Gibbs Sampling is a Markov chain Monte Carlo method that sequentially samples each variable from its full conditional distribution given the current values of all other variables, thereby constructing a Markov chain whose stationary distribution converges to the target joint probability distribution.
2K
Publications
219.7K
Citations
3.8K
Authors
1.3K
Institutions
Publications per year
2017–2026
327
Publications
2K
Markov Chains for Exploring Posterior Distributions
Luke Tierney · The Annals of Statistics · 1994 · 3.5K citations · Full text
1–5 of 2K
Authors
3.8K
- DB
24
h-index
- JS
22
h-index
- GO
16
h-index
- AE
16
h-index
- LC
15
h-index
1–5 of 3.8K
Institutions
1.3K
Duke University
Durham, United States
32 h-index
124 publications
7.6K citations
University of Chicago
Chicago, United States
32 h-index
57 publications
14.1K citations
University of Minnesota
Minneapolis, United States
27 h-index
48 publications
7K citations
Cornell University
Ithaca, United States
25 h-index
53 publications
7.2K citations
University of Cambridge
Cambridge, United Kingdom
25 h-index
70 publications
6.1K citations
Imperial College London
London, United Kingdom
25 h-index
46 publications
18.5K citations
Stanford University
Stanford, United States
23 h-index
54 publications
14.3K citations
University of Washington
Seattle, United States
23 h-index
40 publications
7.5K citations
1–8 of 1.3K
Venues
- Journal of the American Statistical Association
102 publications · 33K citations
61
h-index
- arXiv (Cornell University)
Repository · 86 publications · 9.9K citations
28
h-index
- Journal of Computational and Graphical Statistics
JCGS · 41 publications · 6.4K citations
28
h-index
- Biometrics
37 publications · 2K citations
25
h-index
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
25 publications · 7.5K citations
24
h-index
1–5