6533b7defe1ef96bd1276739
RESEARCH PRODUCT
A new strategy for effective learning in population Monte Carlo sampling
Luca MartinoVictor ElviraMonica F. Bugallosubject
Mathematical optimizationComputer scienceMonte Carlo methodInference02 engineering and technology01 natural sciencesHybrid Monte Carlo010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringQuasi-Monte Carlo methodKinetic Monte Carlo0101 mathematicsComputingMilieux_MISCELLANEOUSbusiness.industryRejection samplingSampling (statistics)020206 networking & telecommunicationsMarkov chain Monte CarloDynamic Monte Carlo methodsymbolsMonte Carlo integrationMonte Carlo method in statistical physicsArtificial intelligenceParticle filterbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMonte Carlo molecular modelingdescription
In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-11-06 |