Results 1  10
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166
Hidden Markov models in computational biology: applications to protein modeling
 JOURNAL OF MOLECULAR BIOLOGY
, 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EFhand calcium binding moti ..."
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Cited by 525 (35 self)
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Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EFhand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the. SWISSPROT 22 database for other sequences. that are members of the given protein family, or contain the given domain. The Hi " produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate threedimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EFhand HMMs), the '\ HMM is able to distinguish members of these families from nonmembers with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false
Hidden Markov processes
 IEEE Trans. Inform. Theory
, 2002
"... Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finite ..."
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Cited by 170 (3 self)
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Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finitestate finitealphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximumlikelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed in this paper. Index Terms—Baum–Petrie algorithm, entropy ergodic theorems, finitestate channels, hidden Markov models, identifiability, Kalman filter, maximumlikelihood (ML) estimation, order estimation, recursive parameter estimation, switching autoregressive processes, Ziv inequality. I.
Continuous Probabilistic Transform for Voice Conversion
 IEEE Transactions on Speech and Audio Processing
, 1998
"... Abstract — Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the r ..."
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Cited by 129 (4 self)
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Abstract — Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic C noise model) system, which allows highquality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods. I.
A SplitMerge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model
 Journal of Computational and Graphical Statistics
, 2000
"... . We propose a splitmerge Markov chain algorithm to address the problem of inefficient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding to an ..."
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Cited by 91 (0 self)
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. We propose a splitmerge Markov chain algorithm to address the problem of inefficient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding to an inappropriate clustering of data points. This article describes a MetropolisHastings procedure that can escape such local modes by splitting or merging mixture components. Our MetropolisHastings algorithm employs a new technique in which an appropriate proposal for splitting or merging components is obtained by using a restricted Gibbs sampling scan. We demonstrate empirically that our method outperforms the Gibbs sampler in situations where two or more components are similar in structure. Key words: Dirichlet process mixture model, Markov chain Monte Carlo, MetropolisHastings algorithm, Gibbs sampler, splitmerge updates 1 Introduction Mixture models are often applied to density estim...
Bayesian Methods for Hidden Markov Models  Recursive Computing in the 21st Century
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2002
"... Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden Markov model (HMM) parameters from their posterior distribution given observed data. Some MCMC methods (for computing likelihood, conditional probabilities of hidden states, and the most likely sequence of states) use ..."
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Cited by 86 (8 self)
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Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden Markov model (HMM) parameters from their posterior distribution given observed data. Some MCMC methods (for computing likelihood, conditional probabilities of hidden states, and the most likely sequence of states) used in practice can be improved by incorporating established recursive algorithms. The most important is a set of forwardbackward recursions calculating conditional distributions of the hidden states given observed data and model parameters. We show how to use the recursive algorithms in an MCMC context and demonstrate mathematical and empirical results showing a Gibbs sampler using the forwardbackward recursions mixes more rapidly than another sampler often used for HMM's. We introduce an augmented variables technique for obtaining unique state labels in HMM's and finite mixture models. We show how recursive computing allows statistically efficient use of MCMC output when estimating the hidden states. We directly calculate the posterior distribution of the hidden chain's state space size by MCMC, circumventing asymptotic arguments underlying the Bayesian information criterion, which is shown to be inappropriate for a frequently analyzed data set in the HMM literature. The use of loglikelihood for assessing MCMC convergence is illustrated, and posterior predictive checks are used to investigate application specific questions of model adequacy.
Model Selection for Probabilistic Clustering Using CrossValidated Likelihood
 Statistics and Computing
, 1998
"... Crossvalidated likelihood is investigated as a tool for automatically determining the appropriate number of components (given the data) in finite mixture modelling, particularly in the context of modelbased probabilistic clustering. The conceptual framework for the crossvalidation approach to mod ..."
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Cited by 65 (4 self)
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Crossvalidated likelihood is investigated as a tool for automatically determining the appropriate number of components (given the data) in finite mixture modelling, particularly in the context of modelbased probabilistic clustering. The conceptual framework for the crossvalidation approach to model selection is direct in the sense that models are judged directly on their outofsample predictive performance. The method is applied to a wellknown clustering problem in the atmospheric science literature using historical records of upper atmosphere geopotential height in the Northern hemisphere. Crossvalidated likelihood provides strong evidence for three clusters in the data set, providing an objective confirmation of earlier results derived using nonprobabilistic clustering techniques. 1 Introduction Crossvalidation is a wellknown technique in supervised learning to select a model from a family of candidate models. Examples include selecting the best classification tree using cr...
