Fused $L_{1/2}$ prior for large scale linear inverse problem with Gibbs bouncy particle sampler

TL;DR

This paper introduces a novel Bayesian sampling method using a fused $L_{1/2}$ prior and a Gibbs bouncy particle sampler to efficiently solve large scale linear inverse problems, demonstrating competitive results in tomography.

Contribution

It proposes a new fused $L_{1/2}$ prior and a Gibbs bouncy particle sampler to improve sampling efficiency for large scale inverse problems.

Findings

The Gibbs-BPS significantly reduces computational complexity.

The method achieves competitive accuracy in tomography applications.

The sampler converges reliably to the target distribution.

Abstract

In this paper, we study Bayesian approach for solving large scale linear inverse problems arising in various scientific and engineering fields. We propose a fused $L_{1/2}$ prior with edge-preserving and sparsity-promoting properties and show that it can be formulated as a Gaussian mixture Markov random field. Since the density function of this family of prior is neither log-concave nor Lipschitz, gradient-based Markov chain Monte Carlo methods can not be applied to sample the posterior. Thus, we present a Gibbs sampler in which all the conditional posteriors involved have closed form expressions. The Gibbs sampler works well for small size problems but it is computationally intractable for large scale problems due to the need for sample high dimensional Gaussian distribution. To reduce the computation burden, we construct a Gibbs bouncy particle sampler (Gibbs-BPS) based on a piecewise deterministic Markov process. This new sampler combines elements of Gibbs sampler with bouncy particle sampler and its computation complexity is an order of magnitude smaller. We show that the new sampler converges to the target distribution. With computed tomography examples, we demonstrate that the proposed method shows competitive performance with existing popular Bayesian methods and is highly efficient in large scale problems.