Group metropolis sampling

Luca Martino, Víctor Elvira, Gustau Camps-Valls

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

Abstract / Description of output

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.
Number of pages5
ISBN (Electronic)9780992862671
ISBN (Print)978-1-5386-0751-0
Publication statusPublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sept 2017


Conference25th European Signal Processing Conference, EUSIPCO 2017

Keywords / Materials (for Non-textual outputs)

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


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