Salīdzināt metodes

	Robust Bayesian model averaging	Brojesa modeļu vidējais svērums
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	Hoeting, Madigan, Raftery & Volinsky
Tips	Bayesian model selection and averaging	Bayesian model averaging
Pirmavots	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
Citi nosaukumi	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Saistītās	6	5
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.	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.