The DOI matches its Crossref record; 171 of the 171 references it cites were found in a registry or an index.
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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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
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Sequential Monte Carlo Methods in Practice
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Self-citationSampling-based approaches to calculating marginal densities
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- Gilks, W. R., &; Berzuini, C. (1998). Monte Carlo inference for dynamic Bayesian models. Unpublished. Medical Research Council, Cambridge, UK.
- Kam, A. H. (2000). A general multiscale scheme for unsupervised image segmentation. Ph.D. Thesis, Department of Engineering, Cambridge University, Cambridge, UK.
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Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494218 · Cited by 6,195
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,049
Incorporating non-local information into information extraction systems by Gibbs sampling
Finkel, J. R., Grenager, T., & Manning, C. D. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219885 · Cited by 3,028
ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
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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