Bayesian System Identification

Bayesian System Identification is a probabilistic framework within system identification that uses Bayesian inference to estimate the posterior distribution of model parameters, incorporating prior knowledge and observed data to quantify uncertainty in dynamical system models.

255

Publications

13.5K

Citations

614

Authors

293

Institutions

Publications per year

2017–2026

109

Authors

614

  1. JL
    James L. Beck

    California Institute of Technology, United States

    16 publications · 3.1K citations

    15

    h-index

  2. SA
    Siu‐Kui Au

    City University of Hong Kong, Hong Kong SAR China

    11 publications · 1.6K citations

    11

    h-index

  3. BH
    Biao Huang

    University of Alberta, Canada

    7 publications · 295 citations

    7

    h-index

  4. LS
    Lambros S. Katafygiotis

    Hong Kong University of Science and Technology, Hong Kong SAR China

    6 publications · 208 citations

    6

    h-index

  5. AC
    Alessandro Chiuso

    University of Padua, Italy

    6 publications · 238 citations

    6

    h-index

1–5 of 614

Venues

  1. Mechanical Systems and Signal Processing

    MSSP · 26 publications · 1.5K citations

    21

    h-index

  2. Automatica

    10 publications · 793 citations

    10

    h-index

  3. 8

    h-index

  4. Journal of Engineering Mechanics

    7 publications · 1.5K citations

    7

    h-index

  5. Computer-Aided Civil and Infrastructure Engineering

    CACAIE · 6 publications · 492 citations

    6

    h-index

1–5