Bayesian Smoothing

Bayesian Smoothing is a statistical inference method that belongs to the class of Bayesian estimation techniques, distinguished by its use of posterior distributions to estimate latent states in a time series by incorporating all available observational data, both past and future, to refine probabilistic beliefs about unobserved states.

134

Publications

9.3K

Citations

286

Authors

163

Institutions

Publications per year

2017–2026

19

Authors

286

  1. MD
    Maarten Dossche

    National Bank of Belgium, Belgium

    3 publications · 221 citations

    3

    h-index

  2. LF
    Ludwig Fahrmeir

    Ludwig-Maximilians-Universität München, Germany

    3 publications · 97 citations

    3

    h-index

  3. OR
    Ori Rosen

    The University of Texas at El Paso, United States

    3 publications · 138 citations

    3

    h-index

  4. CB
    Curtis B. Storlie

    Los Alamos National Laboratory, United States

    3 publications · 167 citations

    3

    h-index

  5. GE
    Gerdie Everaert

    Ghent University, Belgium

    3 publications · 221 citations

    3

    h-index

1–5 of 286

Venues

  1. Journal of the American Statistical Association

    13 publications · 934 citations

    13

    h-index

  2. Biometrika

    7 publications · 951 citations

    7

    h-index

  3. SSRN Electronic Journal

    Repository · 4 publications · 225 citations

    4

    h-index

  4. arXiv (Cornell University)

    Repository · 4 publications · 432 citations

    4

    h-index

  5. IEEE Transactions on Automatic Control

    4 publications · 151 citations

    4

    h-index

1–5