constructed and the web's link structures can be decided. The validity of the algorithm is proved by the experiment at last. Comparing with the Best-First algorithm, this algorithm can get more information pages and has higher precision ratio.
Modeling promoter grammars with evolving hidden Markov models
DEFF Research Database (Denmark)
Won, Kyoung-Jae; Sandelin, Albin; Marstrand, Troels Torben;
2008-01-01
, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk......MOTIVATION: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several...... factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation. RESULTS: With the goal of automatically deciphering such regulatory structures...
Comparison of glycosyltransferase families using the profile hidden Markov model.
Kikuchi, Norihiro; Kwon, Yeon-Dae; Gotoh, Masanori; Narimatsu, Hisashi
2003-10-17
In order to investigate the relationship between glycosyltransferase families and the motif for them, we classified 47 glycosyltransferase families in the CAZy database into four superfamilies, GTS-A, -B, -C, and -D, using a profile Hidden Markov Model method. On the basis of the classification and the similarity between GTS-A and nucleotidylyltransferase family catalyzing the synthesis of nucleotide-sugar, we proposed that ancient oligosaccharide might have been synthesized by the origin of GTS-B whereas the origin of GTS-A might be the gene encoding for synthesis of nucleotide-sugar as the donor and have evolved to glycosyltransferases to catalyze the synthesis of divergent carbohydrates. We also suggested that the divergent evolution of each superfamily in the corresponding subcellular component has increased the complexities of eukaryotic carbohydrate structure. PMID:14521949
Hidden Markov models for fault detection in dynamic systems
Smyth, Padhraic J. (Inventor)
1995-01-01
The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.
Hidden Markov Model Application to Transfer The Trader Online Forex Brokers
Directory of Open Access Journals (Sweden)
Farida Suharleni
2012-05-01
Full Text Available Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A from every broker forex online. Next step is making a probability of observation matrix (B by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1 is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.
Hidden Markov Models approach used for life parameters estimations
International Nuclear Information System (INIS)
In modern electronics and in electrical applications design is very important to be able to predict the actual product life or, at least, to be able to provide the end user with a reasonable estimate of such parameter. It is important to be able to define the availability as a key parameter because, although other performance indicators (as the mean time before failures MTBF or mean time to failure MTTF) exist, they are often misused. To study the availability of an electrical, electronic or an electromechanical system, different methods can be used. The most common one relies on memory-less Markovian state space analysis due to the fact that a little information is needed, and under simple hypothesis, it is possible to gather some outcomes on the availability of steady state value. In this paper the authors, starting from classical approach of Markov models, introduce an extension known as Hidden Markov Models approach to overcome the limits of the previous one in estimating the system availability performance over time. Such a technique can be used to improve the logistic aspects connected with optimal maintenance planning. The provided dissertation in general can be used in different contexts without losing in generality
Optimal State-Space Reduction for Pedigree Hidden Markov Models
Kirkpatrick, Bonnie
2012-01-01
To analyze whole-genome genetic data inherited in families, the likelihood is typically obtained from a Hidden Markov Model (HMM) having a state space of 2^n hidden states where n is the number of meioses or edges in the pedigree. There have been several attempts to speed up this calculation by reducing the state-space of the HMM. One of these methods has been automated in a calculation that is more efficient than the naive HMM calculation; however, that method treats a special case and the efficiency gain is available for only those rare pedigrees containing long chains of single-child lineages. The other existing state-space reduction method treats the general case, but the existing algorithm has super-exponential running time. We present three formulations of the state-space reduction problem, two dealing with groups and one with partitions. One of these problems, the maximum isometry group problem was discussed in detail by Browning and Browning. We show that for pedigrees, all three of these problems hav...
Volatility: a hidden Markov process in financial time series
Eisler, Z; Perello, J; Eisler, Zoltan; Masoliver, Jaume; Perello, Josep
2006-01-01
The volatility characterizes the amplitude of price return fluctuations. It is a central magnitude in finance closely related to the risk of holding a certain asset. Despite its popularity on trading floors, the volatility is unobservable and only the price is known. Diffusion theory has many common points with the research on volatility, the key of the analogy being that volatility is the time-dependent diffusion coefficient of the random walk for the price return. We present a formal procedure to extract volatility from price data, by assuming that it is described by a hidden Markov process which together with the price form a two-dimensional diffusion process. We derive a maximum likelihood estimate valid for a wide class of two-dimensional diffusion processes. The choice of the exponential Ornstein-Uhlenbeck (expOU) stochastic volatility model performs remarkably well in inferring the hidden state of volatility. The formalism is applied to the Dow Jones index. The main results are: (i) the distribution of...
Asymptotics of Entropy Rate in Special Families of Hidden Markov Chains
Han, Guangyue
2008-01-01
We derive an asymptotic formula for entropy rate of a hidden Markov chain around a "weak Black Hole". We also discuss applications of the asymptotic formula to the asymptotic behaviors of certain channels.
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
Identifying Seismicity Levels via Poisson Hidden Markov Models
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.
Statistical evaluation of Hidden Markov Models topologies, based on industrial synthetic model
Roblès, Bernard; Avila, Manuel; Duculty, Florent; Vrignat, Pascal; Kratz, Frédéric
2012-01-01
Prediction of physical particular phenomenon is based on knowledges of the phenomenon. Theses knowledges help us to conceptualize this phenomenon throw different models. Hidden Markov Models (HMM) can be used for modeling complex processes.We use this kind of models as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to evaluate three Hidden Markov Models topologies of Vrignat ...
Methods to choose the best Hidden Markov Model topology for improving maintenance policy
Roblès, Bernard; Avila, Manuel; Duculty, Florent; Vrignat, Pascal; Begot, Stéphane; Kratz, Frédéric
2012-01-01
Prediction of physical particular phenomenon is based on partial knowledge of this phenomenon. Theses knowledges help us to conceptualize this phenomenon according to di erent models. Hidden Markov Models (HMM) can be used for modeling complex processes. We use this kind of models as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to nd the best Hidden Markov Model topologies ...
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
Directory of Open Access Journals (Sweden)
Tan N Doan
Full Text Available 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
A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms
DEFF Research Database (Denmark)
Fang, Jiakun; Su, Chi; Hu, Weihao;
2015-01-01
is adopted to categorize the similar output patterns of several wind farms into joint states. Then the hidden Markov model (HMM) is then designed to describe the temporal correlations among these joint states. Unlike the conventional Markov chain model, the accumulated wind power is taken into consideration...
Approximation of the I-divergence between stationary and hidden Markov processes
L. Finesso; A. Grassi; P. Spreij
2008-01-01
We aim at the construction of a Hidden Markov Model (HMM) of assigned complexity (number of states of the underlying Markov chain) which best approximates, in Kullback-Leibler divergence rate, a given stationary process. We establish, under mild conditions, the existence of the divergence rate betwe
A Duration Hidden Markov Model for the Identification of Regimes in Stock Market Returns
DEFF Research Database (Denmark)
Ntantamis, Christos
This paper introduces a Duration Hidden Markov Model to model bull and bear market regime switches in the stock market; the duration of each state of the Markov Chain is a random variable that depends on a set of exogenous variables. The model not only allows the endogenous determination...
L. Finesso; A. Grassi; P. Spreij
2010-01-01
We propose a two-step algorithm for the construction of a Hidden Markov Model (HMM) of assigned size, i.e. cardinality of the state space of the underlying Markov chain, whose n-dimensional distribution is closest in divergence to a given distribution. The algorithm is based on the factorization of
Landmine detection using discrete hidden Markov models with Gabor features
Frigui, Hichem; Missaoui, Oualid; Gader, Paul
2007-04-01
We propose a general method for detecting landmine signatures in vehicle mounted ground penetrating radar (GPR) using discrete hidden Markov models and Gabor wavelet features. Observation vectors are constructed based on the expansion of the signature's B-scan using a bank of scale and orientation selective Gabor filters. This expansion provides localized frequency description that gets encoded in the observation sequence. These observations do not impose an explicit structure on the mine model, and are used to naturally model the time-varying signatures produced by the interaction of the GPR and the landmines as the vehicle moves. The proposed method is evaluated on real data collected by a GPR mounted on a moving vehicle at three different geographical locations that include several lanes. The model parameters are optimized using the BaumWelch algorithm, and lane-based cross-validation, in which each mine lane is in turn treated as a test set with the rest of the lanes used for training, is used to train and test the model. Preliminary results show that observations encoded with Gabor wavelet features perform better than observation encoded with gradient-based edge features.
Hidden Markov chain modeling for epileptic networks identification.
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
Hidden Markov Models for the Activity Profile of Terrorist Groups
Raghavan, Vasanthan; Tartakovsky, Alexander G
2012-01-01
The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and in general, tracking it over a period of time. Toward this goal, a d-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of d = 2 corresponds to the case where the dynamics are coarsely quantized as Active and Inactive, respectively. Two strategies for spurt detection and tracking are developed here: a model-independent strategy that uses the exponential weighted moving-average (EWMA) filter to track the strength of the group as measured by the number of attacks perpetrated by it, and a state estimation strategy that exploits the underlying HMM structure. The EWMA strategy is robust to modeling uncertainties and errors, and tracks persistent changes (changes that last for a sufficiently long duration) in the strength of the group. On the othe...
Hidden Markov Models as a Process Monitor in Robotic Assembly
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Geir E. Hovland
1999-10-01
Full Text Available A process monitor for robotic assembly based on hidden Markov models (HMMs is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system in which the models are trained off-line with the Baum-Welch reestimation algorithm. The assembly task is modeled as a discrete event dynamic system in which a discrete event is defined as a change in contact state between the workpiece and the environment. Our method (1 allows for dynamic motions of the workpiece, (2 accounts for sensor noise and friction, and (3 exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, the authors use them on-line in a 2D experimental setup to recognize discrete events as they occur. Successful event recognition with an accuracy as high as 97with a training set size of only 20 examples for each discrete event.
Optical character recognition of handwritten Arabic using hidden Markov models
Energy Technology Data Exchange (ETDEWEB)
Aulama, Mohannad M. [University of Jordan; Natsheh, Asem M. [University of Jordan; Abandah, Gheith A. [University of Jordan; Olama, Mohammed M [ORNL
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.
DNA Looping Kinetics Analyzed Using Diffusive Hidden Markov Model
Beausang, J F; Finzi, L; Manzo, C; Nelson, P C; Zurla, C; Beausang, John F.; Dunlap, David; Finzi, Laura; Manzo, Carlo; Nelson, Philip C.; Zurla, Chiara
2007-01-01
Tethered particle experiments use light microscopy to measure the position of a micrometer-sized bead tethered to a microscope slide via a ~micrometer length polymer, in order to infer the behavior of the invisible polymer. Currently, this method is used to measure rate constants of DNA loop formation and breakdown mediated by repressor protein that binds to the DNA. We report a new technique for measuring these rates using a modified hidden Markov analysis that directly incorporates the diffusive motion of the bead, which is an inherent complication of tethered particle motion because it occurs on a time scale between the sampling frequency and the looping time. We compare looping lifetimes found with our method, which are consistent over a range of sampling frequencies, to those obtained via the traditional threshold-crossing analysis, which vary depending on how the raw data are filtered in the time domain. Our method does not involve such filtering, and so can detect short-lived looping events and sudden ...
Use of Hidden Markov Mobility Model for Location Based Services
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Bhakti D. Shelar
2014-07-01
Full Text Available These days people prefer to use portable and wireless devices such as laptops, mobile phones, They are connected through satellites. As user moves from one point to other, task of updating stored information becomes difficult. Provision of Location based services to users, faces some challenges like limited bandwidth and limited client power. To optimize data accessibility and to minimize access cost, we can store frequently accessed data item in cache of client. So small size of cache is introduced in mobile devices. Data fetched from server is stored on cache. So requested data from user is provided from cache and not from remote server. Question arises that which data should be kept in the cache? Performance of cache majorly depends on the cache replacement policies which select data suitable for eviction from cache. This paper presents use of Hidden Markov Models(HMMs for prediction of user‟s future location. Then data item irrelevant to this predicted location is fetched out from the cache. The proposed approach clusters location histories according to their location characteristics and also it considers each user‟s previous actions. This results in producing high packet delivery ratio and minimum delay.
Clustering Multivariate Time Series Using Hidden Markov Models
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Shima Ghassempour
2014-03-01
Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.
Analysis of nanopore data using hidden Markov models
Schreiber, Jacob; Karplus, Kevin
2015-01-01
Motivation: Nanopore-based sequencing techniques can reconstruct properties of biosequences by analyzing the sequence-dependent ionic current steps produced as biomolecules pass through a pore. Typically this involves alignment of new data to a reference, where both reference construction and alignment have been performed by hand. Results: We propose an automated method for aligning nanopore data to a reference through the use of hidden Markov models. Several features that arise from prior processing steps and from the class of enzyme used can be simply incorporated into the model. Previously, the M2MspA nanopore was shown to be sensitive enough to distinguish between cytosine, methylcytosine and hydroxymethylcytosine. We validated our automated methodology on a subset of that data by automatically calculating an error rate for the distinction between the three cytosine variants and show that the automated methodology produces a 2–3% error rate, lower than the 10% error rate from previous manual segmentation and alignment. Availability and implementation: The data, output, scripts and tutorials replicating the analysis are available at https://github.com/UCSCNanopore/Data/tree/master/Automation. Contact: karplus@soe.ucsc.edu or jmschreiber91@gmail.com Supplementary information: Supplementary data are available from Bioinformatics online. PMID:25649617
Drum Sound Detection in Polyphonic Music with Hidden Markov Models
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Jouni Paulus
2009-01-01
Full Text Available This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs. The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations.
A hidden markov model derived structural alphabet for proteins.
Camproux, A C; Gautier, R; Tufféry, P
2004-06-01
Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction. PMID:15147844
Long memory of financial time series and hidden Markov models with time-varying parameters
DEFF Research Database (Denmark)
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
Hidden Markov models are often used to capture stylized facts of daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior for the ability to reproduce the stylized...
Group association test using a hidden Markov model.
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
Markov Chain Monte Carlo Bayesian Learning for Neural Networks
Goodrich, Michael S.
2011-01-01
Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.
Segmentation of cone-beam CT using a hidden Markov random field with informative priors
Moores, M.; Hargrave, C.; Harden, F.; Mengersen, K.
2014-03-01
Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.
HIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTS
Directory of Open Access Journals (Sweden)
Pornpit Sirima
2014-01-01
Full Text Available Monthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a “defective” or “non-defective” state. The number of machine parts errors were collected from a thermo plastic injection molding machine in a car bumper auto parts manufacturer in Liberec city, Czech Republic from January 2012 to November 2012. A Bayesian method is used for parameter estimation. The results of this study indicate that the machine part error correction and the amount of time spent on the error correction do not improve the machine part status of the individual part, but there is a very strong month-to-month dependence of the machine part states. Using the Mean Absolute Error (MAE criterion, the performance of the proposed model (MAE = 1.62 and the HMM including machine part error correction only (MAE = 1.68, from our previous study, is not significantly different. However, the proposed model has more advantage in the fact that the machine part state can be explained by both the machine part error correction and the amount of time spent on the error correction.
Segmentation of cone-beam CT using a hidden Markov random field with informative priors
International Nuclear Information System (INIS)
Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.
Recursive smoothers for hidden discrete-time Markov chains
Directory of Open Access Journals (Sweden)
Lakhdar Aggoun
2005-01-01
Full Text Available We consider a discrete-time Markov chain observed through another Markov chain. The proposed model extends models discussed by Elliott et al. (1995. We propose improved recursive formulae to update smoothed estimates of processes related to the model. These recursive estimates are used to update the parameter of the model via the expectation maximization (EM algorithm.
Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli
2016-04-01
This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.
Swallowing sound detection using hidden markov modeling of recurrence plot features
Energy Technology Data Exchange (ETDEWEB)
Aboofazeli, Mohammad [Faculty of Engineering, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6 (Canada)], E-mail: umaboofa@cc.umanitoba.ca; Moussavi, Zahra [Faculty of Engineering, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6 (Canada)], E-mail: mousavi@ee.umanitoba.ca
2009-01-30
Automated detection of swallowing sounds in swallowing and breath sound recordings is of importance for monitoring purposes in which the recording durations are long. This paper presents a novel method for swallowing sound detection using hidden Markov modeling of recurrence plot features. Tracheal sound recordings of 15 healthy and nine dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using the Taken method of delays. The sequences of three recurrence plot features of the reconstructed trajectories (which have shown discriminating capability between swallowing and breath sounds) were modeled by three hidden Markov models. The Viterbi algorithm was used for swallowing sound detection. The results were validated manually by inspection of the simultaneously recorded airflow signal and spectrogram of the sounds, and also by auditory means. The experimental results suggested that the performance of the proposed method using hidden Markov modeling of recurrence plot features was superior to the previous swallowing sound detection methods.
Makino, Takaki; Takei, Shunsuke; Sato, Issei; Mochihashi, Daichi
2011-01-01
We propose a restricted collapsed draw (RCD) sampler, a general Markov chain Monte Carlo sampler of simultaneous draws from a hierarchical Chinese restaurant process (HCRP) with restriction. Models that require simultaneous draws from a hierarchical Dirichlet process with restriction, such as infinite Hidden markov models (iHMM), were difficult to enjoy benefits of \\markerg{the} HCRP due to combinatorial explosion in calculating distributions of coupled draws. By constructing a proposal of se...
Using frame correlation algorithm in a duration distribution based hidden Markov model
Institute of Scientific and Technical Information of China (English)
王作英; 崔小东
2000-01-01
The assumption of frame independence is a widely known weakness of traditional hidden Markov model (HMM). In this paper, a frame correlation algorithm based on the duration distribution based hidden Markov model (DDBHMM) is proposed. In the algorithm, an AR model is used to depict the low pass effect of vocal tract from which stems the inertia leading to frame correlation. In the preliminary experiment of middle vocabulary speaker dependent isolated word recognition, our frame correlation algorithm outperforms the frame independent one. The average error reduction is about 20% .
Comparison of State Estimation Using Finite Mixtures and Hidden Markov Models
Czech Academy of Sciences Publication Activity Database
Nagy, I.; Suzdaleva, Evgenia; Mlynářová, Tereza
Piscataway : IEEE, 2011, s. 527-531. ISBN 978-1-4577-1424-5. [6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. Prague (CZ), 15.09.2011-17.09.2011] R&D Projects: GA MŠk 1M0572; GA TA ČR TA01030123 Institutional research plan: CEZ:AV0Z10750506 Keywords : mixture models * hidden Markov models * state estimation * online estimation Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2011/AS/nagy-comparison of state estimation using finite mixtures and hidden markov models.pdf
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
Markov Model of Wind Power Time Series UsingBayesian Inference of Transition Matrix
DEFF Research Database (Denmark)
Chen, Peiyuan; Berthelsen, Kasper Klitgaard; Bak-Jensen, Birgitte;
2009-01-01
This paper proposes to use Bayesian inference of transition matrix when developing a discrete Markov model of a wind speed/power time series and 95% credible interval for the model verification. The Dirichlet distribution is used as a conjugate prior for the transition matrix. Three discrete Markov...
A non-parametric hidden Markov model for climate state identification
Lambert, M. F.; Whiting, J. P.; Metcalfe, A. V.
2003-01-01
Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem i...
Douc, Randal; Moulines, Eric; Ritov, Ya'Acov
2007-01-01
21 We give simple conditions that ensure exponential forgetting of the initial conditions of the filter for general state-space hidden Markov chain. The proofs are based on the coupling argument applied to the posterior Markov kernels. These results are useful both for filtering hidden Markov models using approximation methods (e.g., particle filters) and for proving asymptotic properties of estimators. The results are general enough to cover models like the Gaussian state space model, wit...
Bayesian inference for Markov jump processes with informative observations.
Golightly, Andrew; Wilkinson, Darren J
2015-04-01
In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis. PMID:25720091
Ensemble bayesian model averaging using markov chain Monte Carlo sampling
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Diks, Cees G H [NON LANL; Clark, Martyn P [NON LANL
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.
Prediction of signal peptides and signal anchors by a hidden Markov model
DEFF Research Database (Denmark)
Krogh, Anders Stærmose; Nielsen, Henrik
1998-01-01
A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our ...
Stylised facts of financial time series and hidden Markov models in continuous time
DEFF Research Database (Denmark)
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
2015-01-01
Hidden Markov models are often applied in quantitative finance to capture the stylised facts of financial returns. They are usually discrete-time models and the number of states rarely exceeds two because of the quadratic increase in the number of parameters with the number of states. This paper...
Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms
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…
Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments
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.
Nonlinear Filters for Hidden Markov Models of Regime Change with Fast Mean-Reverting States
Papanicolaou, Andrew
2012-01-01
We consider filtering for a hidden Markov model that evolves with multiple time scales in the hidden states. In particular, we consider the case where one of the states is a scaled Ornstein-Uhlenbeck process with fast reversion to a shifting-mean that is controlled by a continuous time Markov chain modeling regime change. We show that the nonlinear filter for such a process can be approximated by an averaged filter that asymptotically coincides with the true nonlinear filter of the regime-changing Markov chain as the rate of mean reversion approaches infinity. The asymptotics exploit weak converge of the state variables to an invariant distribution, which is significantly different from the strong convergence used to obtain asymptotic results in "Filtering for Fast Mean-Reverting Processes" (19).
Segmentation of laser range radar images using hidden Markov field models
International Nuclear Information System (INIS)
Segmentation of images in the context of model based stochastic techniques is connected with high, very often unpracticle computational complexity. The objective with this thesis is to take the models used in model based image processing, simplify and use them in suboptimal, but not computationally demanding algorithms. Algorithms that are essentially one-dimensional, and their extensions to two dimensions are given. The model used in this thesis is the well known hidden Markov model. Estimation of the number of hidden states from observed data is a problem that is addressed. The state order estimation problem is of general interest and is not specifically connected to image processing. An investigation of three state order estimation techniques for hidden Markov models is given. 76 refs
Estimation of the occurrence rate of strong earthquakes based on hidden semi-Markov models
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.
Noe, Frank; Prinz, Jan-Hendrik; Plattner, Nuria
2013-01-01
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 molecula...
Bayesian analysis of variable-order, reversible Markov chains
Bacallado, Sergio
2011-01-01
We define a conjugate prior for the reversible Markov chain of order $r$. The prior arises from a partially exchangeable reinforced random walk, in the same way that the Beta distribution arises from the exchangeable Poly\\'{a} urn. An extension to variable-order Markov chains is also derived. We show the utility of this prior in testing the order and estimating the parameters of a reversible Markov model.
Finesso, L; Spreij, P
2010-01-01
We propose a two-step algorithm for the construction of a Hidden Markov Model (HMM) of assigned size, i.e. cardinality of the state space of the underlying Markov chain, whose $n$-dimensional distribution is closest in divergence to a given distribution. The algorithm is based on the factorization of a pseudo Hankel matrix, defined in terms of the given distribution, into the product of a tall and a wide nonnegative matrix. The implementation is based on the nonnegative matrix factorization (NMF) algorithm. To evaluate the performance of our algorithm we produced some numerical simulations in the context of HMM order reduction.
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.
Gender Based Emotion Recognition System for Telugu Rural Dialects Using Hidden Markov Models
D, Prasad Reddy P V G; Srinivas, Y; Brahmaiah, P
2010-01-01
Automatic emotion recognition in speech is a research area with a wide range of applications in human interactions. The basic mathematical tool used for emotion recognition is Pattern recognition which involves three operations, namely, pre-processing, feature extraction and classification. This paper introduces a procedure for emotion recognition using Hidden Markov Models (HMM), which is used to divide five emotional states: anger, surprise, happiness, sadness and neutral state. The approach is based on standard speech recognition technology using hidden continuous markov model by selection of low level features and the design of the recognition system. Emotional Speech Database from Telugu Rural Dialects of Andhra Pradesh (TRDAP) was designed using several speaker's voices comprising the emotional states. The accuracy of recognizing five different emotions for both genders of classification is 80% for anger-emotion which is achieved by using the best combination of 39-dimensioanl feature vector for every f...
