Health policy evidence-building requires data sources such as health care claims, electronic
health records, probability and nonprobability survey data, epidemiological surveillance …

Entity resolution is the process of merging and removing duplicate records from multiple
data sources, often in the absence of unique identifiers. Bayesian models for entity …

Standard clustering techniques assume a common configuration for all features in a dataset.
However, when dealing with multi-view or longitudinal data, the clusters' number …

Merging datafiles containing information on overlapping sets of entities is a challenging task
in the absence of unique identifiers, and is further complicated when some entities are …

A Unified Framework for De-Duplication and Population Size Estimation (with Discussion)
Page 1 Bayesian Analysis (2020) 15, Number 2, pp. 633–682 A Unified Framework for De-Duplication …

Clustering in high-dimensions poses many statistical challenges. While traditional distance-
based clustering methods are computationally feasible, they lack probabilistic interpretation …

Bayesian nonparametric mixtures and random partition models are powerful tools for
probabilistic clustering. However, standard independent mixture models can be restrictive in …

The availability of both structured and unstructured databases, such as electronic health
data, social media data, patent data, and surveys that are often updated in real time, among …

We develop a flexible Bayesian model for cluster covariance matrices in large dimensions
where the number of clusters and the assignment of cross-sectional units to a cluster are a …

In database management, record linkage aims to identify multiple records that correspond to
the same individual. Record linkage can be treated as a clustering problem in which one or …