De-randomizing MCMC dynamics with the diffusion Stein operator

• URL: http://arxiv.org/abs/2110.03768v1
• Date: Thu, 7 Oct 2021 19:59:46 GMT
• Title: De-randomizing MCMC dynamics with the diffusion Stein operator
• Authors: Zheyang Shen, Markus Heinonen, Samuel Kaski
• Abstract summary: Approximate Bayesian inference estimates descriptors of an intractable target distribution. We propose de-randomized kernel-based particle samplers to all diffusion-based samplers known as MCMC dynamics.
• Score: 21.815713258703575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions. For example, Langevin dynamics (LD) extracts asymptotically exact samples from a diffusion process because the time evolution of its marginal distributions constitutes a curve that minimizes the KL-divergence via steepest descent in the Wasserstein space. Parallel to LD, Stein variational gradient descent (SVGD) similarly minimizes the KL, albeit endowed with a novel Stein-Wasserstein distance, by deterministically transporting a set of particle samples, thus de-randomizes the stochastic diffusion process. We propose de-randomized kernel-based particle samplers to all diffusion-based samplers known as MCMC dynamics. Following previous work in interpreting MCMC dynamics, we equip the Stein-Wasserstein space with a fiber-Riemannian Poisson structure, with the capacity of characterizing a fiber-gradient Hamiltonian flow that simulates MCMC dynamics. Such dynamics discretizes into generalized SVGD (GSVGD), a Stein-type deterministic particle sampler, with particle updates coinciding with applying the diffusion Stein operator to a kernel function. We demonstrate empirically that GSVGD can de-randomize complex MCMC dynamics, which combine the advantages of auxiliary momentum variables and Riemannian structure, while maintaining the high sample quality from an interacting particle system.

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