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The Infinite Hidden Markov Model

by Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - Machine Learning , 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
Abstract - Cited by 637 (41 self) - Add to MetaCart
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data

An introduction to hidden Markov models

by L. R. Rabiner, B. H. Juang - IEEE ASSp Magazine , 1986
"... The basic theory of Markov chains has been known to ..."
Abstract - Cited by 1132 (2 self) - Add to MetaCart
The basic theory of Markov chains has been known to

What is a hidden Markov model?

by Sean R. Eddy , 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho-mologous resi ..."
Abstract - Cited by 1344 (8 self) - Add to MetaCart
, and a polyadenylation signal. All too often, piling more reality onto a fragile ad hoc program makes it collapse under its own weight. Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of

Coupled hidden Markov models for complex action recognition

by Matthew Brand, Nuria Oliver, Alex Pentland , 1996
"... We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and ..."
Abstract - Cited by 501 (22 self) - Add to MetaCart
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling

Hidden Markov Models and Analysis of

by P Yrococcushorikoshii Genome, Hilmi Yildirim
"... Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics, ..."
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Hidden Markov models are widely used in the areas of speech recognition and bioinformatics. Hidden Markov models differ from simple Markov models by including hidden states in addition to observable states. For example in bioinformatics,

Hidden Markov models for detecting remote protein homologies

by Kevin Karplus, Christian Barrett, Richard Hughey - Bioinformatics , 1998
"... A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98 is ..."
Abstract - Cited by 462 (15 self) - Add to MetaCart
A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98

Measures on Hidden Markov Models

by Rune B. Lyngsø, Christian N. S. Pedersen, Henrik Nielsen , 1999
"... Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylogenetic a ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylogenetic

Measures on hidden Markov models

by Henrik Nielsenz
"... Abstract Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction and phylo ..."
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Abstract Hidden Markov models were introduced in the beginning of the 1970's as a tool in speech recognition. During the last decade they have been found useful in addressing problems in computational biology such as characterising sequence families, gene finding, structure prediction

Profile Hidden Markov Models

by Lecturer Mona Singh
"... In the previous lecture, we began our discussion of profiles, and today we will talk about how to use hidden Markov models to build profiles. One of the advantages of using hidden Markov models for profile analysis is that they provide a better method for dealing with gaps found in protein families. ..."
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In the previous lecture, we began our discussion of profiles, and today we will talk about how to use hidden Markov models to build profiles. One of the advantages of using hidden Markov models for profile analysis is that they provide a better method for dealing with gaps found in protein families

Hidden Markov Models

by Martin Sewell , 2007
"... A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain observed in noise. The theory of hidden Markov models was developed in the late 1960s and early 1970s by Baum, Eag ..."
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A hidden Markov model (HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. Loosely speaking, it is a Markov chain observed in noise. The theory of hidden Markov models was developed in the late 1960s and early 1970s by Baum
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