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| [REQUIRES TRANSLATION]× | Mārkova ķēžu Montekarlo (MCMC)× | |
|---|---|---|
| Nozare | Bajesa metodes | Bajesa metodes |
| Saime | Bayesian methods | Bayesian methods |
| Izcelsmes gads≠ | 1961 | — |
| Autors≠ | Harold Jeffreys | — |
| Tips≠ | Bayesian hypothesis comparison | Posterior sampling algorithm |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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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