Bayesian Inference for Initial Heat States with Gaussian Series Priors

TL;DR

This paper develops a Bayesian method using Gaussian series priors to recover initial heat states from interior measurements, providing theoretical guarantees and practical implementation for inverse heat problems.

Contribution

It introduces a Bayesian approach with Gaussian series priors for inverse heat problems, including theoretical guarantees and a numerical implementation.

Findings

Asymptotic performance guarantees for the Bayesian estimators.

Explicit posterior distributions facilitate inference.

Numerical simulations demonstrate the method's effectiveness.

Abstract

We consider the statistical linear inverse problem of recovering the unknown initial heat state from noisy interior measurements over an inhomogeneous domain of the solution to the heat equation at a fixed time instant. We employ nonparametric Bayesian procedures with Gaussian series priors defined on the Dirichlet-Laplacian eigenbasis, yielding convenient conjugate posterior distributions with explicit expressions for posterior inference. We review recent theoretical results that provide asymptotic performance guarantees (in the large sample size limit) for the resulting posterior-based point estimation and uncertainty quantification. We further provide an implementation of the approach, and illustrate it via a numerical simulation study.