Bayesian methods
Expectation Propagation (EP)
Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks.
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Sources
- Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann. DOI: 10.5555/647235.720257 ↗
- Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI]. DOI: 10.48550/arXiv.1301.2294 ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.) ISBN: 978-0387310732