Machine Learning and the Future of Bayesian Computation

Steven Winter; Trevor Campbell; Lizhen Lin; Sanvesh Srivastava; David B Dunson

doi:10.48550/arxiv.2304.11251

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Machine Learning and the Future of Bayesian Computation

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Open access

Machine Learning and the Future of Bayesian Computation

Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava and David B Dunson

ArXiv.org

04/21/2023

DOI: 10.48550/arxiv.2304.11251

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https://doi.org/10.48550/arxiv.2304.11251View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.

Details

Title: Subtitle: Machine Learning and the Future of Bayesian Computation
Creators: Steven Winter
Trevor Campbell
Lizhen Lin
Sanvesh Srivastava
David B Dunson
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2304.11251
ISSN: 2331-8422
Language: English
Date posted: 04/21/2023
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984399495602771

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