Сравнение на методи

	Марковски Монте Карло вериги (MCMC)	Байесовско осредняване на модели (Bayesian Model Averaging, BMA)	Байесов регресионен модел
Област	Бейсови методи	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods	Bayesian methods
Година на възникване	—	1999	—
Създател	—	Hoeting, Madigan, Raftery & Volinsky	—
Тип	Posterior sampling algorithm	Bayesian model averaging	Bayesian linear model
Основополагащ източник	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	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	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
Други названия	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	bayesian linear regression, probabilistic regression, bayesian regresyon
Свързани	3	5	2
Резюме	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.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.