PDP network density estimation

Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, the authors propose a set of algorithms for training a multilayered perceptron as a parallel distributed processing network (PDP) to estimate various probability densities and serve as a Bayes classifier. The effectiveness of a PDP density estimator was measured in terms of the relative difference between the target probability density function and the network output representing the estimation. The classification rate of the PDP network was effectively identical to that of the Bayes classifier