Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals

TL;DR

This paper introduces an adaptive importance sampling method for Bayesian inference of nonlinear multi-output models and their covariance matrices, improving estimation accuracy and efficiency.

Contribution

The paper proposes a novel adaptive importance sampling scheme called ATAIS for joint Bayesian inference of model parameters and covariance matrices in nonlinear multi-output problems.

Findings

ATAIS effectively estimates model parameters and covariance matrices.

Numerical examples demonstrate improved inference accuracy.

The method offers a flexible approach for uncertainty quantification.

Abstract

In this paper we address the problem of performing Bayesian inference for the parameters of a nonlinear multi-output model and the covariance matrix of the different output signals. We propose an adaptive importance sampling (AIS) scheme for multivariate Bayesian inversion problems, which is based in two main ideas: the variables of interest are split in two blocks and the inference takes advantage of known analytical optimization formulas. We estimate both the unknown parameters of the multivariate non-linear model and the covariance matrix of the noise. In the first part of the proposed inference scheme, a novel AIS technique called adaptive target adaptive importance sampling (ATAIS) is designed, which alternates iteratively between an IS technique over the parameters of the non-linear model and a frequentist approach for the covariance matrix of the noise. In the second part of the proposed inference scheme, a prior density over the covariance matrix is considered and the cloud of samples obtained by ATAIS are recycled and re-weighted to obtain a complete Bayesian study over the model parameters and covariance matrix. ATAIS is the main contribution of the work. Additionally, the inverted layered importance sampling (ILIS) is presented as a possible compelling algorithm (but based on a conceptually simpler idea). Different numerical examples show the benefits of the proposed approaches