Contents - I. Generalisation principles and learning. 1. Structure of statistical learning theory . 2. Stochastic complexity: an introduction. 3. MML inference of predictive trees, graphs and nets. 4. Learning and reasoning as information compression by multiple alignment, unification and search. 5. Probailistic association and denotation in machine learning of natural language. II Causation and model selection 6. Causation, action and counterfactuals. 7. Another sematics for pearl's action calculus. 8. Efficient estimation and model selection in large graphical models. 9. T-Normal distribution on the bayesian belief networks. III Bayesian belief networks and hybrid systems 10. Bayesian belief networks with an application in specific case analysis. 11. Bayesian belief networks and patient treatment. 12. A higher order bayesian neural networks for classification and diagnosis. 13. Genetic algorithms applied to bayesian networks. IV Decision-making, optimization and classification. 14. Rationality, conditional independence and statistical models of competition. 15. Axioms for dynamic programming. 16. Mixture-model cluster analysis using the projection pursuit methods. 17. Parallel kn-nearest neighbour classifier for estimation of non-linear decision regions. 18. Extreme values of fucntional characterizing stability of statistical decisions