পদ্ধতির তুলনা করুন

	দৃঢ় বায়েসীয় মডেল গড়করণ (Robust Bayesian Model Averaging)	ভ্যারিয়েশনাল ইনফারেন্স (Variational Inference)
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	1999–2012	1999
প্রবর্তক	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	Jordan, Ghahramani, Jaakkola & Saul
ধরন	Bayesian model selection and averaging	Approximate Bayesian inference
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.	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
অপর নাম	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	VI, variational Bayes, VB, mean-field variational inference
সম্পর্কিত	6	4
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.	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.