Evaluating the RBM without integration using PDF projection

In this paper, we apply probability density function (PDF) projection to arrive at an exact closed-form expression for the marginal distribution of the visible data of a restricted Boltzmann machine (RBM) without requiring integrating over the distribution of the hidden variables or needing to know the partition function. We express the visible data marginal as a projected PDF based on a set of sufficient statistics. When a Gaussian mixture model (GMM) is used to estimate the PDF of the sufficient statistics, then we arrive at a combined RBM/GMM model that serves as a general-purpose PDF estimator and Bayesian classifier. The approach extends recusively to compute the input distribution of a multi-layer network. We demonstrate the method using a reduced subset of the MNIST handwritten character data set.