Tech Report A Variational HEM Algorithm for Clustering Hidden Markov Models
Coviello, Emanuele; Lanckriet, Gert R G
2011-01-01
The hidden Markov model (HMM) is a generative model that treats sequential data under the assumption that each observation is conditioned on the state of a discrete hidden variable that evolves in time as a Markov chain. In this paper, we derive a novel algorithm to cluster HMMs through their probability distributions. We propose a hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a "cluster center", i.e., a novel HMM that is representative for the group. We present several empirical studies that illustrate the benefits of the proposed algorithm.
Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
Durand, Jean-Baptiste
2012-01-01
This report addresses state inference for hidden Markov models. These models rely on unobserved states, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of state uncertainty. The entropy of the state sequence that explains an observed sequence for a given hidden Markov chain model can be considered as the canonical measure of state sequence uncertainty. This canonical measure of state sequence uncertainty is not reflected by the classic multivariate state profiles computed by the smoothing algorithm, which summarizes the possible state sequences. Here, we introduce a new type of profiles which have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of state sequence uncertainty along the sequence and makes it possible to localize this uncertainty, (ii) these profiles are univariate and thus remain easily interpretable on tree structures. We show how to extend the smoothing algori...
Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models
Directory of Open Access Journals (Sweden)
Richard Washington
2008-11-01
Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T- intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.
Directory of Open Access Journals (Sweden)
M. Beyreuther
2011-02-01
Full Text Available Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms (earthquakes are often hard to detect and classify. Here, we propose to use models from speech synthesis which extend the double stochastic models from speech recognition by integrating a more realistic duration of the target waveforms. The method, which has general applicability, is applied to earthquake detection and classification. First, we generate characteristic functions from the time-series. The Hidden semi-Markov Models are estimated from the characteristic functions and Weighted Finite-State Transducers are constructed for the classification. We test our scheme on one month of continuous seismic data, which corresponds to 370 151 classifications, showing that incorporating the time dependency explicitly in the models significantly improves the results compared to Hidden Markov Models.
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Mohammad-Djafari, Ali
2007-01-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali
2004-11-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Mesa, Andrea; Basterrech, Sebastián; Guerberoff, Gustavo; Alvarez-Valin, Fernando
2015-01-01
The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodicall...
Capturing Human Motion based on Modified Hidden Markov Model in Multi-View Image Sequences
Yanan Liu; Lian Kun Jia; Wen Yu Yu
2014-01-01
Human motion capturing is of great importance in video information retrieval, hence, in this paper, we propose a novel approach to effectively capturing human motions based on modified hidden markov model from multi-view image sequences. Firstly, the structure of the human skeleton model is illustrated, which is extended from skeleton root and spine root, and this skeleton consists of right leg, left leg and spine. Secondly, our proposed human motion capturing system is made up of data traini...
A complete solution to Blackwell's unique ergodicity problem for hidden Markov chains
Chigansky, Pavel
2009-01-01
We develop necessary and sufficient conditions for uniqueness of the invariant measure of the filtering process associated to an ergodic hidden Markov model in a finite or countable state space. These results provide a complete solution to a problem posed by Blackwell (1957), and subsume earlier partial results due to Kaijser, Kochman and Reeds. The proofs of our main results are based on the stability theory of nonlinear filters.
Suvorova, S.; Sun, L; Melatos, A.; Moran, W.; Evans, R J
2016-01-01
Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g. superfluid or magnetospheric) or external (e.g. accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F-statistic to surmount ...
A non-parametric hidden Markov model for climate state identification
Lambert, M. F.; Whiting, J. P.; Metcalfe, A. V.
2003-01-01
Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hi...
A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data
Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing
2015-01-01
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a ro...
Quantile Forecasting for Credit Risk Management using Possibly Mis-specified Hidden Markov Models
Banachewicz, K.P.; Lucas, A
2007-01-01
Recent models for credit risk management make use of Hidden Markov Models (HMMs). The HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially mis-specified. In this paper, we focus on mis-specification in the dynamics and the dimension of the HMM. We consider both discrete and continuous state HMMs. The differences are substantial. Underestimating the number of discrete states has an ec...
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
van de Meent, Jan-Willem; Bronson, Jonathan E.; Wood, Frank; Gonzalez Jr., Ruben L.; Wiggins, Chris H.
2013-01-01
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attemp...
He, Zhiquan; Ma, Wenji; Zhang, Jingfen; Xu, Dong
2014-01-01
Protein structure Quality Assessment (QA) is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA. In this work, we developed a new Hidden Markov Model (HMM) to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary struct...
Online Expectation Maximization based algorithms for inference in hidden Markov models
Le Corff, Sylvain; Fort, Gersende
2011-01-01
The Expectation Maximization (EM) algorithm is a versatile tool for model parameter estimation in latent data models. When processing large data sets or data stream however, EM becomes intractable since it requires the whole data set to be available at each iteration of the algorithm. In this contribution, a new generic online EM algorithm for model parameter inference in general Hidden Markov Model is proposed. This new algorithm updates the parameter estimate after a block of observations i...
Implementation of a Connected Digit Recognizer Using Continuous Hidden Markov Modeling
Srichai, Panaithep Albert
1998-01-01
This thesis describes the implementation of a speaker dependent connected-digit recognizer using continuous Hidden Markov Modeling (HMM). The speech recognition system was implemented using MATLAB and on the ADSP-2181, a digital signal processor manufactured by Analog Devices. Linear predictive coding (LPC) analysis was first performed on a speech signal to model the characteristics of the vocal tract filter. A 7 state continuous HMM with 4 mixture density components was used to model e...
A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition
Shi-guang Yue; Peng Jiao; Ya-bing Zha; Quan-jun Yin
2015-01-01
Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the ...
Bayesian inference of BWR model parameters by Markov chain Monte Carlo
International Nuclear Information System (INIS)
In this paper, the Markov chain Monte Carlo approach to Bayesian inference is applied for estimating the parameters of a reduced-order model of the dynamics of a boiling water reactor system. A Bayesian updating strategy is devised to progressively refine the estimates, as newly measured data become available. Finally, the technique is used for detecting parameter changes during the system lifetime, e.g. due to component degradation
Energy Technology Data Exchange (ETDEWEB)
M. Ghil (UCLA), PI; S. Kravtsov (UWM); A. W. Robertson (IRI); P. Smyth (UCI)
2008-10-14
In this project we developed further a twin approach to the study of regional-scale climate variability and change. The two approaches involved probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs). We thus made progress in identifying the predictable modes of climate variability and investigating their impacts on the regional scale. In previous work sponsored by DOEÃ¢ÂÂs Climate Change Prediction Program (CCPP), we had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale seasonal predictions of general circulation models (GCMs). Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might inÃ¯Â¬Âuence large-scale atmospheric circulation patterns on interannual and longer time scales; similar patterns were found in a hybrid coupled oceanÃ¢ÂÂatmosphereÃ¢ÂÂsea-ice model. In this continuation project, we built on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled oceanÃ¢ÂÂatmosphere modes. Our main project results consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM simulations, called empirical mode reduction (EMR); and observational studies of decadal and multi-decadal natural climate variability, informed by ICM simulations. A particularly timely by-product of this work is an extensive study of clustering of cyclone tracks in the extratropical Atlantic and the western
Capturing Human Motion based on Modified Hidden Markov Model in Multi-View Image Sequences
Directory of Open Access Journals (Sweden)
Yanan Liu
2014-01-01
Full Text Available Human motion capturing is of great importance in video information retrieval, hence, in this paper, we propose a novel approach to effectively capturing human motions based on modified hidden markov model from multi-view image sequences. Firstly, the structure of the human skeleton model is illustrated, which is extended from skeleton root and spine root, and this skeleton consists of right leg, left leg and spine. Secondly, our proposed human motion capturing system is made up of data training module and human motion capturing module. In the data training module, multi-views motion information is extracted from a human motion database, and feature database of human motion capturing is constructed through combining multi-views motions. In the human motion capturing module, results of motion capturing can be achieved through motion classification based on a modified hidden markov model. Thirdly, the modified hidden markov model is designed by utilizing the fuzzy measure, fuzzy integer, and fuzzy intersection operator through a scaling process. Finally, a standard motion capture dataset- MPI08_Database is utilized to make performance evaluation. Compared with the existing methods, the proposed approach can effectively capture human motions with high precision
Hidden Markov models and other machine learning approaches in computational molecular biology
Energy Technology Data Exchange (ETDEWEB)
Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)
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.
Analysis of an optimal hidden Markov model for secondary structure prediction
Directory of Open Access Journals (Sweden)
Gibrat Jean-François
2006-12-01
Full Text Available Abstract Background Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. Results Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model α-helices, 12 that model coil and 9 that model β-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. Conclusion The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.
An Approach of Diagnosis Based On The Hidden Markov Chains Model
Directory of Open Access Journals (Sweden)
Karim Bouamrane
2008-07-01
Full Text Available Diagnosis is a key element in industrial system maintenance process performance. A diagnosis tool is proposed allowing the maintenance operators capitalizing on the knowledge of their trade and subdividing it for better performance improvement and intervention effectiveness within the maintenance process service. The Tool is based on the Markov Chain Model and more precisely the Hidden Markov Chains (HMC which has the system failures determination advantage, taking into account the causal relations, stochastic context modeling of their dynamics and providing a relevant diagnosis help by their ability of dubious information use. Since the FMEA method is a well adapted artificial intelligence field, the modeling with Markov Chains is carried out with its assistance. Recently, a dynamic programming recursive algorithm, called 'Viterbi Algorithm', is being used in the Hidden Markov Chains field. This algorithm provides as input to the HMC a set of system observed effects and generates at exit the various causes having caused the loss from one or several system functions.
A non-parametric hidden Markov model for climate state identification
Directory of Open Access Journals (Sweden)
M. F. Lambert
2003-01-01
Full Text Available Hidden Markov models (HMMs can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions
Image Edge Detection Using Hidden Markov Chain Model Based on the Non-decimated Wavelet
Directory of Open Access Journals (Sweden)
Renqi Zhang
2009-03-01
Full Text Available Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift invariant, in this paper, we develop a new edge detection technique using Hidden Markov Chain (HMC model. With this proposed model (NWHMC, each wavelet coefficient contains a hidden state, herein, we adopt Laplacian model and Gaussian model to represent the information of the state “big” and the state “small”. The model can be trained by EM algorithm, and then we employ Viterbi algorithm to reveal the hidden state of each coefficient according to MAP estimation. The detecting results of several images are provided to evaluate the algorithm. In addition, the algorithm can be applied to noisy images efficiently.
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
DEFF Research Database (Denmark)
Hauberg, Søren; Sloth, Jakob
2008-01-01
For many years, the hidden Markov model (HMM) has been one of the most popular tools for analysing sequential data. One frequently used special case is the left-right model, in which the order of the hidden states is known. If knowledge of the duration of a state is available it is not possible to...
Two-stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction
Directory of Open Access Journals (Sweden)
Nhan Nguyen-Duc-Thanh
2012-07-01
Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human‐Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2‐stages Hidden Markov Model. The 1st HMM is to recognize the prime command‐like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.
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.
A path-independent method for barrier option pricing in hidden Markov models
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.
Consistency of the Maximum Likelihood Estimator for general hidden Markov models
Douc, Randal; Olsson, Jimmy; Van Handel, Ramon
2009-01-01
Consider a parametrized family of general hidden Markov models, where both the observed and unobserved components take values in a complete separable metric space. We prove that the maximum likelihood estimator (MLE) of the parameter is strongly consistent under a rather minimal set of assumptions. As special cases of our main result, we obtain consistency in a large class of nonlinear state space models, as well as general results on linear Gaussian state space models and finite state models. A novel aspect of our approach is an information-theoretic technique for proving identifiability, which does not require an explicit representation for the relative entropy rate. Our method of proof could therefore form a foundation for the investigation of MLE consistency in more general dependent and non-Markovian time series. Also of independent interest is a general concentration inequality for $V$-uniformly ergodic Markov chains.
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
Institute of Scientific and Technical Information of China (English)
Zhao Zhi-Jin; Zheng Shi-Lian; Xu Chun-Yun; Kong Xian-Zheng
2007-01-01
Hidden Markov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA's ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.
Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors
Directory of Open Access Journals (Sweden)
Zhang Yingjun
2015-02-01
Full Text Available In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.
Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction
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.
A New Modular Strategy For Action Sequence Automation Using Neural Networks And Hidden Markov Models
Mohamed Adel Taher; Mostapha Abdeljawad
2013-01-01
In this paper, the authors propose a new hybrid strategy (using artificial neural networks and hidden Markov models) for skill automation. The strategy is based on the concept of using an â€œadaptive desiredâ€ that is introduced in the paper. The authors explain how using an adaptive desired can help a system for which an explicit model is not available or is difficult to obtain to smartly cope with environmental disturbances without requiring explicit rules specification (as with fuzzy syste...
Recovering the state sequence of hidden Markov models using mean-field approximations
International Nuclear Information System (INIS)
Inferring the sequence of states from observations is one of the most fundamental problems in hidden Markov models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transfer matrix methods, it becomes quickly intractable when the underlying state space is large. This paper develops several low complexity approximate algorithms to address this inference problem when the state space becomes large. The new algorithms are based on various mean-field approximations of the transfer matrix. Their performances are studied in detail on a simple realistic model for DNA pyrosequencing
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
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.
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
LDA Based Face Recognition by Using Hidden Markov Model in Current Trends
Directory of Open Access Journals (Sweden)
S.Sharavanan
2009-10-01
Full Text Available Hidden Markov model (HMM is a promising method that works well for images with variations in lighting, facial expression, and orientation. Face recognition draws attention as a complex task due to noticeable changes produced on appearance by illumination, facial expression, size, orientation and other external factors. To process images using HMM, the temporal or space sequences are to be considered. In simple terms HMM can be defined as set of finite states with associated probability distributions. Only the outcome is visible to the external user not the states and hence the name Hidden Markov Model. The paper deals with various techniques and methodologies used for resolving the problem .We discuss about appearance based, feature based, model based and hybrid methods for face identification. Conventional techniques such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Independent Component Analysis (ICA, and feature based Elastic Bunch Graph Matching (EBGM and 2D and 3D face models are well-known for face detection and recognition.
Speech-To-Text Conversion STT System Using Hidden Markov Model HMM
Directory of Open Access Journals (Sweden)
Su Myat Mon
2015-06-01
Full Text Available Abstract Speech is an easiest way to communicate with each other. Speech processing is widely used in many applications like security devices household appliances cellular phones ATM machines and computers. The human computer interface has been developed to communicate or interact conveniently for one who is suffering from some kind of disabilities. Speech-to-Text Conversion STT systems have a lot of benefits for the deaf or dumb people and find their applications in our daily lives. In the same way the aim of the system is to convert the input speech signals into the text output for the deaf or dumb students in the educational fields. This paper presents an approach to extract features by using Mel Frequency Cepstral Coefficients MFCC from the speech signals of isolated spoken words. And Hidden Markov Model HMM method is applied to train and test the audio files to get the recognized spoken word. The speech database is created by using MATLAB.Then the original speech signals are preprocessed and these speech samples are extracted to the feature vectors which are used as the observation sequences of the Hidden Markov Model HMM recognizer. The feature vectors are analyzed in the HMM depending on the number of states.
Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea; Montalto, Placido; Patanè, Domenico; Privitera, Eugenio
2016-04-01
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.
Cardiac arrhythmia detection by parameters sharing and MMIE training of Hidden Markov Models.
Lima, Carlos S; Cardoso, Manuel J
2007-01-01
This paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models and Maximum Mutual Information Estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fibrillation and normal beats are morphologically similar except that the former does not exhibit the P wave. In fact there are more differences as the irregularity of the RR interval, but ventricular conduction in AF is normal in morphology. Regarding to the Hidden Markov Models (HMM) modelling this can mean that these two classes can be modelled by HMM's of similar topology and sharing some parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMIE training, and saving computational resources at run-time decoding. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where Maximum Likelihood Estimation training is used alone. PMID:18002835
Bayesian estimation for a parametric Markov Renewal model applied to seismic data
Epifani, I.; Ladelli, L.; Pievatolo, A.
2014-01-01
This paper presents a complete methodology for Bayesian inference on a semi-Markov process, from the elicitation of the prior distribution, to the computation of posterior summaries, including a guidance for its implementation. The inter-occurrence times (conditional on the transition between two given states) are assumed to be Weibull-distributed. We examine the elicitation of the joint prior density of the shape and scale parameters of the Weibull distributions, deriving a specific class of...
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
2000-01-01
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...
Unsupervised SAR images change detection with hidden Markov chains on a sliding window
Bouyahia, Zied; Benyoussef, Lamia; Derrode, Stéphane
2007-10-01
This work deals with unsupervised change detection in bi-date Synthetic Aperture Radar (SAR) images. Whatever the indicator of change used, e.g. log-ratio or Kullback-Leibler divergence, we have observed poor quality change maps for some events when using the Hidden Markov Chain (HMC) model we focus on in this work. The main reason comes from the stationary assumption involved in this model - and in most Markovian models such as Hidden Markov Random Fields-, which can not be justified in most observed scenes: changed areas are not necessarily stationary in the image. Besides the few non stationary Markov models proposed in the literature, the aim of this paper is to describe a pragmatic solution to tackle stationarity by using a sliding window strategy. In this algorithm, the criterion image is scanned pixel by pixel, and a classical HMC model is applied only on neighboring pixels. By moving the window through the image, the process is able to produce a change map which can better exhibit non stationary changes than the classical HMC applied directly on the whole criterion image. Special care is devoted to the estimation of the number of classes in each window, which can vary from one (no change) to three (positive change, negative change and no change) by using the corrected Akaike Information Criterion (AICc) suited to small samples. The quality assessment of the proposed approach is achieved with speckle-simulated images in which simulated changes is introduced. The windowed strategy is also evaluated with a pair of RADARSAT images bracketing the Nyiragongo volcano eruption event in January 2002. The available ground truth confirms the effectiveness of the proposed approach compared to a classical HMC-based strategy.
Braak, ter C.J.F.
2004-01-01
Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likeli
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
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…
Chadeau-Hyam, Marc; Tubert-Bitter, Pascale; Guihenneuc-Jouyaux, Chantal; Campanella, Gianluca; Richardson, Sylvia; Vermeulen, Roel; De Iorio, Maria; Galea, Sandro; Vineis, Paolo
2014-01-01
BACKGROUND:: To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer. METHODS:: The model was fit
Video object's behavior analyzing based on motion history image and hidden markov model
Institute of Scientific and Technical Information of China (English)
Meng Fanfeng; Qu Zhenshen; Zeng Qingshuang; Li li
2009-01-01
A novel method was proposed, which extracted video object's track and analyzed video object's behavior. Firstly, this method tracked the video object based on motion history image, and obtained the coordinate-based track sequence and orientation-based track sequence of the video object. Then the proposed hidden markov model (HMM) based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects' behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object's various behaviors accurately. It provides a base for further research on video object behavior.
Products of Hidden Markov Models: It Takes N>1 to Tango
Taylor, Graham W
2012-01-01
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under the model. In this paper, we demonstrate how the partition function can be estimated reliably via Annealed Importance Sampling. We perform experiments using contrastive divergence learning on rainfall data and data captured from pairs of people dancing. Our results suggest that advances in learning and evaluation for undirected graphical models and recent increases in available computing power make PoHMMs worth considering for complex time-series modeling tasks.
Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model
Wang, Lin; Yang, Chunjie; Sun, Youxian; Pan, Yijun; An, Ruqiao
2015-11-01
Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.
An alternative to the Baum-Welch recursions for hidden Markov models
Bartolucci, Francesco
2012-01-01
We develop a recursion for hidden Markov model of any order h, which allows us to obtain the posterior distribution of the latent state at every occasion, given the previous h states and the observed data. With respect to the well-known Baum-Welch recursions, the proposed recursion has the advantage of being more direct to use and, in particular, of not requiring dummy renormalizations to avoid numerical problems. We also show how this recursion may be expressed in matrix notation, so as to allow for an efficient implementation, and how it may be used to obtain the manifest distribution of the observed data and for parameter estimation within the Expectation-Maximization algorithm. The approach is illustrated by an application to financial data which is focused on the study of the dynamics of the volatility level of log-returns.
Hand Gesture Spotting Based on 3D Dynamic Features Using Hidden Markov Models
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).
3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.
Lavigne, François; Collet, Christophe; Armspach, Jean-Paul
2014-01-01
In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images. PMID:25571045
Sequential Monte Carlo smoothing for general state space hidden Markov models
Douc, Randal; Moulines, Eric; Olsson, Jimmy; 10.1214/10-AAP735
2012-01-01
Computing smoothing distributions, the distributions of one or more states conditional on past, present, and future observations is a recurring problem when operating on general hidden Markov models. The aim of this paper is to provide a foundation of particle-based approximation of such distributions and to analyze, in a common unifying framework, different schemes producing such approximations. In this setting, general convergence results, including exponential deviation inequalities and central limit theorems, are established. In particular, time uniform bounds on the marginal smoothing error are obtained under appropriate mixing conditions on the transition kernel of the latent chain. In addition, we propose an algorithm approximating the joint smoothing distribution at a cost that grows only linearly with the number of particles.
Nastou, Katerina C; Tsaousis, Georgios N; Papandreou, Nikos C; Hamodrakas, Stavros J
2016-07-01
A large number of modular domains that exhibit specific lipid binding properties are present in many membrane proteins involved in trafficking and signal transduction. These domains are present in either eukaryotic peripheral membrane or transmembrane proteins and are responsible for the non-covalent interactions of these proteins with membrane lipids. Here we report a profile Hidden Markov Model based method capable of detecting Membrane Binding Proteins (MBPs) from information encoded in their amino acid sequence, called MBPpred. The method identifies MBPs that contain one or more of the Membrane Binding Domains (MBDs) that have been described to date, and further classifies these proteins based on their position in respect to the membrane, either as peripheral or transmembrane. MBPpred is available online at http://bioinformatics.biol.uoa.gr/MBPpred. This method was applied in selected eukaryotic proteomes, in order to examine the characteristics they exhibit in various eukaryotic kingdoms and phyla. PMID:27048983
A Face Recognition System by Embedded Hidden Markov Model and Discriminating Set Approach
Directory of Open Access Journals (Sweden)
Vitthal Suryakant Phad
2014-07-01
Full Text Available Different approaches have been proposed over the last few years for improving holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. There has been also some research about the combination of different recognition methods, both at the feature and score levels. Embedded hidden Markov model (E-HHM has been widely used in pattern recognition. The performance of Face recognition by E-HMM heavily depends on the choice of model parameters. In this paper, we propose a discriminating set of multi E-HMMs based face recognition algorithm. Experimental results illustrate that compared with the conventional HMM based face recognition algorithm the proposed method obtain better recognition accuracies and higher generalization ability.
Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model
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
A hidden Markov model combined with climate indices for multidecadal streamflow simulation
Bracken, C.; Rajagopalan, B.; Zagona, E.
