Experiences with Bayesian Learning in a Real World Application

This paper reports about an application of Bayes' inferred neu(cid:173) ral network classifiers in the field of automatic sleep staging. The reason for using Bayesian learning for this task is two-fold. First, Bayesian inference is known to embody regularization automati(cid:173) cally. Second, a side effect of Bayesian learning leads to larger variance of network outputs in regions without training data. This results in well known moderation effects, which can be used to detect outliers.