6533b86dfe1ef96bd12c97ee
RESEARCH PRODUCT
Group Metropolis Sampling
Gustau Camps-vallsLuca MartinoVictor Elvirasubject
Computer scienceMonte Carlo methodMarkov processSlice samplingProbability density function02 engineering and technologyMultiple-try MetropolisBayesian inferenceMachine learningcomputer.software_genre01 natural sciencesHybrid Monte Carlo010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsComputingMilieux_MISCELLANEOUSMarkov chainbusiness.industryRejection samplingSampling (statistics)020206 networking & telecommunicationsMarkov chain Monte CarloMetropolis–Hastings algorithmsymbolsMonte Carlo method in statistical physicsMonte Carlo integrationArtificial intelligencebusinessParticle filter[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerAlgorithmImportance samplingMonte Carlo molecular modelingdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-08-30 |