The Bayesian approach to inverse problems

Dashti, Masoumeh and Stuart, Andrew (2016) The Bayesian approach to inverse problems. In: Ghanem, Roger, Higdon, David and Owhadi, Houman (eds.) Handbook of uncertainty quantification. 978-3319123844 . Springer International Publishing, pp. 1-118. ISBN 9783319112596

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Abstract

These lecture notes highlight the mathematical and computational structure relating to the formulation of, and development of algorithms for, the Bayesian approach to inverse problems in differential equations. This approach is fundamental in the quantification of uncertainty within applications involving the blending of mathematical models with data. The finite-dimensional situation is described first, along with some motivational examples. Then the development of probability measures on separable Banach space is undertaken, using a random series over an infinite set of functions to construct draws; these probability measures are used as priors in the Bayesian approach to inverse problems. Regularity of draws from the priors is studied in the natural Sobolev or Besov spaces implied by the choice of functions in the random series construction, and the Kolmogorov continuity theorem is used to extend regularity considerations to the space of Hölder continuous functions. Bayes’ theorem is derived in this prior setting, and here interpreted as finding conditions under which the posterior is absolutely continuous with respect to the prior, and determining a formula for the Radon-Nikodym derivative in terms of the likelihood of the data. Having established the form of the posterior, we then describe various properties common to it in the infinite-dimensional setting. These properties include well-posedness, approximation theory, and the existence of maximum a posteriori estimators. We then describe measure-preserving dynamics, again on the infinite-dimensional space, including Markov chain Monte Carlo and sequential Monte Carlo methods, and measure-preserving reversible stochastic differential equations. By formulating the theory and algorithms on the underlying infinite-dimensional space, we obtain a framework suitable for rigorous analysis of the accuracy of reconstructions, of computational complexity, as well as naturally constructing algorithms which perform well under mesh refinement, since they are inherently well defined in infinite dimensions.

Item Type: Book Section
Keywords: Inverse problems, Bayesian inversion, Tikhonov regularization and MAP estimators, Markov chain Monte Carlo, Sequential Monte Carlo, Langevin stochastic, partial differential equations
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Subjects: Q Science > QA Mathematics
Depositing User: Masoumeh Dashti
Date Deposited: 16 Jan 2017 16:21
Last Modified: 16 Jan 2017 16:21
URI: http://sro.sussex.ac.uk/id/eprint/66224

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