Bayesian State-space Inference
Bayesian State-Space Inference is a statistical framework for estimating the posterior distribution of latent states in a state-space model by combining prior knowledge with observed data through Bayes' theorem, distinguishing itself from classical inference methods by explicitly incorporating parameter and state uncertainty in a probabilistic manner.
198
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14.1K
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639
Authors
342
Institutions
Publications per year
2017–2026
68
Parents
198
On Gibbs sampling for state space models
Chris Carter, Robert Kohn · Biometrika · 1994 · 2K citations
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342
University of Washington
Seattle, United States
7 h-index
11 publications
1.1K citations

University of St Andrews
St Andrews, United Kingdom
7 h-index
24 publications
1.1K citations
University of Oxford
Oxford, United Kingdom
7 h-index
12 publications
541 citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
18 publications
399 citations
NOAA National Marine Fisheries Service
Silver Spring, United States
6 h-index
8 publications
329 citations
Duke University
Durham, United States
6 h-index
10 publications
672 citations
University College London
London, United Kingdom
6 h-index
10 publications
495 citations

Monash University
Melbourne, Australia
5 h-index
11 publications
312 citations
1–8 of 342
10 publications · 556 citations
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Repository · 11 publications · 285 citations
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6 publications · 236 citations
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5 publications · 479 citations
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5 publications · 175 citations
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