Bayesian Dynamic Models
Bayesian Dynamic Models is a class of statistical models that incorporate time-varying parameters and latent states, distinguished by the application of Bayesian inference to update probabilistic beliefs about the evolving system dynamics through sequential data observation.
971
Publications
52.5K
Citations
2.6K
Authors
1K
Institutions
Publications per year
2017–2026
352
Parents
971
Bayesian Analysis of DSGE Models
Sungbae An, Frank Schorfheide · Econometric Reviews · 2007 · 1.3K citations
Computing, Biostatistics, Economics +17
Bayesian Methods for Dynamic Multivariate Models
Christopher A. Sims, Tao Zha · International Economic Review · 1998 · 1K citations
Jing Yu, V. Anne Smith, Paul P. Wang et al. · Bioinformatics · 2004 · 680 citations · Full text
1–5 of 971
2.6K
15
h-index
10
h-index
9
h-index
9
h-index
8
h-index
1–5 of 2.6K
1K
China University of Petroleum, East China
Qingdao, China
20 h-index
88 publications
6.2K citations
Duke University
Durham, United States
18 h-index
34 publications
3.5K citations
University of Pennsylvania
Philadelphia, United States
12 h-index
18 publications
4K citations

Centre National de la Recherche Scientifique
Paris, France
12 h-index
29 publications
830 citations
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
29 publications
1.8K citations
Memorial University of Newfoundland
St. John's, Canada
11 h-index
23 publications
2.4K citations
Tsinghua University
Beijing, China
11 h-index
28 publications
1.1K citations
University College London
London, United Kingdom
11 h-index
25 publications
1.6K citations
1–8 of 1K
35 publications · 2.7K citations
28
h-index
Repository · 27 publications · 839 citations
17
h-index
15 publications · 740 citations
13
h-index
13 publications · 1K citations
13
h-index
12 publications · 430 citations
12
h-index
1–5