Real-time Bayesian Inference

Real-Time Bayesian Inference is a Bayesian inference framework that enables continuous updating of posterior beliefs from sequential data streams with minimal latency, distinguishing itself by its emphasis on computational efficiency and immediate responsiveness to incoming observations.

56

Publications

3K

Citations

173

Authors

106

Institutions

Publications per year

2017–2026

15

Authors

173

  1. CL
    Cristiana Larizza

    University of Pavia, Italy

    2 publications · 343 citations

    2

    h-index

  2. PK
    Pavle Kecman

    Delft University of Technology, Netherlands

    2 publications · 164 citations

    2

    h-index

  3. CB
    Carlo Berzuini

    University of Pavia, Italy

    2 publications · 343 citations

    2

    h-index

  4. NB
    Nicola Best

    Imperial College London, United Kingdom

    2 publications · 343 citations

    2

    h-index

  5. WR
    Walter R. Gilks

    Medical Research Council, United Kingdom

    2 publications · 343 citations

    2

    h-index

1–5 of 173

Venues

  1. 2

    h-index

  2. Comptes Rendus Mécanique

    1 publications · 17 citations

    1

    h-index

  3. 1

    h-index

  4. IISE Transactions

    1 publications · 21 citations

    1

    h-index

  5. Journal of Econometrics

    1 publications · 16 citations

    1

    h-index

1–5