Expected Information Gain
Expected Information Gain is a metric in Bayesian Experimental Design that quantifies the anticipated reduction in posterior uncertainty about model parameters, calculated as the expected Kullback-Leibler divergence between the prior and posterior distributions over possible experimental outcomes.
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14K
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547
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280
Institutions
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2017–2026
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An analog of the minimax theorem for vector payoffs
David Blackwell · Pacific Journal of Mathematics · 1956 · 758 citations · Full text
Expected Information as Expected Utility
José M. Bernardo · The Annals of Statistics · 1979 · 582 citations
Information based adaptive robotic exploration
Frédéric Bourgault, Alexei Makarenko, Stefan B. Williams et al. · 2003 · 445 citations
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Stanford University
Stanford, United States
10 h-index
19 publications
1.2K citations
University of Michigan
Ann Arbor, United States
9 h-index
13 publications
590 citations
University of Oxford
Oxford, United Kingdom
6 h-index
9 publications
186 citations

University of Toronto
Toronto, Canada
6 h-index
8 publications
402 citations

University of Bristol
Bristol, United Kingdom
6 h-index
8 publications
272 citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
11 publications
1.6K citations
New York University
New York, United States
5 h-index
11 publications
529 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
9 publications
564 citations
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Repository · 12 publications · 654 citations
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5 publications · 633 citations
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Conference · 4 publications · 224 citations
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3 publications · 807 citations
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3 publications · 91 citations
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