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Activity Number: 285 - Probabilistic Record Linkage and Inference with Merged Data
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #304814 Presentation 1 Presentation 2
Title: A Structured Prior for Sequential Bayesian Record Linkage
Author(s): Brendan McVeigh* and Jared S Murray
Companies: Carnegie Mellon University and University of Texas at Austin
Keywords: Record Linkage; Unsupervised learning; MCMC

Probabilistic record linkage is the problem of identifying sets of records from multiple databases which correspond to the same underlying entity in the absence of a unique identifier. For all but the smallest problems computational considerations mean that only a small subset of the possible record pairs can be considered for matching. In principle a multistage approach to this problem could deliver substantial gains in computational efficiency. Such an approach first considers a small number of candidate matches for each record, and only considers a larger number of candidates for records which remain unmatched after the first stage. We present a new record linkage prior and latent variable model which capture such a multistage approach. By fully incorporating the multistage approach into our statistical model we allow for valid posterior inference despite the multistage nature of the matching.

Authors who are presenting talks have a * after their name.

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