6533b854fe1ef96bd12af66e

RESEARCH PRODUCT

Metropolis Sampling

Luca MartinoVictor Elvira

subject

FOS: Computer and information sciencesMachine Learning (stat.ML)020206 networking & telecommunications02 engineering and technologyStatistics - Computation01 natural sciencesStatistics::ComputationMethodology (stat.ME)010104 statistics & probabilityStatistics - Machine Learning0202 electrical engineering electronic engineering information engineering0101 mathematicsComputation (stat.CO)Statistics - Methodology

description

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis-Hastings (MH) sampler, which can be considered as the atom of the MCMC techniques, introducing the basic notions and different properties. We describe in details all the elements involved in the MH algorithm and the most relevant variants. Several improvements and recent extensions proposed in the literature are also briefly discussed, providing a quick but exhaustive overview of the current Metropolis-based sampling's world.

yearjournalcountryeditionlanguage
2017-04-15Wiley StatsRef: Statistics Reference Online
https://doi.org/10.1002/9781118445112.stat07951