পদ্ধতির তুলনা করুন

	দৃঢ় বায়েসীয় মডেল গড়করণ (Robust Bayesian Model Averaging)	মার্কভ চেইন মন্টি কার্লো (MCMC)
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	1999–2012	—
প্রবর্তক	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	—
ধরন	Bayesian model selection and averaging	Posterior sampling algorithm
মৌলিক উৎস	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
অপর নাম	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
সম্পর্কিত	6	3
সারসংক্ষেপ	Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.	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.