Hidden-state Models
Hidden-State Models is a statistical framework that belongs to the class of latent variable models, distinguished by its specification of unobservable (hidden) states that stochastically generate observable data through defined transition and emission probabilities, enabling inference of ancestral states, transcriptional dynamics, or other latent biological processes from indirect or noisy measurements.
860
Publications
66.4K
Citations
2.6K
Authors
904
Institutions
Publications per year
2017–2026
345
860
Hidden Markov Models in Computational Biology
Anders Krogh, Michael Brown, Shahzad I. Mian et al. · Journal of Molecular Biology · 1994 · 2K citations
Engineering, Bioinformatics, Biology +14
Generating Text with Recurrent Neural Networks
Ilya Sutskever, James Martens, Geoffrey E. Hinton · 2011 · 1.2K citations
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Centre National de la Recherche Scientifique
Paris, France
17 h-index
40 publications
2.4K citations
University of California, Berkeley
Berkeley, United States
16 h-index
26 publications
1.3K citations
University of Oxford
Oxford, United Kingdom
15 h-index
30 publications
1.8K citations
Massachusetts Institute of Technology
Cambridge, United States
15 h-index
36 publications
7.2K citations
University College London
London, United Kingdom
15 h-index
31 publications
3.4K citations
University of Cambridge
Cambridge, United Kingdom
14 h-index
43 publications
5.7K citations
Tsinghua University
Beijing, China
12 h-index
20 publications
965 citations

University of Toronto
Toronto, Canada
12 h-index
23 publications
7.1K citations
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Repository · 63 publications · 3.8K citations
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17 publications · 959 citations
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10 publications · 782 citations
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Conference · 10 publications · 762 citations
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PRA · 9 publications · 454 citations
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