Sparse Bayesian Models

Sparse Bayesian Models is a Bayesian modeling framework that belongs to the class of probabilistic models characterized by the incorporation of prior distributions promoting sparsity, distinguishing it through its explicit mechanism for driving irrelevant or weakly supported parameters toward zero in order to achieve parsimonious representations.

576

Publications

31.7K

Citations

1.6K

Authors

608

Institutions

Publications per year

2017–2026

310

Authors

1.6K

  1. BD
    Bhaskar D. Rao

    University of California San Diego, United States

    16 publications · 2.8K citations

    15

    h-index

  2. JD
    Jisheng Dai

    Jiangsu University, China

    16 publications · 1.2K citations

    14

    h-index

  3. PG
    Peter Gerstoft

    University of California San Diego, United States

    14 publications · 876 citations

    14

    h-index

  4. YH
    Yong Huang

    Harbin Institute of Technology, China

    13 publications · 507 citations

    12

    h-index

  5. DW
    David Wipf

    Microsoft Research Asia (China), China

    10 publications · 2.5K citations

    10

    h-index

1–5 of 1.6K

Venues

  1. IEEE Transactions on Signal Processing

    36 publications · 4K citations

    26

    h-index

  2. IEEE Access

    15 publications · 380 citations

    14

    h-index

  3. Signal Processing

    18 publications · 677 citations

    14

    h-index

  4. IEEE Signal Processing Letters

    13 publications · 955 citations

    13

    h-index

  5. arXiv (Cornell University)

    Repository · 15 publications · 1.6K citations

    12

    h-index

1–5