Proximal Nested Sampling with Data-Driven Priors for Physical Scientists

Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai, Marcelo Pereyra

Research output: Contribution to journalConference articlepeer-review

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Abstract

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review proximal nested sampling in a pedagogical manner in an attempt to elucidate the framework for physical scientists. Second, we show how proximal nested sampling can be extended in an empirical Bayes setting to support data-driven priors, such as deep neural networks learned from training data.
Original languageEnglish
Article number13
Journal Physical Sciences Forum
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Dec 2023

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