Bayesian Optimal Stopping
Bayesian Optimal Stopping is an optimal stopping framework that incorporates Bayesian inference to sequentially update beliefs about unknown parameters and determine the optimal stopping rule that maximizes expected utility under uncertainty.
80
Publications
3.8K
Citations
143
Authors
77
Institutions
Publications per year
2017–2026
11
Parents
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Optimal Detection of a Change in Distribution
Moshe Pollak · The Annals of Statistics · 1985 · 592 citations
Optional stopping: No problem for Bayesians
Jeffrey N. Rouder · Psychonomic Bulletin & Review · 2014 · 531 citations
Selecting a Selection Procedure
Jürgen Branke, Stephen E. Chick, Christian Schmidt · Management Science · 2007 · 257 citations
A Bayesian Approach to Tackling Hard Computational Problems
Eric Horvitz, Yongshao Ruan, Carla P. Gomes et al. · arXiv (Cornell University) · 2013 · 116 citations · Full text
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London School of Economics and Political Science, United Kingdom
4 publications · 110 citations
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Steklov Mathematical Institute
Moscow, Russia
6 h-index
7 publications
196 citations
Columbia University
New York, United States
5 h-index
5 publications
116 citations
London School of Economics and Political Science
London, United Kingdom
4 h-index
5 publications
109 citations
Russian Academy of Sciences
Moscow, Russia
4 h-index
4 publications
154 citations
University of California, Irvine
Irvine, United States
3 h-index
5 publications
221 citations
Microsoft (United States)
Redmond, United States
3 h-index
7 publications
387 citations

Cornell University
Ithaca, United States
3 h-index
6 publications
356 citations
University of Michigan
Ann Arbor, United States
3 h-index
6 publications
86 citations
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8 publications · 839 citations
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4 publications · 144 citations
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Repository · 4 publications · 168 citations
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3 publications · 105 citations
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3 publications · 57 citations
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