Boundedly Rational Rule Learning in a Guessing Game
 GAMES AND ECONOMIC BEHAVIOR 16, 303–330 (1996)
, 1996
"... We combine Nagel’s “stepk” model of boundedly rational players with a “law of effect” learning model. Players begin with a disposition to use one of the stepk rules of behavior, and over time the players learn how the available rules perform and switch to better performing rules. We offer an econo ..."
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Cited by 59 (9 self)
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We combine Nagel’s “stepk” model of boundedly rational players with a “law of effect” learning model. Players begin with a disposition to use one of the stepk rules of behavior, and over time the players learn how the available rules perform and switch to better performing rules. We offer an econometric specification of this dynamic process and fit it to Nagel’s experimental data. We find that the rule of learning model vastly outperforms other nested and nonnested learning models. We find strong evidence for diverse dispositions and reject the Bayesian rulelearning model.
Colour Model Selection and Adaptation in Dynamic Scenes
, 1998
"... . We use colour mixture models for realtime colourbased object localisation, tracking and segmentation in dynamic scenes. Within such a framework, we address the issues of model order selection, modelling scene background and model adaptation in time. Experimental results are given to demonstrate ..."
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Cited by 51 (3 self)
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. We use colour mixture models for realtime colourbased object localisation, tracking and segmentation in dynamic scenes. Within such a framework, we address the issues of model order selection, modelling scene background and model adaptation in time. Experimental results are given to demonstrate our approach in different scale and lighting conditions. 1 Introduction Colour has been used in machine vision for tasks such as segmentation [1, 2], tracking [3] and recognition [4, 5]. Colour offers many advantages over geometric information in dynamic vision such as robustness under partial occlusion, rotation in depth, scale changes and resolution changes. Furthermore, using colour enables realtime performance on modest hardware platforms [1]. Swain and Ballard [5] described a scheme which used histograms for modelling the colours of an object. The colour space was quantised through the histogram's structure which comprised a number of "bins". An algorithm known as "histogram intersect...
Multiple Regimes in Northern Hemisphere Height Fields via Mixture Model Clustering
 J. Atmos. Sci
, 1998
"... Mixture model clustering is applied to Northern Hemisphere (NH) 700mb geopotential height anomalies. A mixture model is a flexible probability density estimation technique, consisting of a linear combination of k component densities. A key feature of the mixture modeling approach to clustering is t ..."
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Cited by 49 (28 self)
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Mixture model clustering is applied to Northern Hemisphere (NH) 700mb geopotential height anomalies. A mixture model is a flexible probability density estimation technique, consisting of a linear combination of k component densities. A key feature of the mixture modeling approach to clustering is the ability to estimate a posterior probability distribution for k, the number of clusters, given the data and the model, and thus objectively determine the number of clusters that is most likely to fit the data. A data set of 44 winters of NH 700mb fields is projected onto its two leading empirical orthogonal functions (EOFs) and analyzed using mixtures of Gaussian components. Crossvalidated likelihood is used to determine the best value of k, the number of clusters. The posterior probability so determined peaks at k = 3 and thus yields clear evidence for 3 clusters in the NH 700mb data. The 3cluster result is found to be robust with respect to variations in data preprocessing and data an...
ClusterBased Probability Model and Its Application to Image and Texture Processing
, 1997
"... We develop, analyze, and apply a specific form of mixture modeling for density estimation, within the context of image and texture processing. The technique captures much of the higherorder, nonlinear statistical relationships present among vector elements by combining aspects of kernel estimation ..."
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Cited by 49 (2 self)
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We develop, analyze, and apply a specific form of mixture modeling for density estimation, within the context of image and texture processing. The technique captures much of the higherorder, nonlinear statistical relationships present among vector elements by combining aspects of kernel estimation and cluster analysis. Experimental results are presented in the following applications: image restoration, image and texture compression, and texture classification. 1 Introduction In many signal processing tasks, uncertainty plays a fundamental role. Examples of such tasks are compression, detection, estimation, classification, and restoration  in all of these, the future inputs are not known perfectly at the time of system design, but instead must be characterized only in terms of their "typical," or "likely" behavior, by means of some probabilistic model. Every such system has a probabilistic model, be it explicit or implicit. Often, the level of performance achieved by such a syste...