Blocked Sampling

Blocked Sampling is a Markov Chain Monte Carlo sampling strategy in which multiple variables are grouped into blocks and jointly updated within each iteration, rather than being sampled individually, to improve convergence and mixing properties.

28

Publications

3K

Citations

65

Authors

53

Institutions

Publications per year

2017–2026

8

Authors

65

  1. AG
    Alessandra Guglielmi

    Politecnico di Milano, Italy

    2 publications · 35 citations

    2

    h-index

  2. RA
    Raffaele Argiento

    Istituto di Matematica Applicata e Tecnologie Informatiche, Italy

    2 publications · 35 citations

    2

    h-index

  3. MD
    Michael Donaldson

    University of Manchester, United Kingdom

    2 publications · 46 citations

    2

    h-index

  4. SL
    Sik‐Yum Lee

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 59 citations

    2

    h-index

  5. IB
    Ilaria Bianchini

    Istituto di Matematica Applicata e Tecnologie Informatiche, Italy

    2 publications · 35 citations

    2

    h-index

1–5 of 65

Venues

  1. Statistics and Computing

    2 publications · 79 citations

    2

    h-index

  2. Community Dentistry And Oral Epidemiology

    2 publications · 46 citations

    2

    h-index

  3. Journal of the American Statistical Association

    2 publications · 1.6K citations

    2

    h-index

  4. Hormone and Metabolic Research

    1 publications · 49 citations

    1

    h-index

  5. Physical review. E

    PRE · 1 publications · 20 citations

    1

    h-index

1–5