Group metropolis sampling

Luca Martino, Víctor Elvira, Gustau Camps-Valls

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

Abstract

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes as shown by means of numerical simulations.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages201-205
Number of pages5
Volume2017-January
ISBN (Electronic)9780992862671
ISBN (Print)978-1-5386-0751-0
DOIs
Publication statusPublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
Country/TerritoryGreece
CityKos
Period28/08/172/09/17

Keywords

  • Bayesian inference
  • Gaussian processes (GP)
  • Importance Sampling
  • Markov Chain Monte Carlo (MCMC)

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