Bayesian Sequence Models
Bayesian Sequence Models is a class of probabilistic sequence modeling frameworks that integrate Bayesian inference to quantify uncertainty in predictions by treating model parameters as random variables with prior distributions updated through observed sequential data.
21
Publications
646
Citations
76
Authors
61
Institutions
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2017–2026
6
Parents
21
Frank Wood, Jan Gasthaus, Cédric Archambeau et al. · Communications of the ACM · 2011 · 52 citations
David Small, Rachel Smedley, Richard C. Chiverrell et al. · Geological Society of America Bulletin · 2018 · 42 citations · Full text
Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation
Florence Forbes, Senan Doyle, Daniel García-Lorenzo et al. · 2010 · 40 citations · Full text
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Inserm
Paris, France
3 h-index
7 publications
520 citations
Université Paris-Saclay
Gif-sur-Yvette, France
2 h-index
6 publications
480 citations
Cognitive Neuroimaging Lab
Gif-sur-Yvette, France
2 h-index
6 publications
480 citations
University of Sheffield
Sheffield, United Kingdom
2 h-index
2 publications
90 citations

Délégation Paris 5
Paris, France
2 h-index
2 publications
160 citations
Sorbonne Paris Cité
Paris, France
2 h-index
2 publications
160 citations

University of Exeter
Exeter, United Kingdom
2 h-index
2 publications
90 citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
2 publications
90 citations
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1 publications · 48 citations
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1 publications · 34 citations
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1 publications · 17 citations
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