مقایسهٔ روش‌ها

	زنجیره مارکوف مونت کارلو (MCMC)	تحلیل واریانس یک‌طرفه
حوزه	بیزی	آمار
خانواده	Bayesian methods	Hypothesis test
سال پیدایش	—	1925
پدیدآور	—	Ronald A. Fisher
نوع	Posterior sampling algorithm	Parametric mean comparison
منبع بنیادین	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	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
نام‌های دیگر	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
مرتبط	3	4
خلاصه	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.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.