Bayesian model averaging

(1.2 hours to learn)

Summary

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.

Context

This concept has the prerequisites:

Core resources (read/watch one of the following)

-Free-

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)

-Free-

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