Bayesian Inference For Random Fields

Bayesian Inference for Random Fields is a statistical framework that employs Bayesian principles to estimate and update the probabilistic structure of random fields by integrating observed data with prior knowledge, distinguishing itself through the explicit modeling of spatial or temporal dependencies within a hierarchical Bayesian setting.

91

Publications

6.3K

Citations

184

Authors

118

Institutions

Publications per year

2017–2026

17

Authors

184

  1. VD
    Victor De Oliveira

    The University of Texas at San Antonio, United States

    4 publications · 412 citations

    4

    h-index

  2. DS
    Dániel Straub

    Technical University of Munich, Germany

    3 publications · 118 citations

    3

    h-index

  3. KP
    Kok‐Kwang Phoon

    National University of Singapore, Singapore

    3 publications · 222 citations

    3

    h-index

  4. IP
    Iason Papaioannou

    Technical University of Munich, Germany

    3 publications · 96 citations

    3

    h-index

  5. JC
    Jianye Ching

    National Taiwan University, Taiwan

    2 publications · 170 citations

    2

    h-index

1–5 of 184

Venues

  1. 4

    h-index

  2. 3

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  3. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 115 citations

    3

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 2 publications · 35 citations

    2

    h-index

  5. Engineering Structures

    2 publications · 234 citations

    2

    h-index

1–5