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This book presents methods for solving multidimensional statistical problems. Covering both theory and applications, it emphasizes inverse problems, multidimensional modeling, random fields and hierarchical methods.

This book reviews well-known methods for reducing the dimensionality of numerical databases as well as recent developments in nonlinear dimensionality reduction. All are described from a unifying point of view, which highlights their respective strengths and shortcomings.

This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. The theory and methods presented are illustrated through more than 140 examples.

This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.

This volume covers all the important topics concerning support vector machines. It provides a unique in-depth treatment of both fundamental and recent material on SVMs that, up to now, has been scattered in the literature.

A collection of applied papers on time series, appearing here for the first time in English. The applications are primarily found in engineering and the physical sciences.

Afterword of 2006

The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. The book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms.