Making Recursive Bayesian Inference Accessible

Abstract

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. Proposal-Recursive 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 a Markov chain Monte Carlo 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.

Keywords:

• Filtering
• Hierarchical model
• Iterative forecasting
• Markov chain Monte Carlo
• Parallel processing
• Sequential inference

Additional information

Funding

This research was funded by NSF EF 1241856, NSF DMS 1614392, and NSF DEB 1927177. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.