  Finding duplicates in homicide registries is an important step in keeping an
accurate account of lethal violence. This task is not trivial when unique
identifiers of the individuals are not available, and it is especially
challenging when records are subject to errors and missing values. Traditional
approaches to duplicate detection output independent decisions on the
coreference status of each pair of records, which often leads to nontransitive
decisions that have to be reconciled in some ad-hoc fashion. The task of
finding duplicate records in a data file can be alternatively posed as
partitioning the data file into groups of coreferent records. We present an
approach that targets this partition of the file as the parameter of interest,
thereby ensuring transitive decisions. Our Bayesian implementation allows us to
incorporate prior information on the reliability of the fields in the data
file, which is especially useful when no training data are available, and it
also provides a proper account of the uncertainty in the duplicate detection
decisions. We present a study to detect killings that were reported multiple
times to the United Nations Truth Commission for El Salvador.
