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| Bayes-Faktor-Test× | Markov-Kette Monte Carlo (MCMC)× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1961 | — |
| Urheber≠ | Harold Jeffreys | — |
| Typ≠ | Bayesian hypothesis comparison | Posterior sampling algorithm |
| Wegweisende Quelle≠ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682 | 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 |
| Aliasnamen≠ | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | The Bayes factor test, formalised by Harold Jeffreys in 1961, is a Bayesian method for comparing two competing hypotheses. Rather than returning a binary reject/retain verdict, it produces a continuous ratio BF₁₀ that quantifies how much more (or less) probable the data are under the alternative hypothesis H₁ than under the null hypothesis H₀. | 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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