Posterior Mean Estimation
Posterior Mean Estimation is a Bayesian inference method that belongs to the class of point estimation techniques, distinguished by its use of the expected value of the posterior distribution to estimate unknown parameters based on observed data and prior information.
349
Publications
24.6K
Citations
744
Authors
458
Institutions
Publications per year
2017–2026
69
Parents
349
The unscented Kalman filter for nonlinear estimation
Eric A. Wan, Rudolph van der Merwe · 2002 · 3.9K citations
Loss function‐based evaluation of DSGE models
Frank Schorfheide · Journal of Applied Econometrics · 2000 · 672 citations
Social Sciences, Economics, Finance +17
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Centre National de la Recherche Scientifique
Paris, France
14 h-index
39 publications
2.5K citations
North Carolina State University
Raleigh, United States
9 h-index
10 publications
929 citations

Imperial College London
London, United Kingdom
9 h-index
12 publications
356 citations
Purdue University West Lafayette
West Lafayette, United States
8 h-index
14 publications
510 citations
Harvard University
Cambridge, United States
8 h-index
12 publications
494 citations
University of California, Berkeley
Berkeley, United States
7 h-index
9 publications
576 citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
12 publications
427 citations
The University of Texas at Austin
Austin, United States
7 h-index
11 publications
529 citations
1–8 of 458
33 publications · 2.6K citations
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15 publications · 2.4K citations
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Repository · 12 publications · 283 citations
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11 publications · 1.9K citations
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7 publications · 468 citations
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