Prior-likelihood Models

Prior-Likelihood Models is a formal decision-theoretic framework that integrates subjective prior beliefs with probabilistic likelihood assessments to model choice behavior under ambiguity, distinguishing itself from other models by its reliance on Bayesian updating to resolve uncertainty when objective probabilities are unknown.

27

Publications

2.9K

Citations

71

Authors

48

Institutions

Publications per year

2017–2026

9

Authors

71

  1. MA
    Miika Aittala

    Aalto University, Finland

    2 publications · 1.4K citations

    2

    h-index

  2. KP
    Konrad P. Körding

    Northwestern University, United States

    2 publications · 80 citations

    2

    h-index

  3. SL
    Samuli Laine

    Nvidia (United Kingdom), United Kingdom

    2 publications · 1.4K citations

    2

    h-index

  4. JH
    Jon Hasselgren

    Lund University, Sweden

    2 publications · 1.4K citations

    2

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  5. TA
    Timo Aila

    Nvidia (United Kingdom), United Kingdom

    2 publications · 1.4K citations

    2

    h-index

1–5 of 71

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 999 citations

    2

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  2. 1

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  3. 1

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  4. 1

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  5. Information & Management

    1 publications · 16 citations

    1

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1–5