Bayesian Dynamic Models

Bayesian Dynamic Models is a class of statistical models that incorporate time-varying parameters and latent states, distinguished by the application of Bayesian inference to update probabilistic beliefs about the evolving system dynamics through sequential data observation.

971

Publications

52.5K

Citations

2.6K

Authors

1K

Institutions

Publications per year

2017–2026

352

Authors

2.6K

  1. BC
    Baoping Cai

    China University of Petroleum, East China, China

    15 publications · 1.3K citations

    15

    h-index

  2. MW
    Mike West

    Duke University, United States

    11 publications · 893 citations

    10

    h-index

  3. GK
    Gary Koop

    University of Strathclyde, United Kingdom

    9 publications · 373 citations

    9

    h-index

  4. ZL
    Zengkai Liu

    China University of Petroleum, East China, China

    9 publications · 701 citations

    9

    h-index

  5. FK
    Faisal Khan

    Memorial University of Newfoundland, Canada

    8 publications · 781 citations

    8

    h-index

1–5 of 2.6K

Venues

  1. Reliability Engineering & System Safety

    35 publications · 2.7K citations

    28

    h-index

  2. SSRN Electronic Journal

    Repository · 27 publications · 839 citations

    17

    h-index

  3. Journal of the American Statistical Association

    15 publications · 740 citations

    13

    h-index

  4. Journal of Econometrics

    13 publications · 1K citations

    13

    h-index

  5. Ocean Engineering

    12 publications · 430 citations

    12

    h-index

1–5