Location Priors

Location Priors is a type of prior distribution in Bayesian inference that incorporates domain-specific knowledge about the probable values of a model’s location parameters, distinguishing itself by encoding expectations about the central tendency or spatial positioning of parameters within a parameter space.

55

Publications

6.6K

Citations

154

Authors

82

Institutions

Publications per year

2017–2026

5

Authors

154

  1. AO
    Anthony O’Hagan

    University of Sheffield, United Kingdom

    3 publications · 421 citations

    3

    h-index

  2. MF
    Mark F. J. Steel

    University of Warwick, United Kingdom

    3 publications · 119 citations

    3

    h-index

  3. BA
    Bogdan Alexe

    University of California, Santa Cruz, United States

    2 publications · 2.1K citations

    2

    h-index

  4. SP
    Simone Palazzo

    University of Catania, Italy

    2 publications · 91 citations

    2

    h-index

  5. BS
    Bruno Sansó

    Simón Bolívar University, Venezuela

    2 publications · 29 citations

    2

    h-index

1–5 of 154

Venues

  1. Biometrika

    6 publications · 548 citations

    6

    h-index

  2. The Annals of Statistics

    5 publications · 827 citations

    5

    h-index

  3. 3

    h-index

  4. Journal of Vision

    2 publications · 70 citations

    2

    h-index

  5. NeuroImage

    2 publications · 64 citations

    2

    h-index

1–5