This is a new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. It presents a thorough introduction to state-of-the-art solution and analysis algorithms.
With contributions from leaders in the field, this volume assesses the main issues in the experimental analysis of algorithms, examines their developmental cycle, and demonstrates how to configure and tune algorithms with advanced experimental techniques.
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 focuses on discrete time modeling and illustrates that queueing systems encountered in real life can be set up as a Markov chain. This feature is unique because the models are set in such a way that matrix-analytic methods are used to analyze them.
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.
This book provides a strong fundamental background in statistics and probability through simulation exercises. R software code is utilized throughout for assessment and interpretation of data and data mining exercises.