Cited by 2,451Markov Chains and Monte Carlo MethodsGaussian Processes and Bayesian InferenceBayesian Modeling and Causal Inference

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An introduction to MCMC for machine learning.

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Andrieu, Christophe, de Freitas, Nando, Doucet, Arnaud, Jordan, Michael I.·2003·Machine Learning

DOI 10.1023/a:1020281327116

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Authors: Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I.
Affiliations: University of Bristol (GB)
University of California, Berkeley (US)
Corresponding author: Andrieu, Christophe
Jordan, Michael I.
ORCID: Andrieu, Christophe
Jordan, Michael I.
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 2003-01
Volume: 50
Issue: 1-2
Pages: 5-43
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1020281327116
OpenAlex: W2135194391
Cited by: 2,451
References: 166
Citations by year: 2026: 61 · 2025: 81 · 2024: 92 · 2023: 118 · 2022: 141 · 2021: 176 · 2020: 170 · 2019: 175 · 2018: 160 · 2017: 148 · 2016: 129 · 2015: 128 · 2014: 122 · 2013: 102 · 2012: 121
FWCI: 36.33
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Machine Learning (publishedVersion)
Explore Bristol Research (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: Markov chain Monte Carlo, Monte Carlo methods, probabilistic machine learning, MCMC simulation, sampling methods

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