Making Recursive Bayesian inference accessible

American Statistician
By: , and 



Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive Bayesian methods are: Prior- and Proposal-Recursive Bayes. Prior-Recursive Bayes uses Bayesian updating, fitting models to partitions of data sequentially, and provides a way to accommodate new data as they become available using the posterior from the previous stage as the prior in the new stage based on the latest data. ProposalRecursive Bayes is intended for use with hierarchical Bayesian models and uses a set of transient priors in first stage independent analyses of the data partitions. The second stage of Proposal-Recursive Bayes uses the posteriors from the first stage as proposals in an MCMC algorithm to fit the full model. We combine Prior- and Proposal-Recursive concepts to fit any Bayesian model, and often with computational improvements. We demonstrate our method with two case studies. Our approach has implications for big data, streaming data, and optimal adaptive design situations.
Publication type Article
Publication Subtype Journal Article
Title Making Recursive Bayesian inference accessible
Series title American Statistician
DOI 10.1080/00031305.2019.1665584
Volume 75
Issue 2
Year Published 2021
Language English
Publisher Taylor & Francis
Contributing office(s) Coop Res Unit Seattle, Fort Collins Science Center
Description 10 p.
First page 185
Last page 194
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