Záznam dôkazov metódy

Expectation Propagation

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.

Sources recorded, not reviewed

Zdrojový záznam

Citácie skopírované doslovne zo zdrojového záznamu metódy. Nevyplýva z nich žiadne overenie na úrovni tvrdenia.

Expectation Propagation for Approximate Bayesian Inference

Taxonomický záznam metódy · bayesian / bayesian

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. · URL
Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI]. · URL
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.) · ISBN 978-0387310732

Otvoriť celú metódu

Spracované tvrdenia

Tvrdenia uložené v registri dôkazov, každé s vlastným hodnotením.

Zatiaľ žiadne spracované tvrdenia

Tento pohľad nevymýšľa hodnotenie tvrdenia, ak register žiadne nemá.

Súvisiace metódy

Vygenerované z grafu metód a zobrazené ako vzťahy navrhnuté strojom – nevyplýva z nich žiadne tvrdenie o dôkaze.

Same method familyLaplace Approximationmachine-suggested · Relational suggestion, not evidence.Same method familyMCMCmachine-suggested · Relational suggestion, not evidence.Same method familyVariational Inferencemachine-suggested · Relational suggestion, not evidence.