5.5 Model averaging

Model selection is attractive for its simplicity, but we might be missing information about uncertainty in our models. This is somewhat similar to calculating the full posterior and then just keeping the posterior mean; this can lead us to be overconfident about what we think we know.

An alternative is to select a single model but to report and analyze the different models together with the values of the calculated information criteria, their standard errors, and perhaps also the posterior predictive checks. It is important to put all these numbers and tests in the context of our problem so that we and our audience can get a better idea of the possible limitations and shortcomings of the models. For those working in academia, these elements can be used to add elements to the discussion section of a paper, presentation, thesis, etc. In industry, this can be useful for informing stakeholders about the advantages and limitations of models, predictions, and conclusions...