Methoden vergleichen

	Bayesian Model Averaging mit Messfehlern	Markov-Kette Monte Carlo (MCMC)
Fachgebiet	Bayes-Statistik	Bayes-Statistik
Familie	Bayesian methods	Bayesian methods
Entstehungsjahr	1999–2006	—
Urheber	Hoeting, Madigan, Raftery, Volinsky (BMA); Carroll, Stefanski and colleagues (ME correction)	—
Typ	Bayesian ensemble model with covariate error correction	Posterior sampling algorithm
Wegweisende Quelle	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417.	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
Aliasnamen	BMA-ME, BMA with errors-in-variables, Bayesian model averaging errors-in-covariates, measurement error BMA	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Verwandt	3	3
Zusammenfassung	Bayesian model averaging with measurement error (BMA-ME) combines two probabilistic ideas: it averages predictions across competing regression models weighted by each model's posterior probability, while simultaneously accounting for the fact that one or more predictors are observed with random error rather than exactly. The result is a posterior that propagates both model uncertainty and covariate measurement noise into every inference and prediction.	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.