Inverse Problems for Physics-Based Process Models

We describe and compare two formulations of inverse problems for a physics-based process model in the context of uncertainty and random variability: the Bayesian inverse problem and the stochastic inverse problem. We describe the foundations of the two problems in order to create a context for inter...

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Bibliographic Details
Published inAnnual review of statistics and its application Vol. 11; no. 1; pp. 461 - 482
Main Authors Bingham, Derek, Butler, Troy, Estep, Don
Format Journal Article
LanguageEnglish
Published Annual Reviews 22.04.2024
Subjects
Bayesian calibration
Bayesian inverse problem
conditional probability
disintegration
inverse problem
probability
stochastic forward problem
stochastic inverse problem
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Summary:We describe and compare two formulations of inverse problems for a physics-based process model in the context of uncertainty and random variability: the Bayesian inverse problem and the stochastic inverse problem. We describe the foundations of the two problems in order to create a context for interpreting the applicability and solutions of inverse problems important for scientific and engineering inference. We conclude by comparing them to statistical approaches to related problems, including Bayesian calibration of computer models.
ISSN:2326-8298
2326-831X
DOI:10.1146/annurev-statistics-031017-100108