d-blink: Distributed End-to-End Bayesian Entity Resolution
From MaRDI portal
Publication:5066405
DOI10.1080/10618600.2020.1825451OpenAlexW3088072504MaRDI QIDQ5066405
Rebecca C. Steorts, Daniel N. Elazar, Neil G. Marchant, Andee Kaplan, Benjamin I. P. Rubinstein
Publication date: 29 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2020.1825451
Markov chain Monte Carloauxiliary variabledistributed computingrecord linkagepartially collapsed Gibbs sampling
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Entity resolution with empirically motivated priors
- A hierarchical Bayesian approach to record linkage and population size problems
- A unified framework for de-duplication and population size estimation (with discussion)
- Detecting duplicates in a homicide registry using a Bayesian partitioning approach
- Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
- Data Quality and Record Linkage Techniques
- Partially Collapsed Gibbs Samplers
- Multidimensional binary search trees used for associative searching
- An Algorithm for Finding Best Matches in Logarithmic Expected Time
- A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs
- Multivariate output analysis for Markov chain Monte Carlo
- Informed Proposals for Local MCMC in Discrete Spaces
- A Generalized Fellegi–Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems
- Regression Analysis With Linked Data
This page was built for publication: d-blink: Distributed End-to-End Bayesian Entity Resolution