Probabilistic Data Matching

Probabilistic Data Matching is a data integration method that identifies and links records from disparate sources by estimating the likelihood of correspondence based on weighted similarities across multiple fields, using statistical models to distinguish true matches from random coincidences.

46

Publications

1.9K

Citations

179

Authors

100

Institutions

Publications per year

2017–2026

9

Variants

Probabilistic Record Linkage

Authors

179

  1. SE
    Stephen E. Fienberg

    Carnegie Mellon University, United States

    2 publications · 44 citations

    2

    h-index

  2. RH
    Rob Hall

    Carnegie Mellon University, United States

    2 publications · 44 citations

    2

    h-index

  3. SM
    Sam M. Mbulaiteye

    National Cancer Institute, United States

    2 publications · 91 citations

    2

    h-index

  4. RC
    Rebecca C. Steorts

    Duke University, United States

    2 publications · 44 citations

    2

    h-index

  5. MA
    Matthew Armistead

    West Virginia University, United States

    1 publications · 18 citations

    1

    h-index

1–5 of 179

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 56 citations

    3

    h-index

  2. RePEc: Research Papers in Economics

    Repository · 2 publications · 54 citations

    2

    h-index

  3. Methods of Information in Medicine

    2 publications · 44 citations

    2

    h-index

  4. Prehospital Emergency Care

    2 publications · 58 citations

    2

    h-index

  5. Cadernos de Saúde Pública

    CSP · 1 publications · 22 citations

    1

    h-index

1–5