Markov Chain Monte Carlo in Practice
Galin L. Jones, Qian Qin
Abstract
Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of probability distributions commonly encountered in modern applications. For MCMC simulation to produce reliable outcomes, it needs to generate observations representative of the target distribution, and it must be long enough so that the errors of Monte Carlo estimates are small. We review methods for assessing the reliability of the simulation effort, with an emphasis on those most useful in practically relevant settings. Both strengths and weaknesses of these methods are discussed. The methods are illustrated in several examples and in a detailed case study.
Topics & Concepts
Markov chain Monte CarloMonte Carlo methodComputer scienceStrengths and weaknessesReliability (semiconductor)Markov chainStatisticsMathematicsMachine learningEpistemologyPhysicsPhilosophyQuantum mechanicsPower (physics)Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian InferenceStatistical Methods and Inference