Comparison of Approximate Methods for handling Hyperparameters

摘要

I examine tow approximate methods for computational implementation of Bayesian hierarchical models, that is, models that include unknown hyperparameters such as regularization constants and noise levels. In the evidence framework, the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters. The op- timized hyperparameters are used to define a Gaussian approximation to the posterior distribution.