Bayesian Analysis with Python - Second Edition

Model averaging

Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then only keeping the mean of the posterior; we may become overconfident about what we really know. One alternative is to perform model selection but report and discuss the different models, along with the computed information criteria values, their standard error values, and perhaps also the posterior predictive checks. It is important to put all of these numbers and tests in the context of our problem so that we and our audience can have a better feel of the possible limitations and shortcomings of the models. If you are in the academic world, ...