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Metropolis Sampling

Abstract · Apr 15, 2017 12:15 ·

inthisdocument onemightbeinterestedintheestimation citeas systemsimulationandoptimizationproblems wefocusonthemetropolis stat-me cs-ce stat-co stat-ml

Arxiv Abstract

  • Luca Martino
  • Victor Elvira

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.

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