2014-10-01
Hydroclimate time series often exhibit very low year-to-year autocorrelation while showing prolonged wet and dry epochs reminiscent of regime-shifting behavior. Traditional stochastic time series models cannot capture the regime-shifting features thereby misrepresenting the risk of prolonged wet and dry periods, consequently impacting management and planning efforts. Upper Colorado River Basin (UCRB) annual flow series highlights this clearly. To address this, a simulation framework is developed using a hidden Markov (HM) model in combination with large-scale climate indices that drive multidecadal variability. We demonstrate this on the UCRB flows and show that the simulations are able to capture the regime features by reproducing the multidecadal spectral features present in the data where a basic HM model without climate information cannot.
On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model
Institute of Scientific and Technical Information of China (English)
周韶园; 谢磊; 王树青
2005-01-01
An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.
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.
Damage evaluation by a guided wave-hidden Markov model based method
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.
A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning
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.
Abdalla, Mahmoud I
2010-01-01
To improve the performance of speaker identification systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristic of the signal, the Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. Hidden Markov Models (HMMs) were used for the recognition stage as they give better recognition for the speaker's features than Dynamic Time Warping (DTW). Comparison of the proposed approach with the MFCCs conventional feature extraction method shows that the proposed method not only effectively reduces the influence of noise, but also improves recognition. A recognition rate of 99.3% was obtained using the proposed feature extraction technique compared to 98.7% using the MFCCs. When the test patterns were corrupted by additive white Gaussian noise with 20 d...
Hidden Markov model analysis of force/torque information in telemanipulation
Hannaford, Blake; Lee, Paul
1991-01-01
A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.
Hidden Markov models and neural networks for fault detection in dynamic systems
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.
Modeling carbachol-induced hippocampal network synchronization using hidden Markov models
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.
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
Hatt, M.; Lamare, F.; Boussion, N.; Turzo, A.; Collet, C.; Salzenstein, F.; Roux, C.; Jarritt, P.; Carson, K.; Cheze-LeRest, C.; Visvikis, D.
2007-07-01
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
International Nuclear Information System (INIS)
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both
Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.
Song, Sanming; Si, Bailu; Herrmann, J Michael; Feng, Xisheng
2016-05-01
A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption. It adapts, however, the parameters in a block-by-block style with a simple Hebbian learning rule. Experiments with given label fields show that the LAE is able to converge in far less time than required for a scan. It is also possible to derive an estimate for LAE based on a Cramer–Rao bound that is similar to the classical maximum pseudolikelihood method. As a general algorithm, LAE can be used to estimate the parameters in anisotropic label fields. Furthermore, LAE is not limited to the classical Potts model and can be applied to other types of Potts models by simple label field transformations and straightforward learning rule extensions. Experimental results on image segmentations demonstrate the efficiency and generality of the LAE algorithm. PMID:27019491
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection.
Montazeri Ghahjaverestan, N; Masoudi, S; Shamsollahi, M B; Beuchee, A; Pladys, P; Ge, D; Hernandez, A I
2016-03-01
In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and specificity of the classification are above 93.98% and 95.38% and those of the detection reach 94.49% and 99.34%, respectively. The method is also evaluated using a clinical database composed of annotated physiological signal recordings of neonates suffering from apnea-bradycardia. Different combinations of beat-to-beat features extracted from electrocardiographic signals constitute the multidimensional observations for which the proposed CHMM model is applied, to detect each apnea bradycardia episode. The proposed approach is finally compared to other previously proposed HMM-based detection methods. Our CHMM provides the best performance on this clinical database, presenting an average sensitivity of 95.74% and specificity of 91.88% while it reduces the detection delay by -0.59 s. PMID:25706937
A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.
Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing
2015-01-01
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing. PMID:26710073
Michalopoulos, Kostas; Zervakis, Michalis; Deiber, Marie-Pierre; Bourbakis, Nikolaos
2016-09-01
We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials. PMID:27255799
Detecting Gait Phases from RGB-D Images Based on Hidden Markov Model.
Heravi, Hamed; Ebrahimi, Afshin; Olyaee, Ehsan
2016-01-01
Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60-40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision. PMID:27563572
Detecting Gait Phases from RGB-D Images Based on Hidden Markov Model
Heravi, Hamed; Ebrahimi, Afshin; Olyaee, Ehsan
2016-01-01
Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60–40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision. PMID:27563572
Andriyas, S.; McKee, M.
2014-12-01
Anticipating farmers' irrigation decisions can provide the possibility of improving the efficiency of canal operations in on-demand irrigation systems. Although multiple factors are considered during irrigation decision making, for any given farmer there might be one factor playing a major role. Identification of that biophysical factor which led to a farmer deciding to irrigate is difficult because of high variability of those factors during the growing season. Analysis of the irrigation decisions of a group of farmers for a single crop can help to simplify the problem. We developed a hidden Markov model (HMM) to analyze irrigation decisions and explore the factor and level at which the majority of farmers decide to irrigate. The model requires observed variables as inputs and the hidden states. The chosen model inputs were relatively easily measured, or estimated, biophysical data, including such factors (i.e., those variables which are believed to affect irrigation decision-making) as cumulative evapotranspiration, soil moisture depletion, soil stress coefficient, and canal flows. Irrigation decision series were the hidden states for the model. The data for the work comes from the Canal B region of the Lower Sevier River Basin, near Delta, Utah. The main crops of the region are alfalfa, barley, and corn. A portion of the data was used to build and test the model capability to explore that factor and the level at which the farmer takes the decision to irrigate for future irrigation events. Both group and individual level behavior can be studied using HMMs. The study showed that the farmers cannot be classified into certain classes based on their irrigation decisions, but vary in their behavior from irrigation-to-irrigation across all years and crops. HMMs can be used to analyze what factor and, subsequently, what level of that factor on which the farmer most likely based the irrigation decision. The study shows that the HMM is a capable tool to study a process
International Nuclear Information System (INIS)
We present a hierarchical Bayesian method for estimating the density and size distribution of subclad-flaws in French Pressurized Water Reactor (PWR) vessels. This model takes into account in-service inspection (ISI) data, a flaw size-dependent probability of detection (different functions are considered) with a threshold of detection, and a flaw sizing error distribution (different distributions are considered). The resulting model is identified through a Markov Chain Monte Carlo (MCMC) algorithm. The article includes discussion for choosing the prior distribution parameters and an illustrative application is presented highlighting the model's ability to provide good parameter estimates even when a small number of flaws are observed
Bayesian Lorentzian profile fitting using Markov-Chain Monte Carlo: An observer's approach
Gruberbauer, M; Weiss, W W
2008-01-01
Aims. Investigating stochastically driven pulsation puts strong requirements on the quality of (observed) pulsation frequency spectra, such as the accuracy of frequencies, amplitudes, and mode life times and -- important when fitting these parameters with models -- a realistic error estimate which can be quite different to the formal error. As has been shown by other authors, the method of fitting Lorentzian profiles to the power spectrum of time-resolved photometric or spectroscopic data via the Maximum Likelihood Estimation (MLE) procedure delivers good approximations for these quantities. We, however, intend to demonstrate that a conservative Bayesian approach allows to treat this problem in a more consistent way. Methods. We derive a conservative Bayesian treatment for the probability of Lorentzian profiles being present in a power spectrum and describe its implementation via evaluating the probability density distribution of parameters by using the Markov-Chain Monte Carlo (MCMC) technique. In addition, ...
International Nuclear Information System (INIS)
Occurrence of hazardous accident in nuclear power plants and industrial units usually lead to release of radioactive materials and pollutants in environment. These materials and pollutants can be transported to a far downstream by the wind flow. In this paper, we implemented an atmospheric dispersion code to solve the inverse problem. Having received and detected the pollutants in one region, we may estimate the rate and location of the unknown source. For the modeling, one needs a model with ability of atmospheric dispersion calculation. Furthermore, it is required to implement a mathematical approach to infer the source location and the related rates. In this paper the AERMOD software and Bayesian inference along the Markov Chain Monte Carlo have been applied. Implementing, Bayesian approach and Markov Chain Monte Carlo for the aforementioned subject is not a new approach, but the AERMOD model coupled with the said methods is a new and well known regulatory software, and enhances the reliability of outcomes. To evaluate the method, an example is considered by defining pollutants concentration in a specific region and then obtaining the source location and intensity by a direct calculation. The result of the calculation estimates the average source location at a distance of 7km with an accuracy of 5m which is good enough to support the ability of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Liu Jun S
2004-10-01
Full Text Available Abstract Background Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences. Results Here we describe a hidden Markov model (HMM, an algebraic system, and Markov chain Monte Carlo (MCMC sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Giα subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97 AAA+ ATPases. Conclusion While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can
Identification of temporal patterns in the seismicity of Sumatra using Poisson Hidden Markov models
Directory of Open Access Journals (Sweden)
Katerina Orfanogiannaki
2014-05-01
Full Text Available On 26 December 2004 and 28 March 2005 two large earthquakes occurred between the Indo-Australian and the southeastern Eurasian plates with moment magnitudes Mw=9.1 and Mw=8.6, respectively. Complete data (mb≥4.2 of the post-1993 time interval have been used to apply Poisson Hidden Markov models (PHMMs for identifying temporal patterns in the time series of the two earthquake sequences. Each time series consists of earthquake counts, in given and constant time units, in the regions determined by the aftershock zones of the two mainshocks. In PHMMs each count is generated by one of m different Poisson processes that are called states. The series of states is unobserved and is in fact a Markov chain. The model incorporates a varying seismicity rate, it assigns a different rate to each state and it detects the changes on the rate over time. In PHMMs unobserved factors, related to the local properties of the region are considered affecting the earthquake occurrence rate. Estimation and interpretation of the unobserved sequence of states that underlie the data contribute to better understanding of the geophysical processes that take place in the region. We applied PHMMs to the time series of the two mainshocks and we estimated the unobserved sequences of states that underlie the data. The results obtained showed that the region of the 26 December 2004 earthquake was in state of low seismicity during almost the entire observation period. On the contrary, in the region of the 28 March 2005 earthquake the seismic activity is attributed to triggered seismicity, due to stress transfer from the region of the 2004 mainshock.
Application of MultiScale Hidden Markov Modeling Wavelet Coefficients to fMRI Activation Detection
Directory of Open Access Journals (Sweden)
Fangyuan Nan
2008-01-01
Full Text Available Problem Statement: The problem of detection of functional magnetic resonance images (fMRIs, that is, to decide active and nonactive regions of human brain from fMRIs is studied in this paper. fMRI research is finding and will find more and more applications in diagnosing and treating brain diseases like depression and schizophrenia. At its initial stage fMRI detection are pixel-wise methods, which do not take advantage of mutual information among neighboring pixels. Ignoring such spatial information can reduce detection accuracy. During past decade, many efforts have been focusing on taking advantage of spatial correlation inherent in fMRI data. Most well known is smoothing using a fixed Gaussian filter and the compensation for multiple testing using Gaussian random field theory as used by Statistical Parametric Mapping (SPM. Other methods including wavelets had also been proposed by the community. Approach: In this study a novel two-step approach was put forward that incorporates spatial correlation information and is amenable to analysis and optimization. First, a new multi scale image segmentation algorithm was proposed to decompose the correlation image into several different regions, each of which is of homogeneous statistical behavior. Second, each region will be classified independently as active or inactive using existing pixel-wise test methods. The image segmentation consists of two procedures: Edge detection followed by label estimation. To deduce the presence or absence of an edge from continuous data, two fundamental assumption of our algorithm are 1 each wavelet coefficient was described by a 2-state Gaussian Mixture Model (GMM; 2 across scale, each state is caused by its parent state, hence the Multiscale Hidden Markov Model (MHMM. The states of Markov chain are unknown ("hidden" and represent the presence (state 1 or absence (state 0 of edges. Using this interpretation, the edge detection problem boils down to the posterior state
Hidden Markov model analysis of maternal behavior patterns in inbred and reciprocal hybrid mice.
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Valeria Carola
Full Text Available Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM, to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to
Dennemont, Yannick; Bouyer, Guillaume; Otmane, Samir; Mallem, Malik
2012-01-01
This work studies, implements and evaluates a gestures recognition module based on discrete Hidden Markov Models. The module is implemented on Matlab and used from Virtools. It can be used with different inputs therefore serves different recognition purposes. We focus on the 3D positions, our devices common information, as inputs for gesture recognition. Experiments are realized with an infra-red tracked flystick. Finally, the recognition rate is more than 90% with a personalized learning bas...
A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2015-01-01
In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inheren...
International Nuclear Information System (INIS)
A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.
Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model
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Yerim Choi
2014-01-01
Full Text Available With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs, two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.
A transition-constrained discrete hidden Markov model for automatic sleep staging
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Pan Shing-Tai
2012-08-01
Full Text Available Abstract Background Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG recordings including electroencephalograms (EEGs, electrooculograms (EOGs and electromyograms (EMGs, are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. Method The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM, and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. Results Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%, and the least-accurately classified stage was S1 ( Conclusion The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.
Hamdi, Anis; Missaoui, Oualid; Frigui, Hichem; Gader, Paul
2010-04-01
We propose a landmine detection algorithm that uses ensemble discrete hidden Markov models with context dependent training schemes. We hypothesize that the data are generated by K models. These different models reflect the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Model identification is based on clustering in the log-likelihood space. First, one HMM is fit to each of the N individual sequence. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N x N log-likelihood distance matrix that will be partitioned into K groups. In the second step, we learn the parameters of one discrete HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we will investigate the maximum likelihood, and the MCE-based discriminative training approaches. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent HMM models that describe different properties of the data. Each HMM models a group of alarm signatures that share common attributes such as clutter, mine type, and burial depth. Our initial experiments have also indicated that the proposed mixture model outperform the baseline HMM that uses one model for the mine and one model for the background.
Capturing the state transitions of seizure-like events using Hidden Markov models.
Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L
2011-01-01
The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms. PMID:22254742
Automatic sleep staging based on ECG signals using hidden Markov models.
Ying Chen; Xin Zhu; Wenxi Chen
2015-08-01
This study is designed to investigate the feasibility of automatic sleep staging using features only derived from electrocardiography (ECG) signal. The study was carried out using the framework of hidden Markov models (HMMs). The mean, and SD values of heart rates (HRs) computed from each 30-second epoch served as the features. The two feature sequences were first detrended by ensemble empirical mode decomposition (EEMD), formed as a two-dimensional feature vector, and then converted into code vectors by vector quantization (VQ) method. The output VQ indexes were utilized to estimate parameters for HMMs. The proposed model was tested and evaluated on a group of healthy individuals using leave-one-out cross-validation. The automatic sleep staging results were compared with PSG estimated ones. Results showed accuracies of 82.2%, 76.0%, 76.1% and 85.5% for deep, light, REM and wake sleep, respectively. The findings proved that HRs-based HMM approach is feasible for automatic sleep staging and can pave a way for developing more efficient, robust, and simple sleep staging system suitable for home application. PMID:26736316
Song, Changyue; Liu, Kaibo; Zhang, Xi; Chen, Lili; Xian, Xiaochen
2016-07-01
Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications. PMID:26560867
Segmentation of heart sound recordings by a duration-dependent hidden Markov model
International Nuclear Information System (INIS)
Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of heart sound recordings into periods related to the first and second heart sound (S1 and S2) is fundamental in the analysis process. However, segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM identifies the most likely sequence of physiological heart sounds, based on duration of the events, the amplitude of the signal envelope and a predefined model structure. The DHMM model was developed and tested with heart sounds recorded bedside with a commercially available handheld stethoscope from a population of patients referred for coronary arterioangiography. The DHMM identified 890 S1 and S2 sounds out of 901 which corresponds to 98.8% (CI: 97.8–99.3%) sensitivity in 73 test patients and 13 misplaced sounds out of 903 identified sounds which corresponds to 98.6% (CI: 97.6–99.1%) positive predictivity. These results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds
Directory of Open Access Journals (Sweden)
Fei Chen
2015-04-01
Full Text Available Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker’s hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM method is adopted to recognize patterns via data streams and identify workers’ gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.
Effective identification of conserved pathways in biological networks using hidden Markov models.
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Xiaoning Qian
Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.
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.
Snoring detection using a piezo snoring sensor based on hidden Markov models
International Nuclear Information System (INIS)
This study presents a snoring detection method based on hidden Markov models (HMMs) using a piezo snoring sensor. Snoring is a major symptom of obstructive sleep apnea (OSA). In most sleep studies, snoring is detected with a microphone. Since these studies analyze the acoustic properties of snoring, they need to acquire data at high sampling rates, so a large amount of data should be processed. Recently, several sleep studies have monitored snoring using a piezo snoring sensor. However, an automatic method for snoring detection using a piezo snoring sensor has not been reported in the literature. This study proposed the HMM-based method to detect snoring using this sensor, which is attached to the neck. The data from 21 patients with OSA were gathered for training and test sets. The short-time Fourier transform and short-time energy were computed so they could be applied to HMMs. The data were classified as snoring, noise and silence according to their HMMs. As a result, the sensitivity and the positive predictivity values were 93.3% and 99.1% for snoring detection, respectively. The results demonstrated that the method produced simple, portable and user-friendly detection tools that provide an alternative to the microphone-based method. (note)
Suvorova, S; Melatos, A; Moran, W; Evans, R J
2016-01-01
Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g. superfluid or magnetospheric) or external (e.g. accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F-statistic to surmount some of the challenges raised by spin wandering. Specifically it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F-statistic output from coherent segments with duration T_drift = 10d over a total observation time of T_obs = 1yr can detect signals with wave strains h0 > 2e-26 at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital...
Jones, Jonathan-Lee; Essa, Ehab; Xie, Xianghua
2015-08-01
We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method. PMID:26736778
Luo, Yuxuan; Feng, Jianjiang; Xu, Miao; Zhou, Jie; Min, James K; Xiong, Guanglei
2015-08-01
Computed tomography angiography (CTA) allows for not only diagnosis of coronary artery disease (CAD) with high spatial resolution but also monitoring the remodeling of vessel walls in the progression of CAD. Alignment of coronary arteries in CTA images acquired at different times (with a 3-7 years interval) is required to visualize and analyze the geometric and structural changes quantitatively. Previous work in image registration primarily focused on large anatomical structures and leads to suboptimal results when applying to registration of coronary arteries. In this paper, we develop a novel method to directly align the straightened coronary arteries in the cylindrical coordinate system guided by the extracted centerlines. By using a Hidden Markov Model (HMM), image intensity information from CTA and geometric information of extracted coronary arteries are combined to align coronary arteries. After registration, the pathological features in two straightened coronary arteries can be directly visualized side by side by synchronizing the corresponding cross-sectional slices and circumferential rotation angles. By evaluating with manually labeled landmarks, the average distance error is 1.6 mm. PMID:26736676
A Hidden Markov Model for Localization Using Low-End GSM Cell Phones
Ibrahim, Mohamed
2010-01-01
Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localizat...
Reverse engineering a social agent-based hidden markov model--visage.
Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A
2008-12-01
We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution. PMID:19145665
Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant
2015-07-01
Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
van de Meent, Jan-Willem; Bronson, Jonathan E.; Wood, Frank; Gonzalez, Ruben L.; Wiggins, Chris H.
2016-01-01
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process. PMID:26985282
Activation Detection on fMRI Time Series Using Hidden Markov Model
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Rong Duan
2012-01-01
based on hidden Markov model (HMM. HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD. Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.
Detection and diagnosis of bearing and cutting tool faults using hidden Markov models
Boutros, Tony; Liang, Ming
2011-08-01
Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.
Extracting duration information in a picture category decoding task using hidden Markov Models
Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg
2016-04-01
Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.
Application of hidden Markov models to biological data mining: a case study
Yin, Michael M.; Wang, Jason T.
2000-04-01
In this paper we present an example of biological data mining: the detection of splicing junction acceptors in eukaryotic genes. Identification or prediction of transcribed sequences from within genomic DNA has been a major rate-limiting step in the pursuit of genes. Programs currently available are far from being powerful enough to elucidate the gene structure completely. Here we develop a hidden Markov model (HMM) to represent the degeneracy features of splicing junction acceptor sites in eukaryotic genes. The HMM system is fully trained using an expectation maximization (EM) algorithm and the system performance is evaluated using the 10-way cross- validation method. Experimental results show that our HMM system can correctly classify more than 94% of the candidate sequences (including true and false acceptor sites) into right categories. About 90% of the true acceptor sites and 96% of the false acceptor sites in the test data are classified correctly. These results are very promising considering that only the local information in DNA is used. The proposed model will be a very important component of an effective and accurate gene structure detection system currently being developed in our lab.
A Hidden Markov Model for avalanche forecasting on Chowkibal–Tangdhar road axis in Indian Himalayas
Indian Academy of Sciences (India)
Jagdish Chandra Joshi; Sunita Srivastava
2014-12-01
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal–Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992–2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
Temporal structure analysis of broadcast tennis video using hidden Markov models
Kijak, Ewa; Oisel, Lionel; Gros, Patrick
2003-01-01
This work aims at recovering the temporal structure of a broadcast tennis video from an analysis of the raw footage. Our method relies on a statistical model of the interleaving of shots, in order to group shots into predefined classes representing structural elements of a tennis video. This stochastic modeling is performed in the global framework of Hidden Markov Models (HMMs). The fundamental units are shots and transitions. In a first step, colors and motion attributes of segmented shots are used to map shots into 2 classes: game (view of the full tennis court) and not game (medium, close up views, and commercials). In a second step, a trained HMM is used to analyze the temporal interleaving of shots. This analysis results in the identification of more complex structures, such as first missed services, short rallies that could be aces or services, long rallies, breaks that are significant of the end of a game and replays that highlight interesting points. These higher-level unit structures can be used either to create summaries, or to allow non-linear browsing of the video.
Martinez-Murcia, Francisco J; Górriz, Juan M; Ramírez, Javier; Ortiz, Andres
2016-11-01
The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI). PMID:27354189
Identifying bubble collapse in a hydrothermal system using hidden Markov models
Dawson, P.B.; Benitez, M.C.; Lowenstern, J. B.; Chouet, B.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 (>15Hz) 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 ???0.35-0.45s. 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. copyright 2012 by the American Geophysical Union.
Karchin, Rachel; Cline, Melissa; Mandel-Gutfreund, Yael; Karplus, Kevin
2003-06-01
An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE. PMID:12784210
El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar
2014-11-01
Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data. PMID:26353069
Bayesian Inference for LISA Pathfinder using Markov Chain Monte Carlo Methods
Ferraioli, Luigi; Plagnol, Eric
2012-01-01
We present a parameter estimation procedure based on a Bayesian framework by applying a Markov Chain Monte Carlo algorithm to the calibration of the dynamical parameters of a space based gravitational wave detector. The method is based on the Metropolis-Hastings algorithm and a two-stage annealing treatment in order to ensure an effective exploration of the parameter space at the beginning of the chain. We compare two versions of the algorithm with an application to a LISA Pathfinder data analysis problem. The two algorithms share the same heating strategy but with one moving in coordinate directions using proposals from a multivariate Gaussian distribution, while the other uses the natural logarithm of some parameters and proposes jumps in the eigen-space of the Fisher Information matrix. The algorithm proposing jumps in the eigen-space of the Fisher Information matrix demonstrates a higher acceptance rate and a slightly better convergence towards the equilibrium parameter distributions in the application to...
