Bayesian Hidden Markov Models
Bayesian Hidden Markov Models is a probabilistic framework within Bayesian latent variable models that extends the standard Hidden Markov Model by treating model parameters as random variables subject to prior distributions, thereby enabling full posterior inference over both latent states and parameters through Bayesian updating.
125
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4.9K
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344
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202
Institutions
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2017–2026
30
Variants
Bayesian Hmms
125
Hidden Markov Model Induction by Bayesian Model Merging
Andreas Stolcke, Stephen M. Omohundro · 1992 · 242 citations
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Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
6 publications
175 citations
Brno University of Technology
Brno, Czechia
4 h-index
16 publications
339 citations
Columbia University
New York, United States
4 h-index
5 publications
308 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
7 publications
262 citations

Rice University
Houston, United States
3 h-index
8 publications
279 citations
University of California, Irvine
Irvine, United States
3 h-index
5 publications
197 citations
National Cheng Kung University
Tainan City, Taiwan
3 h-index
4 publications
102 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
4 publications
199 citations
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Repository · 8 publications · 326 citations
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5 publications · 255 citations
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CSDA · 3 publications · 51 citations
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3 publications · 74 citations
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2 publications · 66 citations
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