Added Distributionsfor use in Clustering(Mixture Modelling),Function Models, RegressionTrees, Segmentation, and mixed Bayesian Networks in Inductive Programming1.2

Inductive programming is a machine learning paradigm combining functional programming (FP) with the information theoretic criterion, Minimum Message Length (MML). IP 1.2 now includes the Geometric and Poisson distributions over non-negative integers, and Student's t-Distribution over continuous values, as well as the Multinomial and Normal (Gaussian) distributions from before. All of these can be used with IP's model-transformation operators, and structure-learning algorithms including clustering (mixture-models), classification- (decision-) trees and other regressions, and mixed Bayesian networks, provided only that the types match between each corresponding component Model, transformation, structured model, and variable discrete, continuous, sequence, multivariate, and so on.