6533b82cfe1ef96bd128f587
RESEARCH PRODUCT
Recycling Gibbs sampling
Gustau Camps-vallsLuca MartinoVictor Elvirasubject
Computer scienceMonte Carlo methodSlice samplingMarkov processProbability density function02 engineering and technologyMachine 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_MISCELLANEOUSbusiness.industryRejection samplingEstimator020206 networking & telecommunicationsMarkov chain Monte CarlosymbolsArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerAlgorithmGibbs samplingdescription
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical comparisons show the validity of the approach.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-08-30 | 2017 25th European Signal Processing Conference (EUSIPCO) |