  Researchers are often interested in linking individuals between two datasets
that lack a common unique identifier. Matching procedures often struggle to
match records with common names, birthplaces or other field values.
Computational feasibility is also a challenge, particularly when linking large
datasets. We develop a Bayesian method for automated probabilistic record
linkage and show it recovers more than 50% more true matches, holding accuracy
constant, than comparable methods in a matching of military recruitment data to
the 1900 US Census for which expert-labelled matches are available. Our
approach, which builds on a recent state-of-the-art Bayesian method, refines
the modelling of comparison data, allowing disagreement probability parameters
conditional on non-match status to be record-specific in the smaller of the two
datasets. This flexibility significantly improves matching when many records
share common field values. We show that our method is computationally feasible
in practice, despite the added complexity, with an R/C++ implementation that
achieves significant improvement in speed over comparable recent methods. We
also suggest a lightweight method for treatment of very common names and show
how to estimate true positive rate and positive predictive value when true
match status is unavailable.
