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An Introduction to MCMC for Machine Learning
Authors:Andrieu  Christophe  de Freitas  Nando  Doucet  Arnaud  Jordan  Michael I
Affiliation:(1) Department of Mathematics, Statistics Group, University of Bristol, University Walk, Bristol, BS8 1TW, UK;(2) Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada;(3) Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria, 3052, Australia;(4) Departments of Computer Science and Statistics, University of California at Berkeley, 387 Soda Hall, Berkeley, CA 94720-1776, USA
Abstract:This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.
Keywords:Markov chain Monte Carlo  MCMC  sampling  stochastic algorithms
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