Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| ANOVA ya Kibayesi× | Bayes Factor Test× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|---|
| Nyanja | Mbinu za Bayes | Mbinu za Bayes | Mbinu za Bayes |
| Familia | Bayesian methods | Bayesian methods | Bayesian methods |
| Mwaka wa asili≠ | 2012 | 1961 | — |
| Mwanzilishi≠ | Rouder, Morey, Speckman & Province | Harold Jeffreys | — |
| Aina≠ | Bayesian hypothesis test / group comparison | Bayesian hypothesis comparison | Posterior sampling algorithm |
| Chanzo asilia≠ | Rouder, J. N., Morey, R. D., Speckman, P. L. & Province, J. M. (2012). Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ | 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 |
| Majina mbadala≠ | bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Zinazohusiana≠ | 4 | 3 | 3 |
| Muhtasari≠ | Bayesian ANOVA, formalised by Rouder, Morey, Speckman and Province (2012), tests whether group means differ by quantifying the evidence for the alternative hypothesis relative to the null using the Bayes Factor (BF₁₀). Unlike classical ANOVA, it can also measure evidence in favour of the null hypothesis, making it equally informative when groups do not differ. | 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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