Not much really new here. The most exciting is a script for validating probablility density function estimates. It works by first generating a simulated dataset using the metropolis method. The distribution of this simulated dataset should roughly match that of the training data. The training dataset is then split up into two or more subsets and PDF's estimated at each point in the simulated dataset and a cross correlation matrix calculated. Note that parameters used for testing (k, W) will need to be scaled up to match the full size of the training dataset--i.e. multiply by the number of divisions.