Simple, scalable and accurate posterior interval estimation

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Li, C; Srivastava, S; Dunson, DB

Published Date

  • September 1, 2017

Published In

Volume / Issue

  • 104 / 3

Start / End Page

  • 665 - 680

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asx033

Citation Source

  • Scopus