Maximum Entropy Priors

Maximum Entropy Priors is a method of prior specification in Bayesian statistics that selects the least informative prior distribution subject to specified constraints, ensuring maximal uncertainty while respecting known information.

44

Publications

2.8K

Citations

71

Authors

41

Institutions

Publications per year

2017–2026

8

Variants

Maxent Priors

Authors

71

  1. AM
    Ali Mohammad‐Djafari

    Centre National de la Recherche Scientifique, France

    4 publications · 137 citations

    4

    h-index

  2. AC
    Ariel Caticha

    University at Albany, State University of New York, United States

    1 publications · 35 citations

    2

    h-index

  3. SF
    S. F. Gull

    University of Cambridge, United Kingdom

    2 publications · 280 citations

    2

    h-index

  4. JS
    John Skilling

    University of Cambridge, United Kingdom

    2 publications · 466 citations

    2

    h-index

  5. GD
    G. Demoment

    Laboratoire des signaux et systèmes, France

    2 publications · 54 citations

    2

    h-index

1–5 of 71

Venues

  1. AIP conference proceedings

    1 publications · 35 citations

    2

    h-index

  2. Entropy

    2 publications · 466 citations

    2

    h-index

  3. IEEE Transactions on Medical Imaging

    2 publications · 47 citations

    2

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 29 citations

    2

    h-index

  5. Journal of Econometrics

    1 publications · 45 citations

    1

    h-index

1–5