Sample continuation in Bayesian hierarchical model via variational inference

TL;DR

This paper introduces a particle-based variational inference method using SVGD and Birth-Death sampling to analyze how posterior distributions in hierarchical Bayesian models change with prior parameters, aiding sensitivity analysis and solution continuation.

Contribution

It develops an augmented SVGD approach with Birth-Death sampling to track and discover modes in intractable, multimodal posteriors as prior parameters vary.

Findings

Enables continuous tracking of posterior mode evolution.

Facilitates discovery of new modes during prior parameter shifts.

Provides a robust method for sensitivity analysis in hierarchical models.

Abstract

Posterior distributions arising in ill-posed Bayesian inverse problems are often both analytically intractable and highly sensitive to parameters of the chosen prior family. We aim to understand the sensitivity of intractable posterior distributions to changes in prior assumptions by tracking how a sample representation of the posterior changes as the prior parameters change. This enables sensitivity analysis for small perturbations in the prior, providing insights into the robustness of the posterior estimates under minor changes in assumptions. It also allows solution continuation when dealing with significant alterations in prior beliefs, facilitating a comprehensive understanding of how large shifts in assumptions affect the posterior distribution. We focus on a class of non-conjugate hierarchical models tailored to encourage sparsity in linear inverse problems. The specific hierarchical model of interest is chosen since it is parameterized by a small number of shape parameters, and includes most classical sparsity promoting priors as special cases. As the shape parameters change, the posterior can transition continuously from a tractable unimodal distribution to an intractable multimodal distribution. To track the change in the posterior, we adopt particle based variational inference methods, specifically Stein Variational Gradient Descent (SVGD). SVGD iteratively updates a set of samples to minimize the KL-divergence away from a desired target distribution. We augment SVGD by Birth-Death sampling, which can efficiently exchange mass between separated modes, while simultaneously optimizing the kernel bandwidth used to derive the SVGD update. This method enables the discovery of new modes by tracing the modes as they branch out of a simpler, unimodal posterior, derived within the same family of priors.