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arXiv · 2307.00056

Proximal nested sampling with data-driven priors for physical scientists

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

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Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai, Marcelo Pereyra. 2023-06-30. Proximal nested sampling with data-driven priors for physical scientists. https://arxiv.org/abs/2307.00056

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