Multilayer perceptrons as nonlinear generative models for unsupervised learning: a Bayesian treatment

In this paper, multilayer perceptrons are used as nonlinear generative models. The problem of indeterminacy of the models is resolved using a recently developed Bayesian method, called ensemble learning. Using a Bayesian approach, models can be compared according to their probabilities. In simulations with artificial data, the network is able to find the underlying causes of the observations despite the strong nonlinearities of the data