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This book combines a model reduction technique with an efficient parametrization scheme for the purpose of solving a class of complex and computationally expensive simulation-based problems involving finite element models.
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This edited volume comprises invited contributions from world-renowned researchers in the subject of stochastic control and inverse... Læs mere
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This book will cover the issues related to optimization of engineering problems using heuristic techniques, with an industrial outlook. It... Læs mere
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This book provides a unified approach for the study of constrained Markov decision processes
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This three-chapter volume concerns the distributions of certain functionals of Lévy processes. The first chapter, by Makoto Maejima, surveys representations... Læs mere
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This comprehensive introduction to stochastic partial differential equations incorporates the effects of randomness into real-world models, offering... Læs mere
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This book investigates some simple stochastic models and shows how there is an interplay between the models and the science underlying the problem. It combines stochastic modeling and statistical inference in point processes, Markov random fields, and hidden Markov models.
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This book describes the sojourn time distribution for its usefulness in analyzing the distribution of the supremum of the sample function of the process. It is concerned with the distribution of the supremum of a continuous parameter stochastic process.
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Aims to widen the understanding of stochastic dynamic choice and equilibrium models. The book offers a simplified exposition of the theory of Brownian motion and its regulation,... Læs mere
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Featuring recent advances in probability, statistics, and stochastic processes, this new textbook presents Probability and Statistics, and an introduction to Stochastic Processes.