Eecient Approaches to Gaussian Process Classiication
We present three simple approximations for the calculation of the posterior mean in Gaussian Process classiication. The rst two methods are related to mean eld ideas known in Statistical Physics. The third approach is based on Bayesian online approach which was motivated by recent results in the Statistical Mechanics of Neural Networks. We present simulation results showing: 1. that the mean eld Bayesian evidence may be used for hyperparameter tuning and 2. that the online approach may achieve a low training error fast.