The Experts below are selected from a list of 170961 Experts worldwide ranked by ideXlab platform
Sebastien Roch - One of the best experts on this subject based on the ideXlab platform.
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Learning nonsingular phylogenies and hidden Markov Models
The Annals of Applied Probability, 2006Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise, a well-known learning problem conjectured to be computationally hard. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
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learning nonsingular phylogenies and hidden Markov Models
Symposium on the Theory of Computing, 2005Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper, we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
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STOC - Learning nonsingular phylogenies and hidden Markov Models
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing - STOC '05, 2005Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper, we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
Elchanan Mossel - One of the best experts on this subject based on the ideXlab platform.
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Learning nonsingular phylogenies and hidden Markov Models
The Annals of Applied Probability, 2006Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise, a well-known learning problem conjectured to be computationally hard. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
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learning nonsingular phylogenies and hidden Markov Models
Symposium on the Theory of Computing, 2005Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper, we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
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STOC - Learning nonsingular phylogenies and hidden Markov Models
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing - STOC '05, 2005Co-Authors: Elchanan Mossel, Sebastien RochAbstract:In this paper, we study the problem of learning phylogenies and hidden Markov Models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov Models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov Models.
Guangyue Han - One of the best experts on this subject based on the ideXlab platform.
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Limit Theorems in Hidden Markov Models
IEEE Transactions on Information Theory, 2013Co-Authors: Guangyue HanAbstract:In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle, and a variant of the Chernoff bound in finite-state hidden Markov Models. These limit theorems are of interest in certain areas of information theory and statistics. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov Models.
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Limit Theorems in Hidden Markov Models
arXiv: Information Theory, 2011Co-Authors: Guangyue HanAbstract:In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle and a variant of Chernoff bound in finite-state hidden Markov Models. These limit theorems are of interest in certain ares in statistics and information theory. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov Models.
Xianhui Yang - One of the best experts on this subject based on the ideXlab platform.
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automatic creation of Markov Models for reliability assessment of safety instrumented systems
Reliability Engineering & System Safety, 2008Co-Authors: Xianhui YangAbstract:After the release of new international functional safety standards like IEC 61508, people care more for the safety and availability of safety instrumented systems. Markov analysis is a powerful and flexible technique to assess the reliability measurements of safety instrumented systems, but it is fallible and time-consuming to create Markov Models manually. This paper presents a new technique to automatically create Markov Models for reliability assessment of safety instrumented systems. Many safety related factors, such as failure modes, self-diagnostic, restorations, common cause and voting, are included in Markov Models. A framework is generated first based on voting, failure modes and self-diagnostic. Then, repairs and common-cause failures are incorporated into the framework to build a complete Markov model. Eventual simplification of Markov Models can be done by state merging. Examples given in this paper show how explosively the size of Markov model increases as the system becomes a little more complicated as well as the advancement of automatic creation of Markov Models.
Marc Lavielle - One of the best experts on this subject based on the ideXlab platform.
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Maximum likelihood estimation in discrete mixed hidden Markov Models using the SAEM algorithm
Computational Statistics and Data Analysis, 2012Co-Authors: Maud Delattre, Marc LavielleAbstract:Mixed hidden Markov Models have been recently defined in the literature as an extension of hidden Markov Models for dealing with population studies. The notion of mixed hidden Markov Models is particularly relevant for modeling longitudinal data collected during clinical trials, especially when distinct disease stages can be considered. However, parameter estimation in such Models is complex, especially due to their highly nonlinear structure and the presence of unobserved states. Moreover, existing inference algorithms are extremely time consuming when the model includes several random effects. New inference procedures are proposed for estimating population parameters, individual parameters and sequences of hidden states in mixed hidden Markov Models. The main contribution consists of a specific version of the stochastic approximation EM algorithm coupled with the Baum-Welch algorithm for estimating population parameters. The properties of this algorithm are investigated via a Monte-Carlo simulation study, and an application of mixed hidden Markov Models to the description of daily seizure counts in epileptic patients is presented.
