Bayesian Decision-theoretic Sample Size
Bayesian Decision-Theoretic Sample Size is a formal statistical framework that determines the optimal sample size by minimizing the expected posterior loss under a specified decision-theoretic criterion within a Bayesian inferential context.
28
Publications
1.2K
Citations
49
Authors
39
Institutions
Publications per year
2017–2026
1
28
1–5 of 28
49
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The University of Texas MD Anderson Cancer Center, United States
2 publications · 120 citations
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2
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1
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1
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1–5 of 49
39
Duke University
Durham, United States
3 h-index
3 publications
140 citations
Decision Sciences (United States)
Fort Walton Beach, United States
3 h-index
3 publications
140 citations
Harbor–UCLA Medical Center
Torrance, United States
2 h-index
2 publications
120 citations

UCLA Medical Center
Santa Monica, United States
2 h-index
2 publications
120 citations

University of Maryland, Baltimore
Baltimore, United States
2 h-index
2 publications
82 citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
2 publications
70 citations
Baylor Scott & White Health
Dallas, United States
1 h-index
1 publications
17 citations
University of Chicago
Chicago, United States
1 h-index
1 publications
17 citations
1–8 of 39
5 publications · 219 citations
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4 publications · 132 citations
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4 publications · 295 citations
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3 publications · 199 citations
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JBS · 2 publications · 40 citations
2
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