Salīdzināt metodes

	Brojesa modeļu vidējais svērums	Pastiprināšana
Nozare	Bajesa metodes	Mašīnmācīšanās
Saime	Bayesian methods	Machine learning
Izcelsmes gads	1999	1990–1997
Autors	Hoeting, Madigan, Raftery & Volinsky	Schapire, R. E.; Freund, Y.
Tips	Bayesian model averaging	Sequential ensemble (iterative reweighting)
Pirmavots	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Citi nosaukumi	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Saistītās	5	6
Kopsavilkums	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.