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An introduction to MCMC for machine learning.
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“This implies that any composition or mixture of these operators also leaves invariant, see, e.g., [38].”
From Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons (2011) · cites this paper as [38]
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Metadata
- 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.
- Venue
- Machine Learning
- Published
- 2003-01
- Volume
- 50
- Issue
- 1-2
- Pages
- 5-43
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- 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
- 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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