Uporedite metode

	Metropolis-Hastings za poređenje modela	MCMC za poređenje modela
Oblast	Bajesovska statistika	Bajesovska statistika
Porodica	Bayesian methods	Bayesian methods
Godina nastanka	1970 (extended 1995)	1995
Tvorac	W. K. Hastings (1970); extended for model comparison by P. J. Green (1995)	Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling)
Tip	MCMC-based model comparison	Bayesian computational method
Temeljni izvor	Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.	Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732.
Drugi nazivi	MH model comparison, Metropolis-Hastings Bayes factor estimation, reversible-jump Metropolis-Hastings, MH model selection	reversible-jump MCMC, RJMCMC, marginal likelihood estimation via MCMC, Bayesian model selection via MCMC
Srodne	4	5
Sažetak	Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversible-jump MCMC by Green (1995) — handles models of different dimensionalities within a single sampler.	MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully Bayesian model selection and averaging.