Variational Bayes' method for functions with applications to some inverse problems

Abstract

Bayesian approach, as a useful tool for quantifying uncertainties, has been widely used for solving inverse problems of partial differential equations (PDEs). One of the key difficulties for employing Bayesian approach for the issue is how to extract information from the posterior probability measure. Variational Bayes' method (VBM) is firstly and broadly studied in the field of machine learning, which has the ability to extract posterior information approximately by using much lower computational resources compared with the conventional sampling type methods. In this paper, we generalize the usual finite-dimensional VBM to infinite-dimensional space, which makes the usage of VBM for inverse problems of PDEs rigorously. We further establish general infinite-dimensional mean-field approximate theory, and apply this theory to abstract linear inverse problems with Gaussian and Laplace noise assumptions. The results on some numerical examples substantiate the effectiveness of the proposed approach.

arXiv comment35 pages

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Cited by in corpus (2)

• Stein variational gradient descent on infinite-dimensional space and applications to statistical inverse problems
• Non-centered parametric variational Bayes' approach for hierarchical inverse problems of partial differential equations