Forventningsudbredelse (EP)
Forventningsudbredelse (EP) er en deterministisk algoritme for beskedudveksling til approksimativ posterior inferens i Bayesianske modeller, introduceret af Thomas P. Minka ved UAI 2001. Den iterativt raffinerer et sæt lokale approksimative faktorer – hver trukket fra eksponentialfamilien – så deres produkt tæt matcher den sande intrakterbare posterior, hvilket opnår højere nøjagtighed end mean-field variationsinferens på mange sandsynlighedsbaserede maskinlæringsopgaver.
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Method map
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Kilder
- 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 ↗
- Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI]. link ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.) ISBN: 978-0387310732
Sådan citerer du denne side
ScholarGate. (2026, June 3). Expectation Propagation for Approximate Bayesian Inference. ScholarGate. https://scholargate.app/da/bayesian/expectation-propagation
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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Laplace ApproximationBayesiansk↔ compare
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