Prior Regularization

Prior Regularization is a methodological approach in Bayesian inference that belongs to the class of prior specification techniques, distinguished by its incorporation of regularization principles to constrain prior distributions in order to improve model generalization and robustness.

265

Publications

15.8K

Citations

764

Authors

398

Institutions

Publications per year

2017–2026

101

Authors

764

  1. AH
    Amir H. Golnabi

    Dartmouth College, United States

    6 publications · 416 citations

    6

    h-index

  2. PM
    Paul M. Meaney

    Dartmouth College, United States

    6 publications · 404 citations

    6

    h-index

  3. KD
    Keith D. Paulsen

    Dartmouth College, United States

    6 publications · 458 citations

    6

    h-index

  4. AV
    Arash Vahdat

    Simon Fraser University, Canada

    4 publications · 351 citations

    4

    h-index

  5. SD
    Shireen D. Geimer

    Dartmouth College, United States

    3 publications · 191 citations

    3

    h-index

1–5 of 764

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 764 citations

    16

    h-index

  2. IEEE Transactions on Image Processing

    9 publications · 631 citations

    9

    h-index

  3. IEEE Transactions on Medical Imaging

    6 publications · 367 citations

    6

    h-index

  4. IEEE Transactions on Biomedical Engineering

    5 publications · 613 citations

    5

    h-index

  5. Neural Information Processing Systems

    Conference · 4 publications · 160 citations

    4

    h-index

1–5