Variational sequential Monte Carlo

Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

Research output: Contribution to conferencePaperpeer-review


Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

Original languageEnglish (US)
Number of pages10
StatePublished - 2018
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018


Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CityPlaya Blanca, Lanzarote, Canary Islands

ASJC Scopus subject areas

  • Statistics and Probability
  • Artificial Intelligence


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