A Bayesian Approach for De-duplication in the Presence of Relational Data
Methodology
2021-11-17 v4
Abstract
In this paper, we study the impact of combining profile and network data in a de-duplication setting. We also assess the influence of a range of prior distributions on the linkage structure. Furthermore, we explore stochastic gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain samples from the posterior distribution for network parameters. Our methodology is evaluated using the RLdata500 data, which is a popular dataset in the record linkage literature.
Keywords
Cite
@article{arxiv.1909.06519,
title = {A Bayesian Approach for De-duplication in the Presence of Relational Data},
author = {Juan Sosa and Abel Rodriguez},
journal= {arXiv preprint arXiv:1909.06519},
year = {2021}
}