  We review the use of Bayesian Model Averaging in astrophysics. We first
introduce the statistical basis of Bayesian Model Selection and Model
Averaging. We discuss methods to calculate the model-averaged posteriors,
including Markov Chain Monte Carlo (MCMC), nested sampling, Population Monte
Carlo, and Reversible Jump MCMC. We then review some applications of Bayesian
Model Averaging in astrophysics, including measurements of the dark energy and
primordial power spectrum parameters in cosmology, cluster weak lensing and
Sunyaev-Zel'dovich effect data, estimating distances to Cepheids, and
classifying variable stars.
