# 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:

- Bayesian model comparison (Bayesian model averaging is based on the same ideas as Bayesian model comparison.)

## Core resources (read/watch one of the following)

## -Free-

→ Bayesian Model Selection and Model Averaging

## 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

## See also

- Reversible jump MCMC is a class of sampling algorithms often used for Bayesian model averaging.