Wind Farm Reliability Modelling Using Bayesian Networks and Semi-Markov Processes
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Robert Adam Sobolewski
2015-09-01
Full Text Available Technical reliability plays an important role among factors affecting the power output of a wind farm. The reliability is determined by an internal collection grid topology and reliability of its electrical components, e.g. generators, transformers, cables, switch breakers, protective relays, and busbars. A wind farm reliability’s quantitative measure can be the probability distribution of combinations of operating and failed states of the farm’s wind turbines. The operating state of a wind turbine is its ability to generate power and to transfer it to an external power grid, which means the availability of the wind turbine and other equipment necessary for the power transfer to the external grid. This measure can be used for quantitative analysis of the impact of various wind farm topologies and the reliability of individual farm components on the farm reliability, and for determining the expected farm output power with consideration of the reliability. This knowledge may be useful in an analysis of power generation reliability in power systems. The paper presents probabilistic models that quantify the wind farm reliability taking into account the above-mentioned technical factors. To formulate the reliability models Bayesian networks and semi-Markov processes were used. Using Bayesian networks the wind farm structural reliability was mapped, as well as quantitative characteristics describing equipment reliability. To determine the characteristics semi-Markov processes were used. The paper presents an example calculation of: (i probability distribution of the combination of both operating and failed states of four wind turbines included in the wind farm, and (ii expected wind farm output power with consideration of its reliability.
Evaluation of various feature extraction methods for landmine detection using hidden Markov models
Hamdi, Anis; Frigui, Hichem
2012-06-01
Hidden Markov Models (HMM) have proved to be eective for detecting buried land mines using data collected by a moving-vehicle-mounted ground penetrating radar (GPR). The general framework for a HMM-based landmine detector consists of building a HMM model for mine signatures and a HMM model for clutter signatures. A test alarm is assigned a condence proportional to the probability of that alarm being generated by the mine model and inversely proportional to its probability in the clutter model. The HMM models are built based on features extracted from GPR training signatures. These features are expected to capture the salient properties of the 3-dimensional alarms in a compact representation. The baseline HMM framework for landmine detection is based on gradient features. It models the time varying behavior of GPR signals, encoded using edge direction information, to compute the likelihood that a sequence of measurements is consistent with a buried landmine. In particular, the HMM mine models learns the hyperbolic shape associated with the signature of a buried mine by three states that correspond to the succession of an increasing edge, a at edge, and a decreasing edge. Recently, for the same application, other features have been used with dierent classiers. In particular, the Edge Histogram Descriptor (EHD) has been used within a K-nearest neighbor classier. Another descriptor is based on Gabor features and has been used within a discrete HMM classier. A third feature, that is closely related to the EHD, is the Bar histogram feature. This feature has been used within a Neural Networks classier for handwritten word recognition. In this paper, we propose an evaluation of the HMM based landmine detection framework with several feature extraction techniques. We adapt and evaluate the EHD, Gabor, Bar, and baseline gradient feature extraction methods. We compare the performance of these features using a large and diverse GPR data collection.
Suvorova, S.; Sun, L.; Melatos, A.; Moran, W.; Evans, R. J.
2016-06-01
Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g., superfluid or magnetospheric) or external (e.g., accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F -statistic to surmount some of the challenges raised by spin wandering. Specifically, it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F -statistic output from coherent segments with duration Tdrift=10 d over a total observation time of Tobs=1 yr can detect signals with wave strains h0>2 ×10-26 at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital period and semimajor axis are known approximately from electromagnetic observations, HMM tracking of the Bessel-weighted F -statistic output can detect signals with h0>8 ×10-26. An efficient, recursive, HMM solver based on the Viterbi algorithm is demonstrated, which requires ˜103 CPU hours for a typical, broadband (0.5-kHz) search for the low-mass x-ray binary Scorpius X-1, including generation of the relevant F -statistic input. In a "realistic" observational scenario, Viterbi tracking successfully detects 41 out of 50 synthetic signals without spin wandering in stage I of the Scorpius X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration down to a wave strain of h0=1.1 ×10-25, recovering the frequency with a root-mean-square accuracy of ≤4.3 ×10-3 Hz .
Modeling Strategic Use of Human Computer Interfaces with Novel Hidden Markov Models
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Laura Jane Mariano
2015-07-01
Full Text Available 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
Witowski, Vitali; Foraita, Ronja; Pitsiladis, Yannis; Pigeot, Iris; Wirsik, Norman
2014-01-01
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data. PMID:25464514
Preparation of name and address data for record linkage using hidden Markov models
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Lim Kim
2002-12-01
Full Text Available Abstract Background Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs. Methods HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems. Results Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed. Conclusion Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve.
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models
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Borodovsky Mark
2006-03-01
Full Text Available Abstract Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results In this paper, we further refine and extend the hidden semi-Markov model (HSMM initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. Conclusions We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable
Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models
Dammeier, Franziska; Moore, Jeffrey R.; Hammer, Conny; Haslinger, Florian; Loew, Simon
2016-02-01
Data from continuously recording permanent seismic networks can contain information about rockslide occurrence and timing complementary to eyewitness observations and thus aid in construction of robust event catalogs. However, detecting infrequent rockslide signals within large volumes of continuous seismic waveform data remains challenging and often requires demanding manual intervention. We adapted an automatic classification method using hidden Markov models to detect rockslide signals in seismic data from two stations in central Switzerland. We first processed 21 known rockslides, with event volumes spanning 3 orders of magnitude and station event distances varying by 1 order of magnitude, which resulted in 13 and 19 successfully classified events at the two stations. Retraining the models to incorporate seismic noise from the day of the event improved the respective results to 16 and 19 successful classifications. The missed events generally had low signal-to-noise ratio and small to medium volumes. We then processed nearly 14 years of continuous seismic data from the same two stations to detect previously unknown events. After postprocessing, we classified 30 new events as rockslides, of which we could verify three through independent observation. In particular, the largest new event, with estimated volume of 500,000 m3, was not generally known within the Swiss landslide community, highlighting the importance of regional seismic data analysis even in densely populated mountainous regions. Our method can be easily implemented as part of existing earthquake monitoring systems, and with an average event detection rate of about two per month, manual verification would not significantly increase operational workload.
A novel seizure detection algorithm informed by hidden Markov model event states
Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian
2016-06-01
Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h‑1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.
Yoo, Jiyoung; Kwon, Hyun-Han; So, Byung-Jin; Rajagopalan, Balaji; Kim, Tae-Woong
2015-04-01
This study proposed a hidden Markov chain model-based drought analysis (HMM-DA) tool to understand the beginning and ending of meteorological drought and to further characterize typhoon-induced drought busters (TDB) by exploring spatiotemporal drought patterns in South Korea. It was found that typhoons have played a dominant role in ending drought events (EDE) during the typhoon season (July-September) over the last four decades (1974-2013). The percentage of EDEs terminated by TDBs was about 43-90% mainly along coastal regions in South Korea. Furthermore, the TDBs, mainly during summer, have a positive role in managing extreme droughts during the subsequent autumn and spring seasons. The HMM-DA models the temporal dependencies between drought states using Markov chain, consequently capturing the dependencies between droughts and typhoons well, thus, enabling a better performance in modeling spatiotemporal drought attributes compared to traditional methods.
Chen, Shiyang; Langley, Jason; Chen, Xiangchuan; Hu, Xiaoping
2016-05-01
Analyzing functional magnetic resonance imaging (fMRI) time courses with dynamic approaches has generated a great deal of interest because of the additional temporal features that can be extracted. In this work, to systemically model spatiotemporal patterns of the brain, a Gaussian hidden Markov model (GHMM) was adopted to model the brain state switching process. We assumed that the brain switches among a number of different brain states as a Markov process and used multivariate Gaussian distributions to represent the spontaneous activity patterns of brain states. This model was applied to resting-state fMRI data from 100 subjects in the Human Connectome Project and detected nine highly reproducible brain states and their temporal and transition characteristics. Our results indicate that the GHMM can unveil brain dynamics that may provide additional insights regarding the brain at resting state. PMID:27008543
Institute of Scientific and Technical Information of China (English)
DONG Ming
2008-01-01
As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac-tice in industry is effective diagnostics and prognostics. Recently, a pattern recog-nition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip-ment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1)It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations' independence assumption by accom-modating a link between consecutive observations. 3) It does not follow the unre-alistic Markov chain's memoryless assumption and therefore provides more pow-erful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forwardbackward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision-making in equipment health management.
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Roberto Carrillo Aguilar
2007-04-01
Full Text Available Este trabajo da a conocer el sistema de desarrollo de software para el diseño y manipulación de modelos ocultos de Markov, denominado HTK. Actualmente, la técnica de modelos ocultos de Markov es la herramienta más efectiva para implementar sistemas reconocedores del habla. HTK está orientado principalmente a ese aspecto. Su arquitectura es robusta y autosuficiente. Permite: la entrada lógica y natural desde un micrófono, dispone de módulos para la conversión A/D, preprocesado y parametrización de la información, posee herramientas para definir y manipular modelos ocultos de Markov, tiene librerías para entrenamiento y manipulación de los modelos ocultos de Markov ya definidos, considera funciones para definir la gramática, y además: Una serie de herramientas adicionales permiten lograr el objetivo final de obtener una hipotética transcripción del habla (conversión voz - texto.This paper presents HTK, a software development platform for the design and management of Hidden Markov Models. Nowadays, the Hidden Markov Models technique is the more effective one to implement voice recognition systems. HTK is mainly oriented to this application. Its architecture is robust and self-sufficient. It allows a natural input from a microphone, it has modules for A/D conversion, it allows pre-processing and parameterization of information, it possesses tools to define and manage the Hidden Markov Models, libraries for training and use the already defined Hidden Markov Models. It has functions to define the grammar and it has additional tools to reach the final objective, to obtain an hypothetical transcription of the talking (voice to text translation.
A Survey on Hidden Markov Model (HMM Based Intention Prediction Techniques
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Mrs. Manisha Bharati
2016-01-01
Full Text Available The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. This paper discusses two approaches In the first three features namely ongoing attacks, autonomic prevention actions, and risk measure are Integrated to our Autonomic Cloud Intrusion Detection Framework (ACIDF as most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud’s overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system. In another Attack Sequence Detection (ASD approach as Tasks from different users may be performed on the same machine. Therefore, one primary security concern is whether user data is secure in cloud. On the other hand, hacker may facilitate cloud computing to launch larger range of attack, such as a request of port scan in cloud with multiple virtual machines executing such malicious action. In addition, hacker may perform a sequence of attacks in order to compromise his target system in cloud, for example, evading an easy-to-exploit machine in a
Directory of Open Access Journals (Sweden)
Vitali Witowski
Full Text Available INTRODUCTION: The use of accelerometers to objectively measure physical activity (PA has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. METHODS: 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois], the generalized Poisson distribution (HMM[GenPois] and the Gaussian distribution (HMM[Gauss] with regard to misclassification rate (MCR, bout detection, detection of the number of activities performed during the day and runtime. RESULTS: The cutpoint method had a misclassification rate (MCR of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint, 2.0 minutes (HMM[Gauss] and 14.2 minutes (HMM[GenPois]. CONCLUSIONS: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Bayesian inference along Markov Chain Monte Carlo approach for PWR core loading pattern optimization
International Nuclear Information System (INIS)
Highlights: ► The BIMCMC method performs very well and is comparable to GA and PSO techniques. ► The potential of the technique is very well for optimization. ► It is observed that the performance of the method is quite adequate. ► The BIMCMC is very easy to implement. -- Abstract: Despite remarkable progress in optimization procedures, inherent complexities in nuclear reactor structure and strong interdependence among the fundamental indices namely, economic, neutronic, thermo-hydraulic and environmental effects make it necessary to evaluate the most efficient arrangement of a reactor core. In this paper a reactor core reloading technique based on Bayesian inference along Markov Chain Monte Carlo, BIMCMC, is addressed in the context of obtaining an optimal configuration of fuel assemblies in reactor cores. The Markov Chain Monte Carlo with Metropolis–Hastings algorithm has been applied for sampling variable and its acceptance. The proposed algorithm can be used for in-core fuel management optimization problems in pressurized water reactors. Considerable work has been expended for loading pattern optimization, but no preferred approach has yet emerged. To evaluate the proposed technique, increasing the effective multiplication factor Keff of a WWER-1000 core along flattening power with keeping power peaking factor below a specific limit as a first test case and flattening of power as a second test case are considered as objective functions; although other variables such as burn up and cycle length can also be taken into account. The results, convergence rate and reliability of the new method are compared to published data resulting from particle swarm optimization and genetic algorithm; the outcome is quite promising and demonstrating the potential of the technique very well for optimization applications in the nuclear engineering field.
Czech Academy of Sciences Publication Activity Database
Valečková, Markéta; Kárný, Miroslav; Sutanto, E. L.
2001-01-01
Roč. 37, č. 6 (2001), s. 1071-1078. ISSN 0005-1098 R&D Projects: GA ČR GA102/99/1564 Grant ostatní: IST(XE) 1999/12058 Institutional research plan: AV0Z1075907 Keywords : Markov chain * clustering * Bayesian mixture estimation Subject RIV: BC - Control Systems Theory Impact factor: 1.449, year: 2001
Murakami, Yohei; Takada, Shoji
2013-01-01
When exact values of model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC) is a useful method. Biological experiments are often performed with cell population, and the results are represented by histograms. On another front, experiments sometimes indicate the existence of a specific bifurcation patt...
Time-coherency of Bayesian priors on transient semi-Markov chains for audio-to-score alignment
Cuvillier, Philippe
2014-01-01
This paper proposes a novel insight to the problem of real-time alignment with Bayesian inference. When a prior knowledge about the duration of events is available, Semi-Markov models allow the setting of individual duration distributions but give no clue about their choice. We propose a criterion of temporal coherency for such applications and show it might be obtained with the right choice of estimation method. Theoretical insights are obtained through the study of the prior state probabili...
Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina
2015-01-01
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies. PMID:26427023
Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina
2015-01-01
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies. PMID:26427023
Bayesian regularisation methods in a hybrid MLP-HMM system.
Renals, Steve; MacKay, David
1993-01-01
We have applied Bayesian regularisation methods to multi-layer percepuon (MLP) training in the context of a hybrid MLP-HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the training data. Experiments have been carried out on the ARPA Resource Management database.
Entropy Computation in Partially Observed Markov Chains
Desbouvries, François
2006-11-01
Let X = {Xn}n∈N be a hidden process and Y = {Yn}n∈N be an observed process. We assume that (X,Y) is a (pairwise) Markov Chain (PMC). PMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient parameter estimation and Bayesian restoration algorithms. In this paper we propose a fast (i.e., O(N)) algorithm for computing the entropy of {Xn}n=0N given an observation sequence {yn}n=0N.
Guenterberg, Eric; Yang, Allen Y; Ghasemzadeh, Hassan; Jafari, Roozbeh; Bajcsy, Ruzena; Sastry, S Shankar
2009-11-01
Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes. PMID:19726268
Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.
2009-01-01
The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.
Institute of Scientific and Technical Information of China (English)
LIU; Jianfeng; ZHANG; Yuan; ZHANG; Qin; WANG; Lixian; ZHANG; Jigang
2006-01-01
It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution and less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL and polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm and reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well and robust under different family structures and QTL effects. As family size increases and the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.
Benoit, Julia S; Chan, Wenyaw; Luo, Sheng; Yeh, Hung-Wen; Doody, Rachelle
2016-04-30
Understanding the dynamic disease process is vital in early detection, diagnosis, and measuring progression. Continuous-time Markov chain (CTMC) methods have been used to estimate state-change intensities but challenges arise when stages are potentially misclassified. We present an analytical likelihood approach where the hidden state is modeled as a three-state CTMC model allowing for some observed states to be possibly misclassified. Covariate effects of the hidden process and misclassification probabilities of the hidden state are estimated without information from a 'gold standard' as comparison. Parameter estimates are obtained using a modified expectation-maximization (EM) algorithm, and identifiability of CTMC estimation is addressed. Simulation studies and an application studying Alzheimer's disease caregiver stress-levels are presented. The method was highly sensitive to detecting true misclassification and did not falsely identify error in the absence of misclassification. In conclusion, we have developed a robust longitudinal method for analyzing categorical outcome data when classification of disease severity stage is uncertain and the purpose is to study the process' transition behavior without a gold standard. PMID:26782946
Directory of Open Access Journals (Sweden)
Carlos Alejandro De Luna Ortega
2006-01-01
Full Text Available En este artículo se aborda el diseño de un reconocedor de voz, con el idioma español mexicano, del estado de Aguascalientes, de palabras aisladas, con dependencia del hablante y vocabulario pequeño, empleando Redes Neuronales Artificiales (ANN por sus siglas en inglés, Alineamiento Dinámico del Tiempo (DTW por sus siglas en inglés y Modelos Ocultos de Markov (HMM por sus siglas en inglés para la realización del algoritmo de reconocimiento.
Indian Academy of Sciences (India)
G Hemantha Kumar; M Ravishankar; P Nagabushan; Basavaraj S Anami
2006-06-01
Pitman shorthand language (PSL) is a widely practised medium for transcribing/recording speech to text (StT) in English. This recording medium continues to exist in spite of considerable development in speech processing systems (SPS), because of its ability to record spoken/dictated text at high speeds of more than 120 words per minute. Hence, scope exists for exploiting this potential of PSL in present SPS. In this paper, an approach for feature extraction using Mel frequency cepstral coefﬁcients (MFCC) and classiﬁcation using hidden Markov models (HMM) for generating strokes comprising consonants and vowels (CV) in the process of production of Pitman shorthand language from spoken English is proposed. The proposed method is tested on a large number of samples, drawn from different speakers and the results are encouraging. The work is useful in total automation of PSL processing.
Karaman, Svebor; Dovgalecs, Vladislavs; Mégret, Rémi; Pinquier, Julien; André-Obrecht, Régine; Gaëstel, Yann; Dartigues, Jean-François
2011-01-01
This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach.
DEFF Research Database (Denmark)
Mailund, Thomas; Dutheil, Julien; Hobolth, Asger;
2011-01-01
ue to genetic variation in the ancestor of two populations or two species, the divergence time for DNA sequences from two populations is variable along the genome. Within genomic segments all bases will share the same divergence—because they share a most recent common ancestor—when no recombination...... event has occurred to split them apart. The size of these segments of constant divergence depends on the recombination rate, but also on the speciation time, the effective population size of the ancestral population, as well as demographic effects and selection. Thus, inference of these parameters may...... be possible if we can decode the divergence times along a genomic alignment. Here, we present a new hidden Markov model that infers the changing divergence (coalescence) times along the genome alignment using a coalescent framework, in order to estimate the speciation time, the recombination rate...
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.
Camproux, A C; Tufféry, P
2005-08-01
Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence. PMID:16040198
Kim, Jongin; Lee, Suh-Kyung; Lee, Boreom
2013-01-01
The purpose of this paper is to determine whether electroencephalograpy (EEG) can be used as a tool for hearing impairment tests such as hearing screening. For this study, we recorded EEG responses to two syllables, /a/ and /u/, in Korean from three subjects at Gwangju Institute of Science and Technology. The ultimate goal of this study is to classify speech sound data regardless of their size using EEG; however, as an initial stage of the study, we classified only two different speech syllables using Gaussian hidden markov model. The result of this study shows a possibility that EEG could be used for hearing screening and other diagnostic tools related to speech perception. PMID:24110681
International Nuclear Information System (INIS)
A new method for the experimental determination of stopping powers based on Bayesian Inference with the Markov chain Monte Carlo (MCMC) algorithm has been devised. This method avoids the difficulties related to thin target preparation. By measuring the RBS spectra for a known material, and using the known underlying physics, the stopping powers are determined by best matching the simulated spectra with the experimental spectra. Using silicon, SiO2 and Al2O3 as test cases, good agreement is obtained between calculated and experimental data. (author)
A F Pimentel, Marco; Santos, Mauro D; Springer, David B; Clifford, Gari D
2015-08-01
Accurate heart beat detection in signals acquired from intensive care unit (ICU) patients is necessary for establishing both normality and detecting abnormal events. Detection is normally performed by analysing the electrocardiogram (ECG) signal, and alarms are triggered when parameters derived from this signal exceed preset or variable thresholds. However, due to noisy and missing data, these alarms are frequently deemed to be false positives, and therefore ignored by clinical staff. The fusion of features derived from other signals, such as the arterial blood pressure (ABP) or the photoplethysmogram (PPG), has the potential to reduce such false alarms. In order to leverage the highly correlated temporal nature of the physiological signals, a hidden semi-Markov model (HSMM) approach, which uses the intra- and inter-beat depolarization interval, was designed to detect heart beats in such data. Features based on the wavelet transform, signal gradient and signal quality indices were extracted from the ECG and ABP waveforms for use in the HSMM framework. The presented method achieved an overall score of 89.13% on the hidden/test data set provided by the Physionet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Multimodal Data. PMID:26218536
Detecting substeps in the rotary motors of FoF1-ATP synthase by Hidden Markov Models
Zarrabi, N; Dueser, M G; Dunn, S D; Reuter, R; Wrachtrup, J
2007-01-01
FoF1-ATP synthase is the enzyme that provides the 'chemical energy currency' adenosine triphosphate, ATP, for living cells. The formation of ATP is accomplished by a stepwise internal rotation of subunits within the enzyme. We monitor subunit rotation by a single-molecule fluorescence resonance energy transfer (FRET) approach using two fluorophores specifically attached to the enzyme. To identify the stepsize of rotary movements by the motors of ATP synthase we simulated the confocal single-molecule FRET data of freely diffusing enzymes and developed a step finder algorithm based on 'Hidden Markov Models' (HMM). The HMM is able to find the proximity factors, P, for a three-level system and for a five-level system, and to unravel the dwell times of the simulated rotary movements. To identify the number of hidden states in the system, a likelihood parameter is calculated for the series of one-state to eight-state HMMs applied to each set of simulated data. Thereby, the basic prerequisites for the experimental s...
Directory of Open Access Journals (Sweden)
Rondeau Paul
2008-01-01
Full Text Available Speech coding techniques capable of generating encoded representations which are robust against channel losses play an important role in enabling reliable voice communication over packet networks and mobile wireless systems. In this paper, we investigate the use of multiple description index assignments (MDIAs for loss-tolerant transmission of line spectral frequency (LSF coefficients, typically generated by state-of-the-art speech coders. We propose a simulated annealing-based approach for optimizing MDIAs for Markov-model-based decoders which exploit inter- and intraframe correlations in LSF coefficients to reconstruct the quantized LSFs from coded bit streams corrupted by channel losses. Experimental results are presented which compare the performance of a number of novel LSF transmission schemes. These results clearly demonstrate that Markov-model-based decoders, when used in conjunction with optimized MDIA, can yield average spectral distortion much lower than that produced by methods such as interleaving/interpolation, commonly used to combat the packet losses.
Strong law of large numbers for countable nonhomogeneous hidden Markov models%可列非齐次隐Markov模型的强大数定律
Institute of Scientific and Technical Information of China (English)
杨国庆; 杨卫国
2014-01-01
隐马尔科夫模型被广泛的应用于弱相依随机变量的建模，是研究神经生理学、发音过程和生物遗传等问题的有力工具。研究了可列非齐次隐 Markov 模型的若干性质，得到了这类模型的强大数定律，推广了有限非齐次马氏链的一类强大数定律。%Hidden Markov models have been widely used for modeling sequences of weakly dependent random variables, with application in areas such as speech processing, neurophysiology and biology. In this paper, we study some properties of countable nonhomogeneous hidden Markov models, get the strong law of large numbers for those Markov models and extend a class of the strong law of large numbers for finite nonhomogeneous Markov chains.
Directory of Open Access Journals (Sweden)
Giulia Carreras
2012-09-01
Full Text Available
Background: parameter uncertainty in the Markov model’s description of a disease course was addressed. Probabilistic sensitivity analysis (PSA is now considered the only tool that properly permits parameter uncertainty’s examination. This consists in sampling values from the parameter’s probability distributions.
