Salīdzināt metodes

	Robust Bayesian model averaging	Variacionālā secinājumi
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1999–2012	1999
Autors	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	Jordan, Ghahramani, Jaakkola & Saul
Tips	Bayesian model selection and averaging	Approximate Bayesian inference
Pirmavots	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.
Citi nosaukumi	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	VI, variational Bayes, VB, mean-field variational inference
Saistītās	6	4
Kopsavilkums	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.