Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a...

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

This book is really the end product of over a decade of work, on and off, on diagrammatic reasoning in artificial intelligence (AI). In developing this book, I drew inspiration from a variety of sources: two experimental studies, the development of two prototype systems, an extensive literature review and analysis in AI,...

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Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...

Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from...

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...