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.

228

Publications

14K

Citations

547

Authors

280

Institutions

Publications per year

2017–2026

56

Authors

547

  1. AR
    Andrew R. Willan

    University of Toronto, Canada

    5 publications · 201 citations

    5

    h-index

  2. AO
    Alfred O. Hero

    University of Michigan, United States

    4 publications · 198 citations

    4

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  3. CS
    Cyrill Stachniss

    University of Bonn, Germany

    4 publications · 651 citations

    4

    h-index

  4. KK
    Keith Kastella

    University of Michigan, United States

    4 publications · 198 citations

    4

    h-index

  5. LT
    Laurence T. Maloney

    New York University, United States

    4 publications · 191 citations

    4

    h-index

1–5 of 547

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 654 citations

    11

    h-index

  2. Journal of Vision

    5 publications · 633 citations

    5

    h-index

  3. 4

    h-index

  4. Cognitive Science

    3 publications · 807 citations

    3

    h-index

  5. 3

    h-index

1–5