Gaussian Sequence Models

Gaussian Sequence Models is a statistical framework within empirical Bayes methodology that characterizes observations as noisy realizations of an unknown sequence of parameters, distinguished by the assumption that both the noise and the prior distribution of parameters follow Gaussian distributions.

30

Publications

1.9K

Citations

52

Authors

34

Institutions

Publications per year

2017–2026

8

Variants

Gaussian Sequence Model

Authors

52

  1. RR
    Reza Rezaie

    University of New Orleans, United States

    2 publications · 29 citations

    2

    h-index

  2. ZP
    Zuoxiang Peng

    Southwest University, China

    2 publications · 25 citations

    2

    h-index

  3. IM
    Iain M. Johnstone

    Stanford University, United States

    2 publications · 66 citations

    2

    h-index

  4. YQ
    Yongcheng Qi

    University of Minnesota, Duluth, United States

    2 publications · 41 citations

    2

    h-index

  5. XR
    X. Rong Li

    University of New Orleans, United States

    2 publications · 29 citations

    2

    h-index

1–5 of 52

Venues

  1. The Annals of Statistics

    8 publications · 1.3K citations

    8

    h-index

  2. Theory of Probability and Its Applications

    2 publications · 49 citations

    2

    h-index

  3. Lithuanian Mathematical Journal

    2 publications · 29 citations

    2

    h-index

  4. Journal of Multivariate Analysis

    2 publications · 36 citations

    2

    h-index

  5. Metrika

    1 publications · 15 citations

    1

    h-index

1–5