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Merging datafiles containing information on overlapping sets of entities is achallenging task in the absence of unique identifiers, and is furthercomplicated when some entities are duplicated in the datafiles. Most approachesto this problem have focused on linking two files assumed to be free ofduplicates, or on detecting which records in a single file are duplicates.However, it is common in practice to encounter scenarios that fit somewhere inbetween or beyond these two settings. We propose a Bayesian approach for thegeneral setting of multifile record linkage and duplicate detection. We use anovel partition representation to propose a structured prior for partitionsthat can incorporate prior information about the data collection processes ofthe datafiles in a flexible manner, and extend previous models for comparisondata to accommodate the multifile setting. We also introduce a family of lossfunctions to derive Bayes estimates of partitions that allow uncertain portionsof the partitions to be left unresolved. The performance of our proposedmethodology is explored through extensive simulations. Code implementing themethodology is available at https://github.com/aleshing/multilink .

Multifile Partitioning for Record Linkage and Duplicate Detection