Covers the concepts and main results of probability theory, from its fundamental principles to advanced applications. This book provides examples of practical problems such as the safety of a piece of engineering equipment or the inevitability of wrong conclusions in seemingly accurate medical tests for AIDS and cancer.
This book deals with Markov chains and Markov renewal processes (M/G/1 type). It discusses numerical difficulties which are apparently inherent in the classical analysis of a variety of stochastic models by methods of complex analysis.
Covering both theory and applications, this collection surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters.
Papers presented at a workshop held January 1990 (location unspecified) cover just about all aspects of solving Markov models numerically. There are papers on matrix generation techniques and generalized stochastic Petri nets; the computation of stationary distributions, including aggregation/disagg
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