Optimal Sampling Stopping
Optimal Sampling Stopping is a sequential decision-theoretic framework within optimal stopping theory that prescribes the stopping rule maximizing expected utility or minimizing expected cost when sampling from a sequence of observations under uncertainty, distinguished by its integration of sampling costs and information gain in determining the optimal termination point.
37
Publications
2.3K
Citations
96
Authors
65
Institutions
Publications per year
2017–2026
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University of Michigan
Ann Arbor, United States
2 h-index
2 publications
211 citations
University of Tennessee at Knoxville
Knoxville, United States
1 h-index
2 publications
328 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
2 publications
280 citations
Département Génétique Animale
Castanet-Tolosan, France
1 h-index
2 publications
456 citations
Curtin University
Perth, Australia
1 h-index
3 publications
105 citations
IBM (Germany)
Böblingen, Germany
1 h-index
1 publications
36 citations

The University of Sydney
Sydney, Australia
1 h-index
2 publications
26 citations

Tamkang University
Taipei, Taiwan
1 h-index
3 publications
159 citations
1–8 of 65
4 publications · 238 citations
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3 publications · 70 citations
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2 publications · 90 citations
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1 publications · 44 citations
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1 publications · 28 citations
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