Particle Gibbs Sampling
Particle Gibbs Sampling is a Markov chain Monte Carlo algorithm that belongs to the class of particle MCMC methods, distinguished by its integration of a conditional sequential Monte Carlo update within a Gibbs sampling framework to sample from the full posterior distribution of latent states in state-space models.
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1.4K
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75
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Institutions
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2017–2026
7
Variants
Pgibbs
Parents
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Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön · arXiv (Cornell University) · 2014 · 126 citations · Full text
High dimensional dynamic stochastic copula models
Ruey S. Tsay · Journal of Econometrics · 2015 · 111 citations
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Massachusetts Institute of Technology, United States
2 publications · 26 citations
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Linköping University
Linköping, Sweden
8 h-index
10 publications
511 citations
University of Cambridge
Cambridge, United Kingdom
6 h-index
9 publications
384 citations

Uppsala University
Uppsala, Sweden
3 h-index
3 publications
271 citations
University of Oxford
Oxford, United Kingdom
3 h-index
10 publications
124 citations
University of California, Berkeley
Berkeley, United States
3 h-index
3 publications
286 citations
University of Chicago
Chicago, United States
2 h-index
4 publications
354 citations

University of Bristol
Bristol, United Kingdom
2 h-index
2 publications
139 citations
Lancaster University
Lancaster, United Kingdom
2 h-index
2 publications
25 citations
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Repository · 6 publications · 228 citations
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3 publications · 135 citations
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2 publications · 36 citations
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Repository · 2 publications · 21 citations
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Repository · 2 publications · 94 citations
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