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| Expectation Propagation (EP)× | Catena di Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Famiglia | Bayesian methods | Bayesian methods |
| Anno di origine≠ | 2001 | — |
| Ideatore≠ | Thomas P. Minka | — |
| Tipo≠ | Approximate inference algorithm | Posterior sampling algorithm |
| Fonte seminale≠ | 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. link ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Alias≠ | EP, expectation propagation, EP algorithm, assumed-density filtering generalisation | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Correlati | 3 | 3 |
| Sintesi≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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