Bayesian Population Synthesis

Bayesian Population Synthesis is a statistical framework for generating synthetic populations by estimating individual-level attributes through Bayesian inference, distinguishing itself from alternative methods by incorporating prior knowledge and uncertainty quantification into the probabilistic reconstruction of population structures from marginal and joint distributions.

29

Publications

1.3K

Citations

119

Authors

58

Institutions

Publications per year

2017–2026

10

Authors

119

  1. DD
    Daniela De Angelis

    MRC Biostatistics Unit, United Kingdom

    9 publications · 487 citations

    9

    h-index

  2. AM
    Anne M. Presanis

    MRC Biostatistics Unit, United Kingdom

    6 publications · 250 citations

    6

    h-index

  3. AE
    A. E. Ades

    University of Bristol, United Kingdom

    4 publications · 226 citations

    4

    h-index

  4. ON
    O N Gill

    Public Health England, United Kingdom

    4 publications · 180 citations

    4

    h-index

  5. AG
    Aïcha Goubar

    Inserm, France

    3 publications · 150 citations

    3

    h-index

1–5 of 119

Venues

  1. 2

    h-index

  2. Regional Studies Regional Science

    RSRS · 1 publications · 18 citations

    1

    h-index

  3. Statistics in Medicine

    1 publications · 30 citations

    1

    h-index

  4. Physical review. D/Physical review. D.

    PRD · 1 publications · 92 citations

    1

    h-index

  5. Psychological Bulletin

    1 publications · 36 citations

    1

    h-index

1–5