Methods: Markov models fitted with microsimulation were considered and methods for carrying out a PSA on transition probabilities were studied. Two Bayesian solutions were developed: for each row of the modeled transition matrix the prior distribution was assumed as a product of Beta or a Dirichlet. The two solutions differ in the source of information: several different sources for each transition in the Beta approach and a single source for each transition from a given health state in the Dirichlet. The two methods were applied to a simple cervical cancer’s model.
Results : differences between posterior estimates from the two methods were negligible. Results showed that the prior variability highly influence the posterior distribution.
Conclusions: the novelty of this work is the Bayesian approach that integrates the two distributions with a product of Binomial distributions likelihood. Such methods could be also applied to cohort data and their application to more complex models could be useful and unique in the cervical cancer context, as well as in other disease modeling.
In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models
F. Javier Ordoñez; G. Englebienne; P. de Toledo; T. van Kasteren; A. Sanchez; B. Kröse
2014-01-01
Activity recognition in a home setting is being widely explored as a means to support elderly people living alone. Probabilistic models using classical, maximum-likelihood estimation methods are known to work well in this domain, but they are prone to overfitting and require labeled activity data fo
Hammond, Alan; Pete, Gábor
2011-01-01
Consider a Markov process \\omega_t at equilibrium and some event C (a subset of the state-space of the process). A natural measure of correlations in the process is the pairwise correlation \\Pr[\\omega_0,\\omega_t \\in C] - \\Pr[\\omega_0 \\in C]^2. A second natural measure is the probability of the continual occurrence event \\{\\omega_s \\in C, \\forall s\\in [0,t]\\}. We show that for reversible Markov chains, and any event C, pairwise decorrelation of the event C implies a decay of the probability of the continual occurrence event \\{\\omega_s \\in C, \\forall s \\in [0,t]\\} as t\\to\\infty. We provide examples showing that our results are often sharp. Our main applications are to dynamical critical percolation. Let C be the left-right crossing event of a large box, and let us scale time so that the expected number of changes to C is order 1 in unit time. We show that the continual connection event has superpolynomial decay. Furthermore, on the infinite lattice without any time scaling, the first exceptional time with an in...
Evaluation model for e-tourism product: A hidden Markov model-based algorithm
Liu, Chang; Ouzrout, Yacine; Nongaillard, Antoine; Bouras, Abdelaziz; Zhou, Jiliu
2014-01-01
Nowadays, e-tourism is widely pronounced as a kind of web marketing for tourism businesses. Tourist agencies and tourism service providers are able to access their customers directly in a cost-effective way. In this context, due to the high uncertainty of business using the internet, reputation systems play a key role in e-tourism business. After a literature review, and an analysis of the limits of the existing models, we propose a new reputation evaluation model based on optimisation hidden...
Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions
DEFF Research Database (Denmark)
Tataru, Paula; Sand, Andreas; Hobolth, Asger;
2013-01-01
data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of...... three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from...
Directory of Open Access Journals (Sweden)
Ojcius David M
2009-08-01
Full Text Available Abstract Background Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort. Results Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability. Conclusion This strategy and resulting model support the conjecture that DNA biophysical properties
Luk, B. L.; Liu, K. P.; Tong, F.; Man, K. F.
2010-05-01
The impact-acoustics method utilizes different information contained in the acoustic signals generated by tapping a structure with a small metal object. It offers a convenient and cost-efficient way to inspect the tile-wall bonding integrity. However, the existence of the surface irregularities will cause abnormal multiple bounces in the practical inspection implementations. The spectral characteristics from those bounces can easily be confused with the signals obtained from different bonding qualities. As a result, it will deteriorate the classic feature-based classification methods based on frequency domain. Another crucial difficulty posed by the implementation is the additive noise existing in the practical environments that may also cause feature mismatch and false judgment. In order to solve this problem, the work described in this paper aims to develop a robust inspection method that applies model-based strategy, and utilizes the wavelet domain features with hidden Markov modeling. It derives a bonding integrity recognition approach with enhanced immunity to surface roughness as well as the environmental noise. With the help of the specially designed artificial sample slabs, experiments have been carried out with impact acoustic signals contaminated by real environmental noises acquired under practical inspection background. The results are compared with those using classic method to demonstrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
S Prabha
2011-07-01
Full Text Available Resisting distributed denial of service (DDoS attacks become more challenging with the availability of resources and techniques to attackers. The application-layer-based DDoS attacks utilize legitimate HTTP requests to overwhelm victim resources are more undetectable and are protocol compliant and non-intrusive. Focusing on the detection for application layer DDoS attacks, the existing scheme provide an access matrix which capture the spatial-temporal patterns of a normal flash crowd on non stationary object. The access matrix captures the spatial-temporal patterns of the normal flash crowd and the anomaly detector based on hidden Markov model (HMM described the dynamics of Access Matrix (AM to detect the application DDoS attacks. However current application layer attacks have high influence on the stationary object as well. In addition the detection threshold for non stationary object should be reevaluated to improve the performance of false positive rate and detection rate of the DDoS attacks.
Directory of Open Access Journals (Sweden)
Qinming Liu
2012-01-01
Full Text Available Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM with sequential Monte Carlo (SMC methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.
Parida, Bikram K; Panda, Prasanna K; Misra, Namrata; Mishra, Barada K
2015-02-01
Modeling the three-dimensional (3D) structures of proteins assumes great significance because of its manifold applications in biomolecular research. Toward this goal, we present MaxMod, a graphical user interface (GUI) of the MODELLER program that combines profile hidden Markov model (profile HMM) method with Clustal Omega program to significantly improve the selection of homologous templates and target-template alignment for construction of accurate 3D protein models. MaxMod distinguishes itself from other existing GUIs of MODELLER software by implementing effortless modeling of proteins using templates that bear modified residues. Additionally, it provides various features such as loop optimization, express modeling (a feature where protein model can be generated directly from its sequence, without any further user intervention) and automatic update of PDB database, thus enhancing the user-friendly control of computational tasks. We find that HMM-based MaxMod performs better than other modeling packages in terms of execution time and model quality. MaxMod is freely available as a downloadable standalone tool for academic and non-commercial purpose at http://www.immt.res.in/maxmod/. PMID:25636267
Directory of Open Access Journals (Sweden)
Juri Taborri
2015-09-01
Full Text Available Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6, with the best performance for the distributed classifier in two-phase recognition (G = 0.02. Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population.
Bracken, C. W.; Rajagopalan, B.; Zagona, E. A.
2011-12-01
Upper Colorado River Basin annual flow exhibits very low autocorrelation but regime shifting behavior causing long departures from the historical average flow producing sustained wet and dry periods. Traditional stochastic time series models do not capture this feature thereby misleading the water resources system risk and consequently impacting the management and planning efforts. To address this, we developed a nonstationary Hidden Markov (HM) model with Gamma component distributions, as opposed to Normal distributions which is widely used in literature, for stochastic simulation and short term forecasting. Global decoding from this model reveals and captures strong underlying persistent structure in the Lees Ferry flow time series. In addition to capturing the shifting mean, simulations from this model have a 20% greater chance than a first order Auto Regressive model (AR1), the best time series model for this data, of simulating wet and dry runs of 6 or more years. Relative to AR1 the HM model also captures the spectral features quite well. When applied to short term forecasting (i.e. of 1-2 years) they show higher skill relative to climatology but also to an AR1 model.
Directory of Open Access Journals (Sweden)
Paul Gader
2005-07-01
Full Text Available We propose a real-time software system for landmine detection using ground-penetrating radar (GPR. The system includes an efficient and adaptive preprocessing component; a hidden Markov model- (HMM- based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline. It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m2 of simulated dirt and gravel roads for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%.
Khademi, Mahmoud; Kiapour, Mohammad H; Kiaei, Ali A
2010-01-01
Facial Action Coding System consists of 44 action units (AUs) and more than 7000 combinations. Hidden Markov models (HMMs) classifier has been used successfully to recognize facial action units (AUs) and expressions due to its ability to deal with AU dynamics. However, a separate HMM is necessary for each single AU and each AU combination. Since combinations of AU numbering in thousands, a more efficient method will be needed. In this paper an accurate real-time sequence-based system for representation and recognition of facial AUs is presented. Our system has the following characteristics: 1) employing a mixture of HMMs and neural network, we develop a novel accurate classifier, which can deal with AU dynamics, recognize subtle changes, and it is also robust to intensity variations, 2) although we use an HMM for each single AU only, by employing a neural network we can recognize each single and combination AU, and 3) using both geometric and appearance-based features, and applying efficient dimension reducti...
Zarrabi, Nawid; Ernst, Stefan; Verhalen, Brandy; Wilkens, Stephan; Börsch, Michael
2014-03-15
Single-molecule Förster resonance energy (smFRET) transfer has become a powerful tool for observing conformational dynamics of biological macromolecules. Analyzing smFRET time trajectories allows to identify the state transitions occuring on reaction pathways of molecular machines. Previously, we have developed a smFRET approach to monitor movements of the two nucleotide binding domains (NBDs) of P-glycoprotein (Pgp) during ATP hydrolysis driven drug transport in solution. One limitation of this initial work was that single-molecule photon bursts were analyzed by visual inspection with manual assignment of individual FRET levels. Here a fully automated analysis of Pgp smFRET data using hidden Markov models (HMM) for transitions up to 9 conformational states is applied. We propose new estimators for HMMs to integrate the information of fluctuating intensities in confocal smFRET measurements of freely diffusing lipid bilayer bound membrane proteins in solution. HMM analysis strongly supports that under conditions of steady state turnover, conformational states with short NBD distances and short dwell times are more populated compared to conditions without nucleotide or transport substrate present. PMID:23891547
Directory of Open Access Journals (Sweden)
Emilija Kisić
2015-01-01
Full Text Available An innovative approach to condition-based maintenance of coal grinding subsystems at thermoelectric power plants is proposed in the paper. Coal mill grinding tables become worn over time and need to be replaced through time-based maintenance, after a certain number of service hours. At times such replacement is necessary earlier or later than prescribed, depending on the quality of the coal and of the grinding table itself. Considerable financial losses are incurred when the entire coal grinding subsystem is shut down and the grinding table found to not actually require replacement. The only way to determine whether replacement is necessary is to shut down and open the entire subsystem for visual inspection. The proposed algorithm supports condition-based maintenance and involves the application of T2 control charts to distinct acoustic signal parameters in the frequency domain and the construction of Hidden Markov Models whose observations are coded samples from the control charts. In the present research, the acoustic signals were collected by coal mill monitoring at the thermoelectric power plant “Kostolac” in Serbia. The proposed approach provides information about the current condition of the grinding table.
Gómez-Losada, Álvaro; Pires, José Carlos M.; Pino-Mejías, Rafael
2016-02-01
Urban area air pollution results from local air pollutants (from different sources) and horizontal transport (background pollution). Understanding urban air pollution background (lowest) concentration profiles is key in population exposure assessment and epidemiological studies. To this end, air pollution registered at background monitoring sites is studied, but background pollution levels are given as the average of the air pollutant concentrations measured at these sites over long periods of time. This short communication shows how a metric based on Hidden Markov Models (HMMs) can characterise the air pollutant background concentration profiles. HMMs were applied to daily average concentrations of CO, NO2, PM10 and SO2 at thirteen urban monitoring sites from three cities from 2010 to 2013. Using the proposed metric, the mean values of background and ambient air pollution registered at these sites for these primary pollutants were estimated and the ratio of ambient to background air pollution and the difference between them were studied. The ratio indicator for the studied air pollutants during the four-year study sets the background air pollution at 48%-69% of the ambient air pollution, while the difference between these values ranges from 101 to 193 μg/m3, 7-12 μg/m3, 11-13 μg/m3 and 2-3 μg/m3 for CO, NO2, PM10 and SO2, respectively.
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Guoxian Yu
2013-04-01
Full Text Available Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI, fractal dimension, and Accumulated Growing Degree Days (AGDDs. In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs of the National Agricultural Statistics Service (NASS. Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops.
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Yuan Yuan
2015-11-01
Full Text Available In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.
A hidden Markov model to assess drug-induced sleep fragmentation in the telemetered rat.
Diack, C; Ackaert, O; Ploeger, B A; van der Graaf, P H; Gurrell, R; Ivarsson, M; Fairman, D
2011-12-01
Drug-induced sleep fragmentation can cause sleep disturbances either via their intended pharmacological action or as a side effect. Examples of disturbances include excessive daytime sleepiness, insomnia and nightmares. Developing drugs without these side effects requires insight into the mechanisms leading to sleep disturbance. The characterization of the circadian sleep pattern by EEG following drug exposure has improved our understanding of these mechanisms and their translatability across species. The EEG shows frequent transitions between specific sleep states leading to multiple correlated sojourns in these states. We have developed a Markov model to consider the high correlation in the data and quantitatively compared sleep disturbance in telemetered rats induced by methylphenidate, which is known to disturb sleep, and of a new chemical entity (NCE). It was assumed that these drugs could either accelerate or decelerate the transitions between the sleep states. The difference in sleep disturbance of methylphenidate and the NCE were quantitated and different mechanisms of action on rebound sleep were identified. The estimated effect showed that both compounds induce sleep fragmentation with methylphenidate being fivefold more potent compared to the NCE. PMID:21909798
Tutorial on Exact Belief Propagation in Bayesian Networks: from Messages to Algorithms
Nuel, G
2012-01-01
In Bayesian networks, exact belief propagation is achieved through message passing algorithms. These algorithms (ex: inward and outward) provide only a recursive definition of the corresponding messages. In contrast, when working on hidden Markov models and variants, one classically first defines explicitly these messages (forward and backward quantities), and then derive all results and algorithms. In this paper, we generalize the hidden Markov model approach by introducing an explicit definition of the messages in Bayesian networks, from which we derive all the relevant properties and results including the recursive algorithms that allow to compute these messages. Two didactic examples (the precipitation hidden Markov model and the pedigree Bayesian network) are considered along the paper to illustrate the new formalism and standalone R source code is provided in the appendix.
关于树指标隐Markov链及其等价定义∗%Equivalent Definitions of T-indexed Hidden Markov Chains
Institute of Scientific and Technical Information of China (English)
王豹; 杨卫国
2015-01-01
本文参照直线上隐Markov模型的概念，给出有限树指标隐Markov链的定义。在该定义中，树指标隐Markov链由两个树指标随机过程组成，其中第一个树指标随机过程是树指标Markov链，是不能被直接观测到的隐藏链；第二个树指标随机过程是可被观测的且关于第一个树指标随机过程条件独立，对于树上的任意一个顶点，第二个随机过程此处的取值只信赖于隐藏链中此处的取值。最后，我们给出了树指标隐Markov链的三个等价定义。%In this paper, we give the definition of tree indexed hidden Markov chain with finite state space based on the concept of hidden Markov model. In our definition, tree indexed hidden Markov chain consists of two tree indexed random processes. The underlying process is a tree indexed Markov chain and can not be observed, and the second process is conditional independent of the former. For the arbitrary vertex in tree, the second process only dependents on the underlying process. Finally, we propose three equivalent definitions.
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
Weinberg, Martin D
2009-01-01
Computation of the marginal likelihood or "Bayesian Evidence" from a simulated posterior distribution is central to Bayesian model selection but is fraught with difficulty. The often-used harmonic mean approximation uses the posterior directly but is unstably sensitive to samples with anomalously small values of the likelihood and converges very slowly. The Laplace approximation is stable but makes strong, and often inappropriate, assumptions about the shape of the posterior distribution. It is useful, but not general. We need an algorithm that is general and easy to apply, like the harmonic mean approximation, but robust to sample size and multimodality. Here, I argue that the evidence can be stably computed from a posterior sample by careful attention to the numerics of the probability integral. Posing the expression for the Bayesian evidence as a Lebesgue integral, we may convert the evaluation of the sample statistic to a quadrature rule and show that the harmonic mean approximation suffers from enormous ...
Discrete Quantum Markov Chains
Faigle, Ulrich
2010-01-01
A framework for finite-dimensional quantum Markov chains on Hilbert spaces is introduced. Quantum Markov chains generalize both classical Markov chains with possibly hidden states and existing models of quantum walks on finite graphs. Quantum Markov chains are based on Markov operations that may be applied to quantum systems and include quantum measurements, for example. It is proved that quantum Markov chains are asymptotically stationary and hence possess ergodic and entropic properties. With a quantum Markov chain one may associate a quantum Markov process, which is a stochastic process in the classical sense. Generalized Markov chains allow a representation with respect to a generalized Markov source model with definite (but possibly hidden) states relative to which observables give rise to classical stochastic processes. It is demonstrated that this model allows for observables to violate Bell's inequality.
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Gerstein Mark
2004-01-01
Full Text Available Abstract Background Hidden Markov Models (HMMs have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction. Thus far, however, they have been confined to representing 1D sequence (or the aspects of structure that could be represented by character strings. Results We develop an HMM formalism that explicitly uses 3D coordinates in its match states. The match states are modeled by 3D Gaussian distributions centered on the mean coordinate position of each alpha carbon in a large structural alignment. The transition probabilities depend on the spread of the neighboring match states and on the number of gaps found in the structural alignment. We also develop methods for aligning query structures against 3D HMMs and scoring the result probabilistically. For 1D HMMs these tasks are accomplished by the Viterbi and forward algorithms. However, these will not work in unmodified form for the 3D problem, due to non-local quality of structural alignment, so we develop extensions of these algorithms for the 3D case. Several applications of 3D HMMs for protein structure classification are reported. A good separation of scores for different fold families suggests that the described construct is quite useful for protein structure analysis. Conclusion We have created a rigorous 3D HMM representation for protein structures and implemented a complete set of routines for building 3D HMMs in C and Perl. The code is freely available from http://www.molmovdb.org/geometry/3dHMM, and at this site we also have a simple prototype server to demonstrate the features of the described approach.
Energy Technology Data Exchange (ETDEWEB)
Hogden, J.
1996-11-05
The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation may decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.
Huda, Shamsul; Yearwood, John; Togneri, Roberto
2014-10-01
The expectation maximization (EM) is the standard training algorithm for hidden Markov model (HMM). However, EM faces a local convergence problem in HMM estimation. This paper attempts to overcome this problem of EM and proposes hybrid metaheuristic approaches to EM for HMM. In our earlier research, a hybrid of a constraint-based evolutionary learning approach to EM (CEL-EM) improved HMM estimation. In this paper, we propose a hybrid simulated annealing stochastic version of EM (SASEM) that combines simulated annealing (SA) with EM. The novelty of our approach is that we develop a mathematical reformulation of HMM estimation by introducing a stochastic step between the EM steps and combine SA with EM to provide better control over the acceptance of stochastic and EM steps for better HMM estimation. We also extend our earlier work and propose a second hybrid which is a combination of an EA and the proposed SASEM, (EA-SASEM). The proposed EA-SASEM uses the best constraint-based EA strategies from CEL-EM and stochastic reformulation of HMM. The complementary properties of EA and SA and stochastic reformulation of HMM of SASEM provide EA-SASEM with sufficient potential to find better estimation for HMM. To the best of our knowledge, this type of hybridization and mathematical reformulation have not been explored in the context of EM and HMM training. The proposed approaches have been evaluated through comprehensive experiments to justify their effectiveness in signal modeling using the speech corpus: TIMIT. Experimental results show that proposed approaches obtain higher recognition accuracies than the EM algorithm and CEL-EM as well. PMID:24686310
Dwyer, Michael G; Bergsland, Niels; Zivadinov, Robert
2014-04-15
SIENA and similar techniques have demonstrated the utility of performing "direct" measurements as opposed to post-hoc comparison of cross-sectional data for the measurement of whole brain (WB) atrophy over time. However, gray matter (GM) and white matter (WM) atrophy are now widely recognized as important components of neurological disease progression, and are being actively evaluated as secondary endpoints in clinical trials. Direct measures of GM/WM change with advantages similar to SIENA have been lacking. We created a robust and easily-implemented method for direct longitudinal analysis of GM/WM atrophy, SIENAX multi-time-point (SIENAX-MTP). We built on the basic halfway-registration and mask composition components of SIENA to improve the raw output of FMRIB's FAST tissue segmentation tool. In addition, we created LFAST, a modified version of FAST incorporating a 4th dimension in its hidden Markov random field model in order to directly represent time. The method was validated by scan-rescan, simulation, comparison with SIENA, and two clinical effect size comparisons. All validation approaches demonstrated improved longitudinal precision with the proposed SIENAX-MTP method compared to SIENAX. For GM, simulation showed better correlation with experimental volume changes (r=0.992 vs. 0.941), scan-rescan showed lower standard deviations (3.8% vs. 8.4%), correlation with SIENA was more robust (r=0.70 vs. 0.53), and effect sizes were improved by up to 68%. Statistical power estimates indicated a potential drop of 55% in the number of subjects required to detect the same treatment effect with SIENAX-MTP vs. SIENAX. The proposed direct GM/WM method significantly improves on the standard SIENAX technique by trading a small amount of bias for a large reduction in variance, and may provide more precise data and additional statistical power in longitudinal studies. PMID:24333394
Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model.
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
Reves-Sohn, R.; Humphris, S.; Canales, J.
2005-12-01
The TAG hydrothermal mound is a dynamic structure that is continuously growing via mineral deposition, collapsing from gravitational instabilities and anhydrite dissolution, and shaking from frequent seismic activity on the adjacent normal faults. As a result, the sub-surface fluid circulation patterns beneath the mound are continually re-organizing in response to events that close and open flow paths. These characteristics are clearly evident in time series exit-fluid temperature data acquired from June 2003 through July 2004 as part of the Seismicity and Fluid Flow of TAG (STAG) experiment. Twenty one temperature probes were deployed in actively venting cracks across the TAG mound, and temperature measurements were acquired at each site every ~10 minutes. A key insight for understanding the exit-fluid temperature data is that the measurements can be modeled as Markov chains, where each measurement is a random variable drawn from a finite set of probability distributions associated with the hidden states of the system (i.e., Hidden Markov Models). The Markov chain changes states in response to events that can affect multiple probes, but not necessarily in the same way. For example, an event may cause temperatures at one probe to rapidly increase while temperatures at another probe rapidly decrease. The data from many probes can be explained with a two-state Markov chain, with one state corresponding to "crack open" and the second state corresponding to "crack closed", but still other probes require three or more states, possibly in a nested structure. These stochastic models are deepening our understanding of shallow circulation patterns beneath the TAG mound, and we hope to use them to condition subsurface flow models incorporating the relevant physics of permeable flow in fractures and heat flow.
Waldmann, Patrik; Hallander, Jon; Hoti, Fabian; Sillanpää, Mikko J
2008-06-01
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general pedigrees without inbreeding. To optimize between computational time and good mixing of the Markov chain Monte Carlo (MCMC) chains, we used a hybrid Gibbs sampler that combines a single site and a blocked Gibbs sampler. The speed of the hybrid sampler and the mixing of the single-site sampler were further improved by the use of pretransformed variables. Two traits (height and trunk diameter) from a previously published diallel progeny test of Scots pine (Pinus sylvestris L.) and two large simulated data sets with different levels of dominance variance were analyzed. We also performed Bayesian model comparison on the basis of the posterior predictive loss approach. Results showed that models with both additive and dominance components had the best fit for both height and diameter and for the simulated data with high dominance. For the simulated data with low dominance, we needed an informative prior to avoid the dominance variance component becoming overestimated. The narrow-sense heritability estimates in the Scots pine data were lower compared to the earlier results, which is not surprising because the level of dominance variance was rather high, especially for diameter. In general, the hybrid sampler was considerably faster than the blocked sampler and displayed better mixing properties than the single-site sampler. PMID:18558655
Waldmann, Patrik; Hallander, Jon; Hoti, Fabian; Sillanpää, Mikko J.
