Posterior Updating

Posterior Updating is a Bayesian inference procedure that belongs to the class of sequential learning methods, specifically characterized by the iterative revision of a parameter's probability distribution through the application of Bayes' theorem as new observational data becomes available.

177

Publications

10.4K

Citations

434

Authors

223

Institutions

Publications per year

2017–2026

61

Authors

434

  1. QH
    Qiang Han

    Beijing University of Technology, China

    5 publications · 287 citations

    5

    h-index

  2. PN
    Pinghe Ni

    Beijing University of Technology, China

    5 publications · 287 citations

    5

    h-index

  3. AR
    Anders Ringgaard Kristensen

    University of Copenhagen, Denmark

    4 publications · 135 citations

    4

    h-index

  4. AS
    Abdollah Shafieezadeh

    The Ohio State University, United States

    3 publications · 137 citations

    3

    h-index

  5. XD
    Xiuli Du

    Beijing University of Technology, China

    3 publications · 171 citations

    3

    h-index

1–5 of 434

Venues

  1. Mechanical Systems and Signal Processing

    MSSP · 8 publications · 589 citations

    8

    h-index

  2. 6

    h-index

  3. Artificial Intelligence

    5 publications · 429 citations

    5

    h-index

  4. Journal of Sound and Vibration

    JSV · 4 publications · 245 citations

    4

    h-index

  5. Reliability Engineering & System Safety

    4 publications · 112 citations

    4

    h-index

1–5