The Bayesian Approach To Inverse Problems

2013/02/27 by Masoumeh Dashti, Andrew M. Stuart, Dashti, Masoumeh +1 · 39 citations
Computer Science · Mathematics · #Algorithm #Applied mathematics #Artificial intelligence #Bayesian probability #Computer science #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Inverse #Inverse problem #Mathematical optimization #Mathematics #Probability (math.PR) #math.PR

paper · pdf · doi:10.48550/arxiv.1302.6989

published in arXiv (Cornell University) (Cornell University) · Lecture notes to appear in Handbook of Uncertainty Quantification, Editors R. Ghanem, D. Higdon and H. Owhadi, Springer, 2016

openalex publication_date 2013/02/27 · arxiv created 2015/07/02 · arxiv updated 2015/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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.

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