Online Bayesian Inference

Online Bayesian Inference is a methodological framework within Bayesian statistics that enables sequential updating of posterior distributions as new data arrive in a continuous or iterative manner, distinguishing it from batch or offline inference by its adaptive, real-time integration of evidence.

154

Publications

7.2K

Citations

460

Authors

207

Institutions

Publications per year

2017–2026

54

Authors

460

  1. MO
    Manfred Opper

    Technische Universität Berlin, Germany

    4 publications · 287 citations

    4

    h-index

  2. RH
    Ralf Herbrich

    Microsoft Research (United Kingdom), United Kingdom

    3 publications · 57 citations

    3

    h-index

  3. TL
    Thomas L. Griffiths

    University of California, Berkeley, United States

    3 publications · 400 citations

    3

    h-index

  4. MD
    Murat Dündar

    Indiana University – Purdue University Indianapolis, United States

    2 publications · 21 citations

    2

    h-index

  5. TM
    Takashi Matsumoto

    Waseda University, Japan

    2 publications · 26 citations

    2

    h-index

1–5 of 460

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 243 citations

    10

    h-index

  2. IEEE Transactions on Signal Processing

    5 publications · 435 citations

    5

    h-index

  3. Neural Information Processing Systems

    Conference · 4 publications · 117 citations

    4

    h-index

  4. 3

    h-index

  5. 3

    h-index

1–5