BIRS Workshop Lecture Videos
Calibrated Bayes factors for model comparison Xu, Xinyi
Bayes factor is a widely used tool for Bayesian hypothesis testing and model comparison. However, it can be greatly affected by the prior elicitation for the model parameters. When the prior information is weak, people often use proper priors with large variances, but Bayes factors under convenient diffuse priors can be very sensitive to the arbitrary diffuseness of the priors. In this work, we propose an innovative method called calibrated Bayes factor, which uses training samples to calibrate the prior distributions, so that they reach a certain concentration level before we compute Bayes factors. This method provides reliable and robust model preferences under various true models. It makes no assumption on model forms (parametric or nonparametric) or on the integrability of priors (proper or improper), so is applicable in a large variety of model comparison problems.
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