Designing truncated priors for direct and inverse Bayesian problems

Fuente: arXiv
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Main Authors: Agapiou, Sergios, Mathé, Peter
Format: Preprint
Published: 2021
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author Agapiou, Sergios
Mathé, Peter
author_facet Agapiou, Sergios
Mathé, Peter
contents The Bayesian approach to inverse problems with functional unknowns, has received significant attention in recent years. An important component of the developing theory is the study of the asymptotic performance of the posterior distribution in the frequentist setting. The present paper contributes to the area of Bayesian inverse problems by formulating a posterior contraction theory for linear inverse problems, with truncated Gaussian series priors, and under general smoothness assumptions. Emphasis is on the intrinsic role of the truncation point both for the direct as well as for the inverse problem, which are related through the modulus of continuity as this was recently highlighted by Knapik and Salomond (2018).
format Preprint
id arxiv_https___arxiv_org_abs_2105_10254
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Designing truncated priors for direct and inverse Bayesian problems
Agapiou, Sergios
Mathé, Peter
Statistics Theory
62G20, 62C10, 62F15, 45Q05
The Bayesian approach to inverse problems with functional unknowns, has received significant attention in recent years. An important component of the developing theory is the study of the asymptotic performance of the posterior distribution in the frequentist setting. The present paper contributes to the area of Bayesian inverse problems by formulating a posterior contraction theory for linear inverse problems, with truncated Gaussian series priors, and under general smoothness assumptions. Emphasis is on the intrinsic role of the truncation point both for the direct as well as for the inverse problem, which are related through the modulus of continuity as this was recently highlighted by Knapik and Salomond (2018).
title Designing truncated priors for direct and inverse Bayesian problems
topic Statistics Theory
62G20, 62C10, 62F15, 45Q05
url https://arxiv.org/abs/2105.10254