Bayesian model averaging

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In model selection, we typically select a single "best" model from a set of candidate models (based upon some selection criteria, such as an AIC score) and then use this model for prediction. Instead of selecting a single "best" model and using it for prediction, Bayesian Model Averaging BMA uses a weighted average of each model's individual prediction for the final predicted value, where the weight is the posterior probability of the model given the data.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Bayesian Model Selection and Model Averaging
Location: All sections except 6 and 9
Author: Larry Wasserman

Supplemental resources (the following are optional, but you may find them useful)


Bayesian Model Averaging: A Tutorial
Location: Sections 1-3 provide core material and sections 4-7 provide examples and further technical details
Authors: Jennifer A. Hoeting,David Madigan,Adrian E. Raftery,Chris T. Volinsky

See also