Provable Bayesian Inference via Particle Mirror Descent
This addresses the problem of lacking rigorous guarantees in approximate Bayesian inference for practitioners, though it is incremental as it builds on existing methods like mirror descent and particle filtering.
The paper tackles the challenge of scalable and provable Bayesian inference by proposing Particle Mirror Descent (PMD), which achieves a convergence rate of O(1/√m) in KL-divergence with m particles and demonstrates competitive performance on large-scale datasets.
Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters. However, when Bayes' rule does not result in tractable closed-form, most approximate inference algorithms lack either scalability or rigorous guarantees. To tackle this challenge, we propose a simple yet provable algorithm, \emph{Particle Mirror Descent} (PMD), to iteratively approximate the posterior density. PMD is inspired by stochastic functional mirror descent where one descends in the density space using a small batch of data points at each iteration, and by particle filtering where one uses samples to approximate a function. We prove result of the first kind that, with $m$ particles, PMD provides a posterior density estimator that converges in terms of $KL$-divergence to the true posterior in rate $O(1/\sqrt{m})$. We demonstrate competitive empirical performances of PMD compared to several approximate inference algorithms in mixture models, logistic regression, sparse Gaussian processes and latent Dirichlet allocation on large scale datasets.