An Introduction to MCMC for Machine Learning |
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Authors: | Andrieu Christophe de Freitas Nando Doucet Arnaud Jordan Michael I |
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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 |
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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. |
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Keywords: | Markov chain Monte Carlo MCMC sampling stochastic algorithms |
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