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A review of Information Field Theory for Bayesian inference of random fields

Pandey, Aditya and Singh, Ashmeet and Gardoni, Paolo (2022) A review of Information Field Theory for Bayesian inference of random fields. Structural Safety, 99 . Art. No. 102225. ISSN 0167-4730. doi:10.1016/j.strusafe.2022.102225.

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Several physical problems require Bayesian inference of spatial, or spatio-temporal phenomenon – often modeled as random fields defined on a continuous domain – from a discrete set of data points. Kriging, based on Gaussian processes, is one of the commonly used tool for such inference problems. While Gaussian joint probability distributions have known closed form solutions, several physical phenomenon exhibit non-Gaussian features which are analytically intractable. In such problems, one often approximates the underlying distribution by some known, often simpler distribution (for example, a Gaussian), and infers an assigned parametric form for its moments. More rigorous analysis involves computationally expensive methods such as Markov Chain Monte Carlo (MCMC) methods. This paper presents a review of the diagrammatic perturbation theory (following Feynman diagrams used in Physics), a particular technique developed as part of Information Field Theory, for analytically estimating moments of perturbative non-Gaussian distributions.

Item Type:Article
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Pandey, Aditya0000-0002-6075-8310
Singh, Ashmeet0000-0002-4404-1416
Additional Information:© 2022 Elsevier. Received 7 June 2021, Revised 15 December 2021, Accepted 19 April 2022, Available online 20 June 2022, Version of Record 20 June 2022. The authors wish to thank Dr. Armin Tabandeh and Karl Eid for helpful discussions and suggestions.
Subject Keywords:Information Field Theory; Bayesian inference; Random fields; Markov Chain Monte Carlo; Non-Gaussian
Record Number:CaltechAUTHORS:20220705-346543000
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Official Citation:Aditya Pandey, Ashmeet Singh, Paolo Gardoni, A review of Information Field Theory for Bayesian inference of random fields, Structural Safety, Volume 99, 2022, 102225, ISSN 0167-4730,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115328
Deposited By: George Porter
Deposited On:07 Jul 2022 22:29
Last Modified:07 Jul 2022 22:29

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