2008-01-01
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general pedigrees without inbreeding. To optimize between computational time and good mixing of the Markov chain Monte Carlo (MCMC) chains, we used a hybrid Gibbs sampler that combines a single site and a blocked Gibbs sampler. The speed of the hybrid sampler and the mixing of the single-site sampler were further improved by the use of pretransformed variables. Two traits (height and trunk diameter) from a previously published diallel progeny test of Scots pine (Pinus sylvestris L.) and two large simulated data sets with different levels of dominance variance were analyzed. We also performed Bayesian model comparison on the basis of the posterior predictive loss approach. Results showed that models with both additive and dominance components had the best fit for both height and diameter and for the simulated data with high dominance. For the simulated data with low dominance, we needed an informative prior to avoid the dominance variance component becoming overestimated. The narrow-sense heritability estimates in the Scots pine data were lower compared to the earlier results, which is not surprising because the level of dominance variance was rather high, especially for diameter. In general, the hybrid sampler was considerably faster than the blocked sampler and displayed better mixing properties than the single-site sampler. PMID:18558655
Directory of Open Access Journals (Sweden)
Persson Bengt
2010-10-01
Full Text Available Abstract Background The Medium-chain Dehydrogenases/Reductases (MDR form a protein superfamily whose size and complexity defeats traditional means of subclassification; it currently has over 15000 members in the databases, the pairwise sequence identity is typically around 25%, there are members from all kingdoms of life, the chain-lengths vary as does the oligomericity, and the members are partaking in a multitude of biological processes. There are profile hidden Markov models (HMMs available for detecting MDR superfamily members, but none for determining which MDR family each protein belongs to. The current torrential influx of new sequence data enables elucidation of more and more protein families, and at an increasingly fine granularity. However, gathering good quality training data usually requires manual attention by experts and has therefore been the rate limiting step for expanding the number of available models. Results We have developed an automated algorithm for HMM refinement that produces stable and reliable models for protein families. This algorithm uses relationships found in data to generate confident seed sets. Using this algorithm we have produced HMMs for 86 distinct MDR families and 34 of their subfamilies which can be used in automated annotation of new sequences. We find that MDR forms with 2 Zn2+ ions in general are dehydrogenases, while MDR forms with no Zn2+ in general are reductases. Furthermore, in Bacteria MDRs without Zn2+ are more frequent than those with Zn2+, while the opposite is true for eukaryotic MDRs, indicating that Zn2+ has been recruited into the MDR superfamily after the initial life kingdom separations. We have also developed a web site http://mdr-enzymes.org that provides textual and numeric search against various characterised MDR family properties, as well as sequence scan functions for reliable classification of novel MDR sequences. Conclusions Our method of refinement can be readily applied to
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Michael Seifert
Full Text Available Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual
Efficient Bayesian estimation of Markov model transition matrices with given stationary distribution
Trendelkamp-Schroer, Benjamin
2013-01-01
Direct simulation of biomolecular dynamics in thermal equilibrium is challenging due to the metastable nature of conformation dynamics and the computational cost of molecular dynamics. Biased or enhanced sampling methods may improve the convergence of expectation values of equilibrium probabilities and expectation values of stationary quantities significantly. Unfortunately the convergence of dynamic observables such as correlation functions or timescales of conformational transitions relies on direct equilibrium simulations. Markov state models are well suited to describe both, stationary properties and properties of slow dynamical processes of a molecular system, in terms of a transition matrix for a jump process on a suitable discretiza- tion of continuous conformation space. Here, we introduce statistical estimation methods that allow a priori knowledge of equilibrium probabilities to be incorporated into the estimation of dynamical observables. Both, maximum likelihood methods and an improved Monte Carlo...
Minsley, B.J.
2011-01-01
A meaningful interpretation of geophysical measurements requires an assessment of the space of models that are consistent with the data, rather than just a single, 'best' model which does not convey information about parameter uncertainty. For this purpose, a trans-dimensional Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed for assessing frequency-domain electromagnetic (FDEM) data acquired from airborne or ground-based systems. By sampling the distribution of models that are consistent with measured data and any prior knowledge, valuable inferences can be made about parameter values such as the likely depth to an interface, the distribution of possible resistivity values as a function of depth and non-unique relationships between parameters. The trans-dimensional aspect of the algorithm allows the number of layers to be a free parameter that is controlled by the data, where models with fewer layers are inherently favoured, which provides a natural measure of parsimony and a significant degree of flexibility in parametrization. The MCMC algorithm is used with synthetic examples to illustrate how the distribution of acceptable models is affected by the choice of prior information, the system geometry and configuration and the uncertainty in the measured system elevation. An airborne FDEM data set that was acquired for the purpose of hydrogeological characterization is also studied. The results compare favourably with traditional least-squares analysis, borehole resistivity and lithology logs from the site, and also provide new information about parameter uncertainty necessary for model assessment. ?? 2011. Geophysical Journal International ?? 2011 RAS.
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Allen Rodrigo
2006-01-01
Full Text Available Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low.
Efficient Bayesian estimation of Markov model transition matrices with given stationary distribution
Trendelkamp-Schroer, Benjamin; Noé, Frank
2013-04-01
Direct simulation of biomolecular dynamics in thermal equilibrium is challenging due to the metastable nature of conformation dynamics and the computational cost of molecular dynamics. Biased or enhanced sampling methods may improve the convergence of expectation values of equilibrium probabilities and expectation values of stationary quantities significantly. Unfortunately the convergence of dynamic observables such as correlation functions or timescales of conformational transitions relies on direct equilibrium simulations. Markov state models are well suited to describe both stationary properties and properties of slow dynamical processes of a molecular system, in terms of a transition matrix for a jump process on a suitable discretization of continuous conformation space. Here, we introduce statistical estimation methods that allow a priori knowledge of equilibrium probabilities to be incorporated into the estimation of dynamical observables. Both maximum likelihood methods and an improved Monte Carlo sampling method for reversible transition matrices with fixed stationary distribution are given. The sampling approach is applied to a toy example as well as to simulations of the MR121-GSGS-W peptide, and is demonstrated to converge much more rapidly than a previous approach of Noé [J. Chem. Phys. 128, 244103 (2008), 10.1063/1.2916718].
Estimation in autoregressive models with Markov regime
Ríos, Ricardo; Rodríguez, Luis
2005-01-01
In this paper we derive the consistency of the penalized likelihood method for the number state of the hidden Markov chain in autoregressive models with Markov regimen. Using a SAEM type algorithm to estimate the models parameters. We test the null hypothesis of hidden Markov Model against an autoregressive process with Markov regime.
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Márcio das Chagas Moura
2008-08-01
Full Text Available In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.Neste trabalho, é proposto um modelo baseado na integração entre processos semi-Markovianos e redes Bayesianas para avaliação da disponibilidade de sistemas tolerantes à falha. Esta integração resulta em um modelo estocástico híbrido o qual é capaz de representar as características dinâmicas de um sistema assim como tratar as relações de causa e efeito entre fatores externos tais como condições ambientais e operacionais. Além disso, o modelo híbrido permite avaliar a propagação de incerteza sobre a disponibilidade do sistema. É também proposto um procedimento numérico para a solução das equações de probabilidade de estado de processos semi-Markovianos descritos por taxas de transição. Tal procedimento numérico é baseado na aplicação de transformadas de Laplace que são invertidas pelo método de quadratura Gaussiana conhecido como Gauss Legendre. O modelo híbrido e procedimento numérico são ilustrados por meio de um exemplo de aplicação no contexto de
Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Cemgil, Ali Taylan;
2010-01-01
a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment...
Lamiable, A; Thevenet, P; Tufféry, P
2016-08-01
Hidden Markov Model derived structural alphabets are a probabilistic framework in which the complete conformational space of a peptidic chain is described in terms of probability distributions that can be sampled to identify conformations of largest probabilities. Here, we assess how three strategies to sample sub-optimal conformations-Viterbi k-best, forward backtrack and a taboo sampling approach-can lead to the efficient generation of peptide conformations. We show that the diversity of sampling is essential to compensate biases introduced in the estimates of the probabilities, and we find that only the forward backtrack and a taboo sampling strategies can efficiently generate native or near-native models. Finally, we also find such approaches are as efficient as former protocols, while being one order of magnitude faster, opening the door to the large scale de novo modeling of peptides and mini-proteins. © 2016 Wiley Periodicals, Inc. PMID:27317417
A Hidden Genetic Layer Based Neural Network for Mobility Prediction
L. Velmurugan; P. Thangaraj
2012-01-01
Problem statement: With numerous wireless devices increasingly connecting to the internet, WLAN infrastructure planning becomes very important to maintain desired quality of services. For maintaining desired quality of service it is desirable to know the movement pattern of users. Mobility prediction involves finding the mobile device's next access point as it moves through the wireless network. Hidden Markov models and Bayesian approach have been proposed to predict the next hop. Approach: I...
International Nuclear Information System (INIS)
Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion. Accessing the full range of Bayesian inference methods requires being able to draw samples from the posterior probability distribution in a fast and efficient way. This is usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this paper, we develop and examine a new implementation of a single component Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that the efficiency of our Gibbs sampler increases when the level of sparsity or the dimension of the unknowns is increased. This property is contrary to the properties of the most commonly applied Metropolis–Hastings (MH) sampling schemes. We demonstrate that the efficiency of MH schemes for L1-type priors dramatically decreases when the level of sparsity or the dimension of the unknowns is increased. Practically, Bayesian inversion for L1-type priors using MH samplers is not feasible at all. As this is commonly believed to be an intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also challenges common beliefs about the applicability of sample based Bayesian inference. (paper)
Das, Raibatak; Cairo, Christopher W.; Coombs, Daniel
2009-01-01
The extraction of hidden information from complex trajectories is a continuing problem in single-particle and single-molecule experiments. Particle trajectories are the result of multiple phenomena, and new methods for revealing changes in molecular processes are needed. We have developed a practical technique that is capable of identifying multiple states of diffusion within experimental trajectories. We model single particle tracks for a membrane-associated protein interacting with a homoge...
Institute of Scientific and Technical Information of China (English)
林文龙; 刘业政; 朱庆生; 奚冬芹
2009-01-01
针对传统的Markov链模型不能有效的表征长串访问序列所蕴含的丰富的用户行为特征(用户类别特征、访问兴趣迁移特征)的缺点,提出混合隐Markov链浏览模型.混合隐Markov链模型使用多个不同的模型来区分不同类别用户的浏览特征,并为每个类别的用户设置了能跟踪捕捉其访问兴趣变化的类隐Markov链模型,能更好地对WWW长串访问序列的复杂特征进行建模,在真实WWW站点访问日志数据上的用户聚类实验与个性化推荐实验的结果表明,混合隐Markov链模型与传统的Markov链模型相比,具有更理想的聚类性能和推荐性能.%Since the Markov Chain Model can not denote the abundant users' behavioral characteristics(such as: characteristics of users' type, characteristics of users' interests transfer ) of a long access sequence effectively, the Mixtures of Hidden Markov Chain Models is proposed. Mixtures of Hidden Markov Chain Models use different models to distinguish the browsing categories of users from different types, and set a Hidden Markov Chain Models (can track and catch the changes of users' interests) for each users' type. Mixtures of Hidden Markov Chain Models can model the complex characteristics of the WWW long access sequences better. The results of users clustering experiment and personalized recommendation experiment with a real WWW web access log data show that Mixtures of Hidden Markov Chain Models have more perfect clustering and recommendation performance than Markov Chain Model.
基于层叠隐马模型的屏蔽关键词研究%Research on Shielded Keywords Based on Cascaded Hidden Markov Model
Institute of Scientific and Technical Information of China (English)
陶非凡
2014-01-01
信息时代给人们的生活带来巨大改善，但同时也伴随一系列问题的产生，其中如何对网络中产生的大数据量的言论信息进行过滤的问题是研究的一大难点。传统的屏蔽法效率较低而且不够准确，因此文中提出了一种新的关键词屏蔽技术。主要采用二元语法模型结合层叠隐马可夫分词技术，首先运用二元语法模型在大量语料中得到普通词和关键词的构成概率，建立一个有普通词和关键词分类的词典，再结合层叠隐马可夫模型对具体句子进行分词处理，对分词后的结果计算其关键词屏蔽概率，最终得到一个科学的屏蔽概率，可以大大提高关键词屏蔽的准确性。%The information age brings a huge improvement in people's lives,but also accompanied by a series of problems arising,in which how to filter a large amount of information the network's remarks generated is a major difficulty. The traditional method of shiel-ding has low efficiency and is not accurate enough,so propose a new keyword shielding technology. Mainly use binary syntax model combined with layered hidden Markov model segmentation techniques,first utilize binary syntax model to get the constitute probability of the common words and keywords in a large corpus,creating a dictionary of common words and keywords classified,then combined casca-ding hidden Markov model for the specific sentence word processing,calculate the probability of its keywords shield for segmented result, finally get a scientific shielding probability,which can greatly improve the accuracy of keyword shield.
Gelman, Andrew; Stern, Hal S; Dunson, David B; Vehtari, Aki; Rubin, Donald B
2013-01-01
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear
基于主题隐马尔科夫模型的人体异常行为识别%Human Abnormal Behavior Recognition Based on Topic Hidden Markov Model
Institute of Scientific and Technical Information of China (English)
朱旭东; 刘志镜
2012-01-01
This paper aimed to address the problem of modeling human behavior patterns captured in surveillance videos for the application of online normal behavior recognition and anomaly detection. From the perspective of cognitive psychology,a novel method was developed for automatic behavior modeling and online anomaly detection without the need for manual labeling of the training data set The work has been done with the hierarchical structure,following the routine of "Video Representation-Semantic Behavior (Topic) Model-Behavior Classification": 1) A compact and effective behavior representation method is developed based on spatial-temporal interest point detection. 2) The natural grouping of behavior patterns is determined through a novel clustering algorithm, topic hidden Markov model (THMM) built upon the existing hidden Markov model (HMM) and latent Dirichlet allocation (LDA), which overcomes the current limitations in accuracy, robustness,and computational efficiency. The new model is a four-level hierarchical Bayesian model, in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern is a distribution over some segments of the video. Each of these segments in the video can be modeled as a mixture of actions where each action is a distribution over spatial-temporal words. 3) An online anomaly measure is introduced to detect abnormal behavior, whereas normal behavior is recognized by runtime accumulative visual evidence using likelihood ratio test (LRT) method. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.%针对基于监控视频的人体异常行为识别问题,提出了基于主题隐马尔科夫模型的人体异常行为识别方法,即通过无任何人工标注的视频训练集自动学习人体行为模型,并能够应用学到的人体行为模型实时检测异常行为和识别正常行为.这一方法主
隐马尔可夫模型及其在自动词类标注中的应用%Hidden Markov model and its application in automatic POS tagging
Institute of Scientific and Technical Information of China (English)
冯志伟
2013-01-01
The mathematical research of A. A. Markov to"Eugene Onegin"is introduced in this paper, which shows that the language usage process is a stochastic process. Markov chain and Hidden Markov Model (HMM) are mathematically described by a weather example, then how to apply HMM to the automatic POS tagging in natural language processing is explained.%介绍了马尔可夫对《欧根·奥涅金》的数学研究，说明了语言的使用是一个随机过程，通过天气事件的实例对马尔可夫链和隐马尔可夫模型进行了数学描述，最后应用隐马尔可夫模型来解决自然语言处理中的自动词类标注问题。
Power, Sarah D.; Falk, Tiago H.; Chau, Tom
2010-04-01
Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% ± 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.
Real-time Risk Management Based on Hidden Markov Model%基于隐马尔科夫模型下的实时风险管理
Institute of Scientific and Technical Information of China (English)
何丽
2014-01-01
在目前实时风险管理整体现状的研究基础上，该文独立设计出了一种实时风险管理的框架。理论分析表明，与传统做法相比较，改进后的实时风险评估子模块不仅节省存储空间而且在与其它子模块进行交互时更具有实时性，得到的风险值也更加合理。%In this article, a real-time risk management framework based on the hidden Markov model is proposed independently after studies on the current status of real-time risk management as a whole. Theoretical analysis shows that compared with tradi-tional practices, improved real-time risk assessment sub-module not only saves storage space but also makes the real-time better while interacting with other sub-modules and the risk values are more reasonable.
Directory of Open Access Journals (Sweden)
Sofia Siachalou
2015-03-01
Full Text Available Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece.
Dean, Ben
2013-01-01
The use of miniature data loggers is rapidly increasing our understanding of the movements and habitat preferences of pelagic seabirds. However, objectively interpreting behavioural information from the large volumes of highly detailed data collected by such devices can be challenging. We combined three biologging technologies—global positioning system (GPS), saltwater immersion and time–depth recorders—to build a detailed picture of the at-sea behaviour of the Manx shearwater (Puffinus puffinus) during the breeding season. We used a hidden Markov model to explore discrete states within the combined GPS and immersion data, and found that behaviour could be organized into three principal activities representing (i) sustained direct flight, (ii) sitting on the sea surface, and (iii) foraging, comprising tortuous flight interspersed with periods of immersion. The additional logger data verified that the foraging activity corresponded well to the occurrence of diving. Applying this approach to a large tracking dataset revealed that birds from two different colonies foraged in local waters that were exclusive, but overlapped in one key area: the Irish Sea Front (ISF). We show that the allocation of time to each activity differed between colonies, with birds breeding furthest from the ISF spending the greatest proportion of time engaged in direct flight and the smallest proportion of time engaged in foraging activity. This type of analysis has considerable potential for application in future biologging studies and in other taxa. PMID:23034356
DEFF Research Database (Denmark)
Scholer, Marie; Irving, James; Zibar, Majken Caroline Looms;
2012-01-01
-chain-Monte-Carlo inversion approach with different priors. The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the......We examined to what extent time-lapse crosshole ground-penetrating radar traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify vadose zone hydraulic properties and their corresponding uncertainties using a Bayesian Markov......-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in...
Directory of Open Access Journals (Sweden)
Kevin McNally
2012-01-01
Full Text Available There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure.
Olivares, G.; Teferle, F. N.
2013-12-01
Geodetic time series provide information which helps to constrain theoretical models of geophysical processes. It is well established that such time series, for example from GPS, superconducting gravity or mean sea level (MSL), contain time-correlated noise which is usually assumed to be a combination of a long-term stochastic process (characterized by a power-law spectrum) and random noise. Therefore, when fitting a model to geodetic time series it is essential to also estimate the stochastic parameters beside the deterministic ones. Often the stochastic parameters include the power amplitudes of both time-correlated and random noise, as well as, the spectral index of the power-law process. To date, the most widely used method for obtaining these parameter estimates is based on maximum likelihood estimation (MLE). We present an integration method, the Bayesian Monte Carlo Markov Chain (MCMC) method, which, by using Markov chains, provides a sample of the posteriori distribution of all parameters and, thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. This algorithm automatically optimizes the Markov chain step size and estimates the convergence state by spectral analysis of the chain. We assess the MCMC method through comparison with MLE, using the recently released GPS position time series from JPL and apply it also to the MSL time series from the Revised Local Reference data base of the PSMSL. Although the parameter estimates for both methods are fairly equivalent, they suggest that the MCMC method has some advantages over MLE, for example, without further computations it provides the spectral index uncertainty, is computationally stable and detects multimodality.
Directory of Open Access Journals (Sweden)
In-Ho Choi
2016-05-01
Full Text Available This study presents a new method to track driver’s facial states, such as head pose and eye-blinking in the real-time basis. Since a driver in the natural driving condition moves his head in diverse ways and his face is often occluded by his hand or the wheel, it should be a great challenge for the standard face models. Among many, Active Appearance Model (AAM, and Active Shape Model (ASM are two favored face models. We have extended Discriminative Bayesian ASM by incorporating the extreme pose cases, called it Pose Extended—Active Shape model (PE-ASM. Two face databases (DB are used for the comparison purpose: one is the Boston University face DB and the other is our custom-made driving DB. Our evaluation indicates that PE-ASM outperforms ASM and AAM in terms of the face fitting against extreme poses. Using this model, we can estimate the driver’s head pose, as well as eye-blinking, by adding respective processes. Two HMMs are trained to model temporal behaviors of these two facial features, and consequently the system can make inference by enumerating these HMM states whether the driver is drowsy or not. Result suggests that it can be used as a driver drowsiness detector in the commercial car where the visual conditions are very diverse and often tough to deal with.
Adaptive Dynamic Bayesian Networks
Energy Technology Data Exchange (ETDEWEB)
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Evidence Fusion of the Network Forensics on the Hidden Markov Models%网络取证隐马尔可夫模型证据融合方法
Institute of Scientific and Technical Information of China (English)
杨珺; 马秦生; 王敏; 曹阳
2013-01-01
针对网络取证因果关联证据融合方法存在的算法复杂、重现场景不够精确等问题,提出了基于隐马尔科夫模型的网络取证证据融合方法,阐述了应用隐马尔科夫模型进行证据融合的可行性.该方法以元证据序列作为随机观察序列,以网络入侵步骤作为随机状态序列,通过对元证据序列进行解码操作,找寻最可能的网络入侵步骤并据此回溯证据链.实验结果表明,与基于贝叶斯网络的多源证据融合方法相比,该方法的算法复杂度和抵御干扰项的能力均得到了明显的改善,该方法能够以较小的代价较精确地重现网络入侵的犯罪现场.%To improve the algorithm complexity and the accuracy of reproduced scene,a new method for the evidence fusion of the network forensics on the hidden Markov models (HMM) is proposed.The feasibility of this method is expounded.By taking the sequence of the meta-evidenee as the random observation sequence,and the network intrusion step as the random state sequence,the most likely network intrusion step is inferred by the decoding operation aimed at the sequence of the meta-evidenee and the chain of the evidence is backtracked accordingly.When they are applied in the same problem,the algorithm complexity and the anti-interference ability of the proposed method are dramatically modified compared with the method of Bayesian network.Therefore,the proposed method has a good ability in the cost to reproduce the scene of the crime.
Braak, ter C.J.F.
2006-01-01
Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likeli
Yamamoto, Toshiyuki; Shimojima, Keiko; Ondo, Yumiko; Imai, Katsumi; Chong, Pin Fee; Kira, Ryutaro; Amemiya, Mitsuhiro; Saito, Akira; Okamoto, Nobuhiko
2016-01-01
Next-generation sequencing (NGS) is widely used for the detection of disease-causing nucleotide variants. The challenges associated with detecting copy number variants (CNVs) using NGS analysis have been reported previously. Disease-related exome panels such as Illumina TruSight One are more cost-effective than whole-exome sequencing (WES) because of their selective target regions (~21% of the WES). In this study, CNVs were analyzed using data extracted through a disease-related exome panel analysis and the eXome Hidden Markov Model (XHMM). Samples from 61 patients with undiagnosed developmental delays and 52 healthy parents were included in this study. In the preliminary study to validate the constructed XHMM system (microarray-first approach), 34 patients who had previously been analyzed by chromosomal microarray testing were used. Among the five CNVs larger than 200 kb that were considered as non-pathogenic CNVs and were used as positive controls, four CNVs was successfully detected. The system was subsequently used to analyze different samples from 27 patients (NGS-first approach); 2 of these patients were successfully diagnosed as having pathogenic CNVs (an unbalanced translocation der(5)t(5;14) and a 16p11.2 duplication). These diagnoses were re-confirmed by chromosomal microarray testing and/or fluorescence in situ hybridization. The NGS-first approach generated no false-negative or false-positive results for pathogenic CNVs, indicating its high sensitivity and specificity in detecting pathogenic CNVs. The results of this study show the possible clinical utility of pathogenic CNV screening using disease-related exome panel analysis and XHMM.
