Provable Bayesian inference via particle mirror descent

Bo Dai, Niao He, Hanjun Dai, Le Song

Research output: Contribution to conferencePaperpeer-review


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, 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/√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.

Original languageEnglish (US)
Number of pages10
StatePublished - 2016
Externally publishedYes
Event19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 - Cadiz, Spain
Duration: May 9 2016May 11 2016


Conference19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability


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