arxiv:2502.18300

Bayesian Computation in Deep Learning

Published on Feb 25

Authors:

Bolian Li ,

Abstract

This paper introduces Bayesian computation techniques for deep learning models, focusing on methods for Bayesian neural networks and deep generative models, and discusses the challenges and solutions in posterior inference.

AI-generated summary

This review paper is intended for the 2nd edition of the Handbook of Markov chain Monte Carlo. We provide an introduction to approximate inference techniques as Bayesian computation methods applied to deep learning models. We organize the chapter by presenting popular computational methods for Bayesian neural networks and deep generative models, explaining their unique challenges in posterior inference as well as the solutions.

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