Kostiou, Vasiliki D; Theodoropoulou, Margarita C; Hamodrakas, Stavros J
2016-05-01
Heterotrimeric G-proteins form a major protein family, which participates in signal transduction. They are composed of three subunits, Gα, Gβ and Gγ. The Gα subunit is further divided in four distinct families Gs, Gi/o, Gq/11 and G12/13. The goal of this work was to detect and classify members of the four distinct families, plus the Gβ and the Gγ subunits of G-proteins from sequence alone. To achieve this purpose, six specific profile Hidden Markov Models (pHMMs) were built and checked for their credibility. These models were then applied to ten (10) proteomes and were able to identify all known G-protein and classify them into the distinct families. In a separate case study, the models were applied to twenty seven (27) arthropod proteomes and were able to give more credible classification in proteins with uncertain annotation and in some cases to detect novel proteins. An online tool, GprotPRED, was developed that uses these six pHMMs. The sensitivity and specificity for all pHMMs were equal to 100% with the exception of the Gβ case, where sensitivity equals to 100%, while specificity is 99.993%. In contrast to Pfam's pHMM which detects Gα subunits in general, our method not only detects Gα subunits but also classifies them into the appropriate Gα-protein family and thus could become a useful tool for the annotation of G-proteins in newly discovered proteomes. GprotPRED online tool is publicly available for non-commercial use at http://bioinformatics.biol.uoa.gr/GprotPRED and, also, a standalone version of the tool at https://github.com/vkostiou/GprotPRED. PMID:26854601
Yamamoto, Toshiyuki; Shimojima, Keiko; Ondo, Yumiko; Imai, Katsumi; Chong, Pin Fee; Kira, Ryutaro; Amemiya, Mitsuhiro; Saito, Akira; Okamoto, Nobuhiko
2016-01-01
Next-generation sequencing (NGS) is widely used for the detection of disease-causing nucleotide variants. The challenges associated with detecting copy number variants (CNVs) using NGS analysis have been reported previously. Disease-related exome panels such as Illumina TruSight One are more cost-effective than whole-exome sequencing (WES) because of their selective target regions (~21% of the WES). In this study, CNVs were analyzed using data extracted through a disease-related exome panel analysis and the eXome Hidden Markov Model (XHMM). Samples from 61 patients with undiagnosed developmental delays and 52 healthy parents were included in this study. In the preliminary study to validate the constructed XHMM system (microarray-first approach), 34 patients who had previously been analyzed by chromosomal microarray testing were used. Among the five CNVs larger than 200 kb that were considered as non-pathogenic CNVs and were used as positive controls, four CNVs was successfully detected. The system was subsequently used to analyze different samples from 27 patients (NGS-first approach); 2 of these patients were successfully diagnosed as having pathogenic CNVs (an unbalanced translocation der(5)t(5;14) and a 16p11.2 duplication). These diagnoses were re-confirmed by chromosomal microarray testing and/or fluorescence in situ hybridization. The NGS-first approach generated no false-negative or false-positive results for pathogenic CNVs, indicating its high sensitivity and specificity in detecting pathogenic CNVs. The results of this study show the possible clinical utility of pathogenic CNV screening using disease-related exome panel analysis and XHMM. PMID:27579173
Waldmann, Patrik; Hallander, Jon; Hoti, Fabian; Sillanpää, Mikko J.
2008-01-01
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general...
Continuous Time Markov Networks
El-Hay, Tal; Friedman, Nir; Koller, Daphne; Kupferman, Raz
2012-01-01
A central task in many applications is reasoning about processes that change in a continuous time. The mathematical framework of Continuous Time Markov Processes provides the basic foundations for modeling such systems. Recently, Nodelman et al introduced continuous time Bayesian networks (CTBNs), which allow a compact representation of continuous-time processes over a factored state space. In this paper, we introduce continuous time Markov networks (CTMNs), an alternative representation lang...
Energy Technology Data Exchange (ETDEWEB)
Kulkarni, Ramaprasad; Tuller, Markus; Fink, Wolfgang; Wildschild, Dorthe (Oregon State U.); (Ariz)
2012-07-27
Advancements in noninvasive imaging methods such as X-ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore-scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable three-dimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet-dry or dual-energy scans, image alignment, and subtraction analysis, commonly applied in X-ray micro-CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three-dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuff obtained with synchrotron X-ray micro-CT demonstrate great potential of the MRF image model for three-dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.
Directory of Open Access Journals (Sweden)
Alex Avilés
2016-01-01
Full Text Available The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC and Bayesian network- (BN based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.
Directory of Open Access Journals (Sweden)
Kanagi Kanapathy
2014-01-01
Full Text Available The research question is whether the positive relationship found between supplier involvement practices and new product development performances in developed economies also holds in emerging economies. The role of supplier involvement practices in new product development performance is yet to be substantially investigated in the emerging economies (other than China. This premise was examined by distributing a survey instrument (Jayaram’s (2008 published survey instrument that has been utilised in developed economies to Malaysian manufacturing companies. To gauge the relationship between the supplier involvement practices and new product development (NPD project performance of 146 companies, structural equation modelling was adopted. Our findings prove that supplier involvement practices have a significant positive impact on NPD project performance in an emerging economy with respect to quality objectives, design objectives, cost objectives, and “time-to-market” objectives. Further analysis using the Bayesian Markov Chain Monte Carlo algorithm, yielding a more credible and feasible differentiation, confirmed these results (even in the case of an emerging economy and indicated that these practices have a 28% impact on variance of NPD project performance. This considerable effect implies that supplier involvement is a must have, although further research is needed to identify the contingencies for its practices.
Directory of Open Access Journals (Sweden)
Carlen Peter L
2011-04-01
Full Text Available Abstract Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR analysis and Akaike Information Criterion (AICc were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal, early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was
Anticipated utility and rational expectations as approximations of Bayesian decision making
Cogley, Timothy W.; Sargent, Thomas J.
2005-01-01
For a Markov decision problem in which unknown transition probabilities serve as hidden state variables, we study the quality of two approximations to the decision rule of a Bayesian who each period updates his subjective distribu- tion over the transition probabilities by Bayes’ law. The first is the usual ratio- nal expectations approximation that assumes that the decision maker knows the transition probabilities. The second approximation is a version of Kreps’ (1998) anticipated utility mo...
The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach
Stewart, Benjamin; Ko, Jonathan; Fox, Dieter; Konolige, Kurt
2012-01-01
We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previously-built portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a "typical" environment as a hidden Markov model that generates sequences of views observed by a robot ...
Mondal, A.
2010-03-01
In this paper, we study the uncertainty quantification in inverse problems for flows in heterogeneous porous media. Reversible jump Markov chain Monte Carlo algorithms (MCMC) are used for hierarchical modeling of channelized permeability fields. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies using dynamic data such as production data. The search with Metropolis-Hastings algorithm results in very low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use a two-stage MCMC that utilizes upscaled models to screen the proposals. In our numerical results, we assume that the channels intersect the wells and the intersection locations are known. Our results show that the proposed algorithms are capable of capturing the channel boundaries and describe the permeability variations within the channels using dynamic production history at the wells. © 2009 Elsevier Ltd. All rights reserved.
Mares, Ileana; Mares, Constantin; Mihailescu, Mihaela
2013-04-01
In the present study, first, we achieve a stochastic modeling between sea level pressure (SLP) and the Danube lower basin discharge using observational daily data (1958-1999) during spring and then, we use this modeling result to estimate the discharge of the 21st century. The Danube discharge is considered as states of Hidden Markov Model (HMM), and observations are represented by atmospheric circulation (emissions). We want to estimate the discharge behavior in the 21st century knowing the pressure at sea level simulated by climate models. We take into account the properties of HMM that both states and observations are considered simultaneously. From the physical point of view, this association is correct, that in all calculations we consider values SLP with 10 days before the discharges, the lag for which the correlations are the most significant. For the Danube lower basin was considered Orsova station that is situated at the Danube entry in Romania. From the correlative analysis we found that the maximum correlation between SLP and Danube discharge at Orsova is in the grid point (47.5N; 20E), and the different atmospheric indices were calculated around this point. Thus, there were calculated indices like: vorticity, gradients S-N and W-E, centered on this point, as well as pressure mean values. All these measure were calculated considering both the values in the respective point and in the neighboring ones. The tests have revealed the fact that the best predictor is the mean pressure on the considered area. The mean pressure values were classified in 3 equal probable classes that we considered as states of the atmospheric circulations. Therefore we can conclude that the types of atmospheric circulation in their sequence give us the weather rainy or dry interval sequences which in turn is reflected in the succession of states of the Danube flows. Here we achieved a simple classification (3 states) of the SLP based on pressure mean values around the point
Institute of Scientific and Technical Information of China (English)
刘震; 王厚军; 龙兵; 张治国
2009-01-01
针对电子系统状态趋势预测问题,提出了一种加权隐马尔可夫模型的自回归趋势预测方法.该方法以自回归模型作为隐马尔可夫的状态输出,利用加权预测思想对马尔可夫链中的隐状态进行混合高斯模型的加权序列预测,并利用最大概率隐状态下的自回归系数计算模型输出.通过对实际的复杂混沌序列和电子系统BIT状态数据进行趋势预测,并针对不同模型参数下的预测结果进行实验分析,结果表明该方法对系统状态变化的趋势具有较好的预测性能.%A novel trend prediction approach based on weighed hidden Markov model (HMM) and autoregressive model (AR) is presented in order to solve this problem of bend prediction for complex electronic system. This approach regards the autoregressive model as the output of HMM, uses weighted prediction method and mixed Gaussianin model to predict the hidden state of Markov chain,and calculates the output of model by using the regression coefficient of the maximum probability hidden state. This approach is applied to the trend prediction of complex chaotic time series and typical electronic equipment's BIT data, and the effects of various model parameters on trend prediction precision are discussed.The experiments based on condition trend prediction for electronic equipments demonstrate the effectiveness of the method.
A hidden Markov model based algorithm for data stream classification algorithm%基于隐马尔可夫模型的流数据分类算法
Institute of Scientific and Technical Information of China (English)
潘怡; 何可可; 李国徽
2014-01-01
为优化周期性概念漂移分类精度，提出了一种基于隐马尔可夫模型的周期性流式数据分类（HMM -SDC）算法，算法结合实际可观测序列的输出建立漂移概念状态序列的转移矩阵概率模型，由观测值概率分布密度来预测状态的转移序列。当预测误差超过用户定义阈值时，算法能够更新优化转移矩阵参数，无须重复学习历史概念即可实现对数据概念漂移的有效预测。此外，算法采用半监督 K-M ean学习方法训练样本集，降低了人工标记样例的代价，能够避免隐形马尔可夫模型因标记样例不足而产生的欠学习问题。实验结果表明：相对传统集成分类算法，新算法对周期性数据漂移具有更好的分类精确度及分类时效性。%To improve the classification accuracy on data stream ,HMM -SDC(hidden Markov model based stream data classification )algorithm was presented . The invisible data concept states was a-ligned with the observable sequences through a hidden Markov chain model ,and the drifted concept could be forecasted with the actual observation value .When the mean predictive error was larger than a user defined threshold ,the state transition probability matrix was updated automatically without re-learning the historical data concepts . In addition , part of the unlabeled samples were classified through the semi-supervised K-Means method ,which reduced the impact of the insufficient labeled data for training the hidden Markov model .The experimental results show that the new algorithm has better performance than the traditional ensemble classification algorithm in periodical data stream clas-sification .
Energy Technology Data Exchange (ETDEWEB)
Nicoulaud-Gouin, V.; Giacalone, M.; Gonze, M.A. [Institut de Radioprotection et de Surete Nucleaire-PRP-ENV/SERIS/LM2E (France); Martin-Garin, A.; Garcia-Sanchez, L. [IRSN-PRP-ENV/SERIS/L2BT (France)
2014-07-01
Calibration of transfer models according to observation data is a challenge, especially if parameters uncertainty is required, and if competing models should be decided between them. Generally two main calibration methods are used: The frequentist approach in which the unknown parameter of interest is supposed fixed and its estimation is based on the data only. In this category, least squared method has many restrictions in nonlinear models and competing models need to be nested in order to be compared. The bayesian inference in which the unknown parameter of interest is supposed random and its estimation is based on the data and on prior information. Compared to frequentist method, it provides probability density functions and therefore pointwise estimation with credible intervals. However, in practical cases, Bayesian inference is a complex problem of numerical integration, which explains its low use in operational modeling including radioecology. This study aims to illustrate the interest and feasibility of Bayesian approach in radioecology particularly in the case of ordinary differential equations with non-constant coefficients models, which cover most radiological risk assessment models, notably those implemented in the Symbiose platform (Gonze et al, 2010). Markov Chain Monte Carlo (MCMC) method (Metropolis et al., 1953) was used because the posterior expectations are intractable integrals. The invariant distribution of the parameters was performed by the metropolis-Hasting algorithm (Hastings, 1970). GNU-MCSim software (Bois and Maszle, 2011) a bayesian hierarchical framework, was used to deal with nonlinear differential models. Two case studies including this type of model were investigated: An Equilibrium Kinetic sorption model (EK) (e.g. van Genuchten et al, 1974), with experimental data concerning {sup 137}Cs and {sup 85}Sr sorption and desorption in different soils studied in stirred flow-through reactors. This model, generalizing the K{sub d} approach
International Nuclear Information System (INIS)
Calibration of transfer models according to observation data is a challenge, especially if parameters uncertainty is required, and if competing models should be decided between them. Generally two main calibration methods are used: The frequentist approach in which the unknown parameter of interest is supposed fixed and its estimation is based on the data only. In this category, least squared method has many restrictions in nonlinear models and competing models need to be nested in order to be compared. The bayesian inference in which the unknown parameter of interest is supposed random and its estimation is based on the data and on prior information. Compared to frequentist method, it provides probability density functions and therefore pointwise estimation with credible intervals. However, in practical cases, Bayesian inference is a complex problem of numerical integration, which explains its low use in operational modeling including radioecology. This study aims to illustrate the interest and feasibility of Bayesian approach in radioecology particularly in the case of ordinary differential equations with non-constant coefficients models, which cover most radiological risk assessment models, notably those implemented in the Symbiose platform (Gonze et al, 2010). Markov Chain Monte Carlo (MCMC) method (Metropolis et al., 1953) was used because the posterior expectations are intractable integrals. The invariant distribution of the parameters was performed by the metropolis-Hasting algorithm (Hastings, 1970). GNU-MCSim software (Bois and Maszle, 2011) a bayesian hierarchical framework, was used to deal with nonlinear differential models. Two case studies including this type of model were investigated: An Equilibrium Kinetic sorption model (EK) (e.g. van Genuchten et al, 1974), with experimental data concerning 137Cs and 85Sr sorption and desorption in different soils studied in stirred flow-through reactors. This model, generalizing the Kd approach, distinguishes
Bayesian modeling of ChIP-chip data using latent variables.
Wu, Mingqi
2009-10-26
BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the
Bayesian modeling of ChIP-chip data using latent variables
Directory of Open Access Journals (Sweden)
Tian Yanan
2009-10-01
Full Text Available Abstract Background The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. Results In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. Conclusion The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results
Institute of Scientific and Technical Information of China (English)
周韶园; 谢磊; 王树青
2005-01-01
An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.
Application of Coupled Hidden Markov Models in Bearing Fault Diagnosis%耦合隐马尔可夫模型在轴承故障诊断中的应用
Institute of Scientific and Technical Information of China (English)
肖文斌; 陈进; 周宇
2011-01-01
由于多通道数据包含了丰富的信息,有效融合多通道数据可以得到更加准确可靠的诊断结果.鉴于此,提出一种基于耦合隐马尔可夫模型的滚动轴承多通道融合故障诊断方法.该方法利用含两条链的耦合隐马尔可夫模型融合轴承水平方向和垂直方向的振动信号来进行故障诊断.通过对滚动轴承常见故障的诊断分析表明,与常用的基于隐马尔可夫模型的故障诊断方法相比,该方法可以更加准确地诊断轴承的故障.%Due to the abundant information contained in multichannel data, a multichannel fusion method based on coupled hidden Markov models (CHMMs) is presented for rolling- element-bearing' s fault diagnosis. A double-chain CHMM is adopted to combine the horizontal and vertical vibration signals from bearings. An experiment was carried out to validate the proposed method. The experimental results show that the proposed method can improve the diagnostic accuracy when compared with the conventional hidden Markov model (HMM) based method.
Bayesian inference tools for inverse problems
Mohammad-Djafari, Ali
2013-08-01
In this paper, first the basics of Bayesian inference with a parametric model of the data is presented. Then, the needed extensions are given when dealing with inverse problems and in particular the linear models such as Deconvolution or image reconstruction in Computed Tomography (CT). The main point to discuss then is the prior modeling of signals and images. A classification of these priors is presented, first in separable and Markovien models and then in simple or hierarchical with hidden variables. For practical applications, we need also to consider the estimation of the hyper parameters. Finally, we see that we have to infer simultaneously on the unknowns, the hidden variables and the hyper parameters. Very often, the expression of this joint posterior law is too complex to be handled directly. Indeed, rarely we can obtain analytical solutions to any point estimators such the Maximum A posteriori (MAP) or Posterior Mean (PM). Three main tools are then can be used: Laplace approximation (LAP), Markov Chain Monte Carlo (MCMC) and Bayesian Variational Approximations (BVA). To illustrate all these aspects, we will consider a deconvolution problem where we know that the input signal is sparse and propose to use a Student-t prior for that. Then, to handle the Bayesian computations with this model, we use the property of Student-t which is modelling it via an infinite mixture of Gaussians, introducing thus hidden variables which are the variances. Then, the expression of the joint posterior of the input signal samples, the hidden variables (which are here the inverse variances of those samples) and the hyper-parameters of the problem (for example the variance of the noise) is given. From this point, we will present the joint maximization by alternate optimization and the three possible approximation methods. Finally, the proposed methodology is applied in different applications such as mass spectrometry, spectrum estimation of quasi periodic biological signals and
A new approach to simulating stream isotope dynamics using Markov switching autoregressive models
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.
Estimating demographic parameters using hidden process dynamic models.
Gimenez, Olivier; Lebreton, Jean-Dominique; Gaillard, Jean-Michel; Choquet, Rémi; Pradel, Roger
2012-12-01
Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error. PMID:22373775
2015-01-01
Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden...
Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays
Directory of Open Access Journals (Sweden)
Davis Caleb
2010-12-01
Full Text Available Abstract Background The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference. Results The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection. Conclusions We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.
Generator estimation of Markov jump processes
Metzner, P.; Dittmer, E.; Jahnke, T.; Schütte, Ch.
2007-11-01
Estimating the generator of a continuous-time Markov jump process based on incomplete data is a problem which arises in various applications ranging from machine learning to molecular dynamics. Several methods have been devised for this purpose: a quadratic programming approach (cf. [D.T. Crommelin, E. Vanden-Eijnden, Fitting timeseries by continuous-time Markov chains: a quadratic programming approach, J. Comp. Phys. 217 (2006) 782-805]), a resolvent method (cf. [T. Müller, Modellierung von Proteinevolution, PhD thesis, Heidelberg, 2001]), and various implementations of an expectation-maximization algorithm ([S. Asmussen, O. Nerman, M. Olsson, Fitting phase-type distributions via the EM algorithm, Scand. J. Stat. 23 (1996) 419-441; I. Holmes, G.M. Rubin, An expectation maximization algorithm for training hidden substitution models, J. Mol. Biol. 317 (2002) 753-764; U. Nodelman, C.R. Shelton, D. Koller, Expectation maximization and complex duration distributions for continuous time Bayesian networks, in: Proceedings of the twenty-first conference on uncertainty in AI (UAI), 2005, pp. 421-430; M. Bladt, M. Sørensen, Statistical inference for discretely observed Markov jump processes, J.R. Statist. Soc. B 67 (2005) 395-410]). Some of these methods, however, seem to be known only in a particular research community, and have later been reinvented in a different context. The purpose of this paper is to compile a catalogue of existing approaches, to compare the strengths and weaknesses, and to test their performance in a series of numerical examples. These examples include carefully chosen model problems and an application to a time series from molecular dynamics.
Stability analysis of networked control systems based on hidden Markov models%基于隐马尔可夫的网络控制系统稳定性分析
Institute of Scientific and Technical Information of China (English)
葛愿; 陈其工; 江明; 刘振安
2008-01-01
讨论了一类具有随机通信时延的网络控制系统的建模及稳定性分析,其中网络诱导时延受控于一概率分布未知的马尔可夫链,其概率分布可通过Baum-Welch算法计算.基于隐马尔町夫模型理论,将采用状态反馈的闭环网络控制系统建模成跳变线性系统,给出了这类网络控制系统随机稳定的允分条件,并将状态反馈控制器的求解问题转化为线性矩阵不等式的解的问题.最后,通过一个仿真算例说明了上述判定系统稳定性条件的有效性.%The modeling and stability analysis for a kind of networked control systems (NCSs) with random commu-nication delays are discussed. The network-induced delays are considered to be governed by an underlying Markov chain with unknown probability distribution, which can be calculated by using Baum-Welch algorithm. Based on hidden Markov model (HMM) theory, the resulting closed-loop systems are jump linear systems, and the sufficient conditions on the existence of the stabilizing controller are established by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is given to show the efficiency and feasibility of our proposed approach.
Markov Monitoring with Unknown States
Smyth, Padhraic
1993-01-01
Pattern recognition methods and hidden Markov models can be effective tools for online health monitoring of communications systems. Previous work has assumed that the states in the system model are exhaustive. This can be a significant drawback in real-world fault monitoring applications where it is difficult if not impossible to model all the possible fault states of the system in advance.
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
Institute of Scientific and Technical Information of China (English)
黄小娟; 吴荣腾
2014-01-01
人脸表情识别是人工智能领域中极富挑战性的课题，针对表情识别中存在的识别率低与计算量大的问题，提出了一种新的改进的隐马尔可夫表情识别模型参数优化的算法。先采用新的初始参数优化模型，然后利用Baum-Welch算法进行重估计，从而建立新的HMM人脸表情模型。实验结果表明，新模型明显提高了人脸表情的识别率并降低了计算量。%Facial expression recognition is quite a challenging subject in the field of artificial intelligence. Aiming at the problems of low recognition rate and the large computational problem of face expression recognition,a new modified parameter optimization algo-rithm is proposed for facial expression recognition based on the hidden Markov model. The method uses the initial parameters to opti-mize the model,and then uses Baum-Welch algorithm to estimate the parameters again. Hence,the new facial expression model based on HMM is established. The experimental results show that the new model significantly reduces the calculation amount and improve the facial expression recognition rate.
Institute of Scientific and Technical Information of China (English)
余文利; 廖建平; 马文龙
2010-01-01
针对传统的基于隐马尔可夫模型HMM(Hidden Markov model)的股票价格序列预测方法的不足,提出一种新的基于HMM的股票价格预测的方法.采用一种CBIC(Clustering and BIC)算法自动确定HMM隐状态数,在预测过程中当预测误差大于一定阈值时,采用模型自动更新方法建立新的模型.通过对股票价格序列的转换,建立相应的HMM,进行单步值预测.单步值预测与Hassan等人的HMM fusion model方法、ARIMA方法进行了比较,实验结果表明所提出的预测算法在股票价格预测中,比现有的不更新模型的方法能得到更好的结果.
