Bayesian Binning Into Quantiles

Bayesian Binning Into Quantiles is a probabilistic calibration method that partitions predicted probabilities into quantile bins using a Bayesian framework to optimize bin boundaries and improve the reliability of uncertainty estimates.

7

Publications

1.1K

Citations

22

Authors

10

Institutions

Publications per year

2017–2026

6

Authors

22

  1. JS
    Johanna Schwarz

    Stockholm University, Sweden

    1 publications · 46 citations

    1

    h-index

  2. DH
    Dominik Heider

    Philipps University of Marburg, Germany

    1 publications · 46 citations

    1

    h-index

  3. RA
    Rahim Alhamzawi

    University of Al-Qadisiyah, Iraq

    1 publications · 31 citations

    1

    h-index

  4. MH
    Miloš Hauskrecht

    University of Pittsburgh, United States

    1 publications · 947 citations

    1

    h-index

  5. GC
    Gregory Cooper

    University of Pittsburgh, United States

    1 publications · 947 citations

    1

    h-index

1–5 of 22

Venues

  1. Journal of Hydrology

    1 publications · 25 citations

    1

    h-index

  2. 1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 947 citations

    1

    h-index

  4. Bioinformatics

    1 publications · 46 citations

    1

    h-index

  5. METRON

    1 publications · 31 citations

    1

    h-index

1–5