Langevin-based Strategy for Efficient Proposal Adaptation in Population Monte Carlo

Victor Elvira, Emilie Chouzenoux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Population Monte Carlo (PMC) algorithms are a family of adaptive importance sampling (AIS) methods for approximating integrals in Bayesian inference. In this paper, we propose a novel PMC algorithm that combines recent advances in the AIS and the optimization literatures. In such a way, the proposal densities are adapted according to the past weighted samples via a local resampling that preserves the diversity, but we also exploit the geometry of the targeted distribution. A scaled Langevin strategy with Newton-based scaling metric is retained for this purpose, allowing to adapt jointly the means and the covariances of the proposals, without needing to tune any extra parameter. The performance of the proposed technique is clearly superior in two numerical examples at the cost of a reasonable computational complexity increment.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5077-5081
Number of pages5
Volume2019-May
ISBN (Electronic)978-1-4799-8131-1
ISBN (Print)978-1-4799-8132-8
DOIs
Publication statusPublished - 1 May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Importance sampling
  • Langevin dynamics
  • Monte Carlo methods
  • Newton algorithm
  • population Monte Carlo
  • stochastic optimization

Fingerprint

Dive into the research topics of 'Langevin-based Strategy for Efficient Proposal Adaptation in Population Monte Carlo'. Together they form a unique fingerprint.

Cite this