Protocol Anomaly Detection Based on Hidden Markov Model%基于隐Markov模型的协议异常检测
Institute of Scientific and Technical Information of China (English)
赵静; 黄厚宽; 田盛丰
2010-01-01
入侵检测是网络安全领域的研究热点,协议异常检测更是入侵检测领域的研究难点.提出一种新的基于隐Markov模型(HMM)的协议异常检测模型.这种方法对数据包的标志位进行量化,得到的数字序列作为HMM的输入,从而对网络的正常行为建模.该模型能够区分攻击和正常网络数据.模型的训练和检测使用DARPA 1999年的数据集,实验结果验证了所建立模型的准确性,同现有的基于Markov链(Markov chain)的检测方法相比,提出的方法具有较高的检测率.
Bayesian tomographic reconstruction of microsystems
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-11-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast). To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique. In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations.
Testing the Markov condition in ion channel recordings
Timmer, J
1997-01-01
A statistical test is presented to decide whether data are adequately described by probabilistic functions of finite state Markov chains (''hidden Markov models'') as applied in the analysis of ion channel data. Particularly, the test can be used to decide whether a system obeys the Markov condition. Simulation studies are performed in order to investigate the sensitivity of the proposed test against violations of the model assumptions. The test can be applied analogously to Markov models.
人脸面部表情图像的隐马尔科夫建模及情感识别%Facial Images Using Hidden Markov Models and Emotion Recognition
Institute of Scientific and Technical Information of China (English)
穆静; 陈芳; 王长元
2015-01-01
The paper describes the emotion recognition of facial images by using Hidden Markov Models (HMM) .Firstly ,the facial expression images were sampled by using the sub-window and the feature vectors were extracted by using discrete cosine transformation .Then HMMs of facial expression images were constructed .The feature vectors were used as observation vectors to train the HMMs of facial expression images .Finally ,emotion recognizing emotion images was realized by using the trained HMMs .The experiments on JAFFE database were conducted to recognize the seven emotions of the subjects ,with the recognition rate is above 87 .3% .Simulations demonstrate that the emotion recognition for facial expression images using HMMs is an effective method .%文中旨在使用隐马尔科夫模型对人脸面部表情图像进行建模以对人脸表情中的情感进行识别,采用子窗口对人脸面部表情图像进行采样 ,利用离散余弦变换提取了所需要的特征向量 ,通过对人脸面部图像进行隐马尔科夫建模 ,使用获得的特征向量作为观测向量对人脸面部图像的隐马尔科夫模型进行训练 .使用训练后的隐马尔科夫模型对JA FFE人脸图像测试集中的人脸表情图像进行情感识别 .测试结果表明 :隐马尔科夫模型用于人脸面部表情图像的情感识别有效可行 ,其总体正确识别率达到87 .3% .
Institute of Scientific and Technical Information of China (English)
余浩; 陈开颜; 张阳; 邓高明; 吴恒旭
2012-01-01
针对微控制器代码旁路逆向恢复的问题,采用逆向工程思想与旁路攻击方法,依据不同的指令在芯片内执行时,会产生不同的功耗旁路泄漏信号这一特点,在已实现的单条指令旁路模板恢复的基础上,综合考虑程序的＂上下文＂信息,运用隐马尔可夫模型（HMM）对该问题进行建模描述与求解.对AT89C52微控制器中运行的数据加密标准（DES）加密算法的部分指令序列的恢复实验表明,该方法能够有效的恢复出微控制器芯片中运行的指令序列.%In order to exploit prior knowledge about the program code, a math model, Hidden Markov Model （HMM）, is applied to describe and resolve a problem of recovering the program code of a mierocontroller. With the idea of reverse engineering and the method of Side-Channel Attacks （SCA）, and according to the property that different power consumption leakage characters for different instructions executed in chips, a practical experiment based on previous side channel templates built by making use of power consumption leakages for each instruction has shown that instruction sequences executed in a Data Encryption Standard （DES） microcontroller （AT89C52） cipher chip circuit can be reconstructed and has confirmed the effectiveness of method mentioned above.
Markov Models for Handwriting Recognition
Plotz, Thomas
2011-01-01
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden
Optimal Filter Approximations in Conditionally Gaussian Pairwise Markov Switching Models
Abbassi, N; Benboudjema, D; Derrode, Stéphane; Pieczynski, W
2015-01-01
—We consider a general triplet Markov Gaussian linear system (X, R, Y), where X is an hidden continuous random sequence, R is an hidden discrete Markov chain, Y is an observed continuous random sequence. When the triplet (X, R, Y) is a classical " Conditionally Gaussian Linear State-Space Model " (CGLSSM), the mean square error optimal filter is not workable with a reasonable complexity and different approximate methods, e.g. based on particle filters, are used. We propose two contributions. ...
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
International Nuclear Information System (INIS)
The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme-mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals ''out of the box'', handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.
Hidden Markov Models for Human Genes
DEFF Research Database (Denmark)
Baldi, Pierre; Brunak, Søren; Chauvin, Yves;
1997-01-01
complete internal exons flanked by introns, or splice sites flanked by coding and non-coding sequence. Together, models of donor site regions, acceptor site regions and flanked internal exons, show that exons - besides the reading frame - hold a specific periodic pattern. The pattern has the consensus: non...
Evolving the structure of hidden Markov Models
DEFF Research Database (Denmark)
won, K. J.; Prugel-Bennett, A.; Krogh, A.
2006-01-01
and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature....
A unified Bayesian hierarchical model for MRI tissue classification.
Feng, Dai; Liang, Dong; Tierney, Luke
2014-04-15
Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets. PMID:24738112
Energy Technology Data Exchange (ETDEWEB)
Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan; Ren, Huiying; Liu, Ying; Swiler, Laura P.
2016-07-04
The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesian model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.
Bayesian Analysis of Multivariate Probit Models
Siddhartha Chib; Edward Greenberg
1996-01-01
This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal dat...
DEFF Research Database (Denmark)
Krogh, Anders Stærmose; Riis, Søren Kamaric
1999-01-01
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...
Institute of Scientific and Technical Information of China (English)
欧阳红兵; 苏海军
2012-01-01
本文将隐Markov链对波动性和相关性的驱动分析引入DCC多元GARCH,对波动和相关分析建立起了直接的联系,进而考察次贷危机、欧洲债务危机在主要证券市场间的传染性。研究发现,高波动高相关机制为联动性提供了一种直接的表述方式,且这一机制在危机期间处于支配地位;次贷危机、欧洲债务危机具有传染性,传染期以区间的形式出现,且危机初期的市场在各机制间有较为频繁的转换,不可根据危机事件对样本进行武断地分割;同时,危机的传染在所考察的市场之间具有系统性,应对危机需要各国政策间的协调配合;另外,有证据显示美国次贷市场在2006年年中已显现出问题,有关国家贻误了深入分析和应对危机的时机。%A hidden Markov chain is introduced to drive both volatilities and correlations into dynamic conditional correlation multivariate GARCH model,which can put direct analysis to volatilities and correlations under one framework.Then the contagion resulted from American subprime mortgage crisis and European sovereign debt crisis among the major stock markets is in restigated.The findings are firstly,the regime of high volatility with high correlation,which dominates the market during crises,provides a direct way of expression to the concept of comovement.Second,the American subprime mortgage crisis and European sovereign debt crisis are contagious and emerge in the form of intervals,and show the market transfer between different regimes more frequent in the early stage of these crises.So it is arbitrary to investigate crisis contagion based on dividing sample into subsamples according to prior breakpoints.Third,it is necessary for countries to cooperate with each other because the contagion resulted from American subprime mortgage crisis and European sovereign debt crisis both are a systemic risk among the investigated markets.Finally,there is evidence that American subprime
Stabilization of stochastic systems with hidden Markovian jumps
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper considers the adaptive control of discrete-time hybrid stochastic systems with unknown randomly jumping parameters described by a finite-state hidden Markov chain. An intuitive yet longstanding conjecture in this area is that such hybrid systems can be adaptively stabilized whenever the rate of transition of the hidden Markov chain is small enough. This paper provides a rigorous positive answer to this conjecture by establishing the global stability of a gradient-algorithm-based adaptive linear-quadratic control.
Al-Ghraibah, Amani
error of approximately 3/4 a GOES class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a TPR. of 0.69 and a TNR of 0.86 for flare prediction, consistent with our previous studies of flare prediction using the same magnetic complexity features. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This conjecture is supported by our larger error rates of some 40 hours in the time-to-flare regression problem. The magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem. We also study the prediction of solar flare size and time-to-flare using two temporal features, namely the ▵- and ▵-▵-features, the same average size and time-to-flare regression error are found when these temporal features are used in size and time-to-flare prediction. In the third topic, we study the temporal evolution of active region magnetic fields using Hidden Markov Models (HMMs) which is one of the efficient temporal analyses found in literature. We extracted 38 features which describing the complexity of the photospheric magnetic field. These features are converted into a sequence of symbols using k-nearest neighbor search method. We study many parameters before prediction; like the length of the training window Wtrain which denotes to the number of history images use to train the flare and non-flare HMMs, and number of hidden states Q. In training phase, the model parameters of the HMM of each category are optimized so as to best describe the training symbol sequences. In testing phase, we use the best flare and non-flare models to predict/classify active regions as a flaring or non-flaring region
Graphs: Associated Markov Chains
Murthy, Garimella Rama
2012-01-01
In this research paper, weighted / unweighted, directed / undirected graphs are associated with interesting Discrete Time Markov Chains (DTMCs) as well as Continuous Time Markov Chains (CTMCs). The equilibrium / transient behaviour of such Markov chains is studied. Also entropy dynamics (Shannon entropy) of certain structured Markov chains is investigated. Finally certain structured graphs and the associated Markov chains are studied.
Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.
2012-12-01
In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by
Energy Technology Data Exchange (ETDEWEB)
Frank, T.D. [Center for the Ecological Study of Perception and Action, Department of Psychology, University of Connecticut, 406 Babbidge Road, Storrs, CT 06269 (United States)], E-mail: till.frank@uconn.edu
2008-06-16
Some elementary properties and examples of Markov processes are reviewed. It is shown that the definition of the Markov property naturally leads to a classification of Markov processes into linear and nonlinear ones.
International Nuclear Information System (INIS)
Some elementary properties and examples of Markov processes are reviewed. It is shown that the definition of the Markov property naturally leads to a classification of Markov processes into linear and nonlinear ones
Bayesian state space models for dynamic genetic network construction across multiple tissues.
Liang, Yulan; Kelemen, Arpad
2016-08-01
Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes. PMID:27343475
Markov constant and quantum instabilities
Pelantová, Edita; Starosta, Štěpán; Znojil, Miloslav
2016-04-01
For a qualitative analysis of spectra of certain two-dimensional rectangular-well quantum systems several rigorous methods of number theory are shown productive and useful. These methods (and, in particular, a generalization of the concept of Markov constant known in Diophantine approximation theory) are shown to provide a new mathematical insight in the phenomenologically relevant occurrence of anomalies in the spectra. Our results may inspire methodical innovations ranging from the description of the stability properties of metamaterials and of certain hiddenly unitary quantum evolution models up to the clarification of the mechanisms of occurrence of ghosts in quantum cosmology.
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Hidaka, Shohei
2015-01-01
A Markov process, which is constructed recursively, arises in stochastic games with Markov strategies. In this study, we defined a special class of random processes called the recursive Markov process, which has infinitely many states but can be expressed in a closed form. We derive the characteristic equation which the marginal stationary distribution of an arbitrary recursive Markov process needs to satisfy.
Markov processes and controlled Markov chains
Filar, Jerzy; Chen, Anyue
2002-01-01
The general theory of stochastic processes and the more specialized theory of Markov processes evolved enormously in the second half of the last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas. However, this may be the first volume dedicated to highlighting these synergies and, almost certainly, it is the first volume that emphasizes the contributions of the vibrant and growing Chinese school of probability. The chapters that appear in this book reflect both the maturity and the vitality of modern day Markov processes and controlled Markov chains. They also will provide an opportunity to trace the connections that have emerged between the work done by members of the Chinese school of probability and the work done by the European, US, Central and South Ameri...
Loredo, T J
2004-01-01
I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational eff...
Applying Hierarchical Bayesian Neural Network in Failure Time Prediction
Directory of Open Access Journals (Sweden)
Ling-Jing Kao
2012-01-01
Full Text Available With the rapid technology development and improvement, the product failure time prediction becomes an even harder task because only few failures in the product life tests are recorded. The classical statistical model relies on the asymptotic theory and cannot guarantee that the estimator has the finite sample property. To solve this problem, we apply the hierarchical Bayesian neural network (HBNN approach to predict the failure time and utilize the Gibbs sampler of Markov chain Monte Carlo (MCMC to estimate model parameters. In this proposed method, the hierarchical structure is specified to study the heterogeneity among products. Engineers can use the heterogeneity estimates to identify the causes of the quality differences and further enhance the product quality. In order to demonstrate the effectiveness of the proposed hierarchical Bayesian neural network model, the prediction performance of the proposed model is evaluated using multiple performance measurement criteria. Sensitivity analysis of the proposed model is also conducted using different number of hidden nodes and training sample sizes. The result shows that HBNN can provide not only the predictive distribution but also the heterogeneous parameter estimates for each path.
Bayesian model selection applied to artificial neural networks used for water resources modeling
Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
2008-04-01
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.
On multitarget pairwise-Markov models
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.
Semi-Markov Arnason-Schwarz models.
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
Modeling and Parameter Estimation of a Class of General Hidden Markov Model%一类广义隐马尔科夫模型的建模与参数估计
Institute of Scientific and Technical Information of China (English)
胡可; 张大力
2005-01-01
It is well-known that HMM has been widely used in many fields. In this paper we will discuss a more general model, which is similar to Pairwise Markov Model (PMM) proposed by Wojciech Pieczynski. Compared to HMM, the state process here is not necessarily a Markov chain. So it has more general applications in image segmentation, speech signal processing, and etc. We will give a complete mathematical description for this model with discrete states and discrete observations, including modeling,state estimation and parameter estimation, which haven't been studied before. Based on the method proposed here, we will get a recursive algorithm for the estimation of the state and the parameters.%隐马尔科夫模型在很多方面已有广泛应用.讨论了一类更为一般的模型,这类模型由Wojciech Pieczynski首次提出,并且给出了在图像识别中的应用.这里首次给出在离散观测和离散状态下该模型的精确数学描述,其中包括建模、状态估计和参数估计,这些算法都是首次被提出的.
Dynamic risk management with Markov decision processes
Mundt, André Philipp
2008-01-01
An important tool in risk management is the implementation of risk measures. We study dynamic models where risk measures and dynamic risk measures can be applied. In particular, we solve various portfolio optimization problems and introduce a class of dynamic risk measures via the notion of Markov decision processes. Using Bayesian control theory we furthermore derive an extension of the latter setting when we face model uncertainty.
Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network
Institute of Scientific and Technical Information of China (English)
2012-01-01
In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
Markov-switching model for nonstationary runoff conditioned on El Nino information
DEFF Research Database (Denmark)
Gelati, Emiliano; Madsen, H.; Rosbjerg, Dan
2010-01-01
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...
Stochastic thermodynamics of hidden pumps
Esposito, Massimiliano; Parrondo, Juan M. R.
2015-05-01
We show that a reversible pumping mechanism operating between two states of a kinetic network can give rise to Poisson transitions between these two states. An external observer, for whom the pumping mechanism is not accessible, will observe a Markov chain satisfying local detailed balance with an emerging effective force induced by the hidden pump. Due to the reversibility of the pump, the actual entropy production turns out to be lower than the coarse-grained entropy production estimated from the flows and affinities of the resulting Markov chain. Moreover, in presence of a large time scale separation between the fast-pumping dynamics and the slow-network dynamics, a finite current with zero dissipation may be produced. We make use of these general results to build a synthetase-like kinetic scheme able to reversibly produce high free-energy molecules at a finite rate and a rotatory motor achieving 100% efficiency at finite speed.
Nonuniform Markov Geometric Measures
Neunhäuserer, J.
2015-01-01
We generalize results of Fan and Zhang [6] on absolute continuity and singularity of the golden Markov geometric series to nonuniform stochastic series given by arbitrary Markov process. In addition we describe an application of these results in fractal geometry.
Quantum Markov fields on graphs
Accardi, Luigi; Ohno, Hiromichi; Mukhamedov, Farrukh
2009-01-01
We introduce generalized quantum Markov states and generalized d-Markov chains which extend the notion quantum Markov chains on spin systems to that on $C^*$-algebras defined by general graphs. As examples of generalized d-Markov chains, we construct the entangled Markov fields on tree graphs. The concrete examples of generalized d-Markov chains on Cayley trees are also investigated.
Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model
Mo, Qianxing
2010-01-29
ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.
Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition
Stephenson, Todd Andrew; Magimai.-Doss, Mathew; Bourlard, Hervé
2001-01-01
Standard hidden Markov models (HMMs), as used in automatic speech recognition (ASR), calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned on the hidden state variable, considering the emissions independent of any other variable in the model. Recent work showed the benefit of conditioning the emission distributions on a discrete auxiliary variable, which is observed in training and hidden in recognition. Related work has shown the ...
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
Bayesian approach to rough set
Marwala, Tshilidzi
2007-01-01
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
International Nuclear Information System (INIS)
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on Rk (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
Barat, Éric; Dautremer, Thomas
2007-11-01
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution—normalized emission intensity of the spatial poisson process—is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on Rk (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM.
Characterization of prokaryotic and eukaryotic promoters usinghidden Markov models
DEFF Research Database (Denmark)
Pedersen, Anders Gorm; Baldi, Pierre; Brunak, Søren;
1996-01-01
In this paper we utilize hidden Markov models (HMMs) and information theory to analyze prokaryotic and eukaryotic promoters. We perform this analysis with special emphasis on the fact that promoters are divided into a number of different classes, depending on which polymerase-associated factors...
Detecting targets hidden in random forests
Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao
2009-05-01
Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.
Dynkin, E B
1960-01-01
Theory of Markov Processes provides information pertinent to the logical foundations of the theory of Markov random processes. This book discusses the properties of the trajectories of Markov processes and their infinitesimal operators.Organized into six chapters, this book begins with an overview of the necessary concepts and theorems from measure theory. This text then provides a general definition of Markov process and investigates the operations that make possible an inspection of the class of Markov processes corresponding to a given transition function. Other chapters consider the more c
Frost, Andrew J.; Thyer, Mark A.; Srikanthan, R.; Kuczera, George
2007-07-01
SummaryMulti-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box-Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney's main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box-Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.
Variational Infinite Hidden Conditional Random Fields.
Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja; Ghahramani, Zoubin
2015-09-01
Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases the computational cost of such algorithms often becomes prohibitive. These limitations can be overcome by variational techniques. In this paper, we present a generalized framework for infinite HCRF models, and a novel variational inference approach on a model based on coupled Dirichlet Process Mixtures, the HCRF-DPM. We show that the variational HCRF-DPM is able to converge to a correct number of represented hidden states, and performs as well as the best parametric HCRFs-chosen via cross-validation-for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences. PMID:26353136
Loredo, Thomas J.
2004-04-01
I describe a framework for adaptive scientific exploration based on iterating an Observation-Inference-Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data-measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object-show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.
Chain ladder method: Bayesian bootstrap versus classical bootstrap
Peters, Gareth W.; Mario V. W\\"uthrich; Shevchenko, Pavel V.
2010-01-01
The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In this context, a novel numerical procedure utilising Markov chain Monte Carlo (MCMC), ABC and a Bayesian bootstrap procedure was developed in a truly distribution-free setting. T...
International Nuclear Information System (INIS)
With the acceptance of QCD as the fundamental theory of strong interactions, one of the basic problems in the analysis of nuclear phenomena became how to consistently account for the effects of the underlying quark/gluon structure of nucleons and nuclei. Besides providing more detailed understanding of conventional nuclear physics, QCD may also point to novel phenomena accessible by new or upgraded nuclear experimental facilities. We discuss a few interesting applications of QCD to nuclear physics with an emphasis on the hidden color degrees of freedom
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Institute of Scientific and Technical Information of China (English)
刘芳; 毛志忠
2011-01-01
针对过程工业中强噪声环境下实时采集的控制过程海量数据难以在线精确检测的问题,提出了基于阶数自学习自回归隐马尔可夫模型(ARHMM)的工业控制过程异常数据在线检测方法.该算法采用自同归(AR)模型对时间序列进行拟合,利用隐马尔科夫模型(HMM)作为数据检测的工具,避免了传统检测方法中需要预先设定检测阈值的问题,并将传统的BDT(Brockwell-Dahlhaus-Trindade)算法改进成为对于时间和阶数均实施迭代的双重迭代结构,以实现ARHMM参数在线更新.为了减小异常数据对ARHMM参数更新的影响,本文采用先检测后更新的方式,根据检测结果采取不同的更新方法,提高了该算法的鲁棒性.模型数据仿真与应用试验结果证明,该算法具有较高的检测精度和抗干扰能力,同时具备在线检测的能力.通过与传统基于AR模型的异常数据检测方法比较,证明了该方法更适合作为过程工业控制过程数据的异常检测工具.%For the accurate online detection and collection of massive real-time data of a control process in strong noise environment, we propose an autoregressive hidden Markov model (AJRHMM) algorithm with order self-learning. This algorithm employs an AR model to fit the time series and makes use of the hidden Markov model as the basic detection tool for avoiding the deficiency in presetting the threshold in traditional detection methods. In order to update the parameters of ARHMM online, we adopt the improved traditional BDT(Brockwell-Dahlhaus-Trindade) algorithm with double iterative structures, in which the iterative calculations are performed respectively for both time and order. To reduce the influence of outlier on parameter updating in ARHMM, we adopt the strategy of detection-before-update, and select the method for updating based on the detection results. This strategy improves the robustness of the algorithm. Simulation with emulation data and
A hidden Ising model for ChIP-chip data analysis
Mo, Q.
2010-01-28
Motivation: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. Availability: A software called iChip is freely available at http://www.bioconductor.org/. Contact: moq@mskcc.org. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.
A bayesian integrative model for genetical genomics with spatially informed variable selection.
Cassese, Alberto; Guindani, Michele; Vannucci, Marina
2014-01-01
We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation. PMID:25288877
BayesPeak: Bayesian analysis of ChIP-seq data
Directory of Open Access Journals (Sweden)
Stark Rory
2009-09-01
Full Text Available Abstract Background High-throughput sequencing technology has become popular and widely used to study protein and DNA interactions. Chromatin immunoprecipitation, followed by sequencing of the resulting samples, produces large amounts of data that can be used to map genomic features such as transcription factor binding sites and histone modifications. Methods Our proposed statistical algorithm, BayesPeak, uses a fully Bayesian hidden Markov model to detect enriched locations in the genome. The structure accommodates the natural features of the Solexa/Illumina sequencing data and allows for overdispersion in the abundance of reads in different regions. Moreover, a control sample can be incorporated in the analysis to account for experimental and sequence biases. Markov chain Monte Carlo algorithms are applied to estimate the posterior distributions of the model parameters, and posterior probabilities are used to detect the sites of interest. Conclusion We have presented a flexible approach for identifying peaks from ChIP-seq reads, suitable for use on both transcription factor binding and histone modification data. Our method estimates probabilities of enrichment that can be used in downstream analysis. The method is assessed using experimentally verified data and is shown to provide high-confidence calls with low false positive rates.
DEFF Research Database (Denmark)
Justesen, Jørn
2005-01-01
A simple construction of two-dimensional (2-D) fields is presented. Rows and columns are outcomes of the same Markov chain. The entropy can be calculated explicitly.......A simple construction of two-dimensional (2-D) fields is presented. Rows and columns are outcomes of the same Markov chain. The entropy can be calculated explicitly....
Bayesian Approach to Neuro-Rough Models for Modelling HIV
Marwala, Tshilidzi
2007-01-01
This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.