Posterior Model Averaging is a Bayesian inference technique that belongs to the class of model averaging methods, differing by specifically using the posterior probabilities of models given observed data to compute weighted predictions or estimates across a set of candidate models.
44
Publications
4.3K
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108
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77
Institutions
Publications per year
2017–2026
9
Parents
Publications
44
David McAllester · 1999 · 372 citations
Bayesian Learning via Stochastic Dynamics
Radford M. Neal · Neural Information Processing Systems · 1992 · 184 citations
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Authors
108
- CP
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- EL
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1–5 of 108
Institutions
77
International Monetary Fund
Washington D.C., United States
4 h-index
4 publications
2K citations
Louisiana State University
Baton Rouge, United States
3 h-index
4 publications
129 citations
University of St Andrews
St Andrews, United Kingdom
3 h-index
3 publications
2K citations
University of Washington
Seattle, United States
3 h-index
3 publications
96 citations
SGH Warsaw School of Economics
Warsaw, Poland
2 h-index
5 publications
216 citations
University of Kent
Canterbury, United Kingdom
2 h-index
2 publications
2K citations
Ifo Institute for Economic Research
Munich, Germany
2 h-index
3 publications
99 citations
University of Toronto
Toronto, Canada
2 h-index
4 publications
232 citations
1–8 of 77
Venues
- SSRN Electronic Journal
Repository · 2 publications · 22 citations
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- Journal of Macroeconomics
2 publications · 66 citations
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- Empirica
1 publications · 20 citations
1
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- Forest Ecology and Management
1 publications · 22 citations
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- Stochastic Environmental Research and Risk Assessment
1 publications · 49 citations
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