Journal article
Simple, scalable and accurate posterior interval estimation
Biometrika, Vol.104(3), pp.665-680
09/01/2017
DOI: 10.1093/biomet/asx033
Abstract
Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those from the full posterior in the leading parametric order. Our algorithm has a better balance of accuracy and efficiency than its competitors across a variety of simulations and a real-data example.
Details
- Title: Subtitle
- Simple, scalable and accurate posterior interval estimation
- Creators
- Cheng Li - National University of SingaporeSanvesh Srivastava - University of IowaDavid B Dunson - Duke University
- Resource Type
- Journal article
- Publication Details
- Biometrika, Vol.104(3), pp.665-680
- Publisher
- OXFORD UNIV PRESS
- DOI
- 10.1093/biomet/asx033
- ISSN
- 0006-3444
- eISSN
- 1464-3510
- Number of pages
- 16
- Grant note
- Office of Naval Research United States National Science Foundation
- Language
- English
- Date published
- 09/01/2017
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257622602771
Metrics
15 Record Views