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 language | English |
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Article number | 13 |
Journal | Physical Sciences Forum |
Volume | 9 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2023 |