Description
Book SynopsisProviding a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistic
Table of ContentsPartial table of contents:
GENERALISATION PRINCIPLES AND LEARNING.
Structure of Statistical Learning Theory (V. Vapnik).
MML Inference of Predictive Trees, Graphs and Nets (C.Wallace).
Probabilistic Association and Denotation in Machine Learning ofNatural Language (P. Suppes & L. Liang).
CAUSATION AND MODEL SELECTION.
Causation, Action, and Counterfactuals (J. Pearl).
Efficient Estimation and Model Selection in Large Graphical Models(D. Wedelin).
BAYESIAN BELIEF NETWORKS AND HYBRID SYSTEMS.
Bayesian Belief Networks and Patient Treatment (L. Meshalkin &E. Tsybulkin).
DECISION-MAKING, OPTIMIZATION AND CLASSIFICATION.
Axioms for Dynamic Programming (P. Shenoy).
Extreme Values of Functionals Characterizing Stability ofStatistical Decisions (A. Nagaev).
Index.
