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An Introduction to MCMC for Machine Learning

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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APA Style

Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. (2003). An Introduction to MCMC for Machine Learning. Machine Learning, 50(1−2), 5–43.

MLA Style

Andrieu, C, et al. “An Introduction to MCMC for Machine Learning.” Machine Learning, vol. 50, no. 1−2, 2003, pp. 5–43.

Chicago Style

Andrieu, C, N de Freitas, A Doucet, and M Jordan. 2003. “An Introduction to MCMC for Machine Learning.” Machine Learning 50 (1−2): 5–43.
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Publisher copy:: 10.1023/A:1020281327116

Authors

+ Andrieu, C More by this author

Role:: Author

+ de Freitas, N More by this author

Role:: Author

+ Doucet, A More by this author

Role:: Author

+ Jordan, M More by this author

Role:: Author

Journal:: Machine Learning More from this journal
Volume:: 50
Issue:: 1−2
Pages:: 5-43
Publication date:: 2003-01-01
DOI:: 10.1023/A:1020281327116
ISSN:: 0885-6125

UUID:: uuid:68de6b0a-3673-4eea-8ce8-98c450020f46
Local pid:: cs:7533
Deposit date:: 2015-03-31
ARK identifier:: ark:/29072/ora_68de6b0a36734eea8ce898c450020f46

Terms of use

Copyright date:: 2003

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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