C O D E

DigitDemo v1.0: This code package contains an interactive demo of Gibbs sampling, mean-field reconstruction and classification for restricted Boltzmann machines trained using four different inductive principles including stochastic maximum likelihood (SML), contrastive divergence (CD), ratio matching (RM) and pseudo-likelihood (PL). The MNIST data set was used as training data. See our AISTATS 2010 paper Inductive Principles for Restricted Boltzmann Machine Learning for details. Note that this demo uses trained models and does not include code for learning.
GGM-Gwishart v1.0: This MATLAB code package implements several marginal likelihood approximation methods for non-decomposable Gaussian Graphical Models (GGMs) under a G-Wishart prior. The methods are described in our NIPS 2009 paper. The approximations include BIC score, Laplace approximation, diagonal Hessian Laplace approximation, and the Monte Carlo estimator of Atay-Kayis and Massam.