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A Bayesian approach for de-duplication in the presence of relational data

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  • Juan Sosa
  • Abel Rodríguez

Abstract

In this paper, we study the impact of combining profile and network data in solving record de-duplication problems. We also assess the influence of a range of prior distributions on the linkage structure, and explore the use of stochastic gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain samples from the posterior distribution for network parameters. Our methodology is evaluated using the RLdata500 data, which is a popular dataset in the record linkage literature.

Suggested Citation

  • Juan Sosa & Abel Rodríguez, 2024. "A Bayesian approach for de-duplication in the presence of relational data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(2), pages 197-215, January.
    • Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:197-215
    DOI: 10.1080/02664763.2022.2118678
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