Published by Dunod, 1969
Seller: Ammareal, Morangis, France
Condition: Bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 1969. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Slight signs of wear on the cover. Edition 1969. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Hardcover. Condition: new. Hardcover. Mathematical methods in engineering are characterized by a wide range of techniques for approaching various problems. Moreover, completely different analysis techniques can be applied to the same problem, which is justified by the difference in specific applications. Therefore, the study of the analyses and solutions of specific problems leads the researcher to generate their own techniques for the analysis of similar problems continuously arising in the process of technical development. Computational Methods and Mathematical Modeling in Cyberphysics and Engineering Applications contains solutions to specific problems in current areas of computational engineering and cyberphysics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by ISTE Ltd and John Wiley & Sons Inc, London, 2020
ISBN 10: 1786305984 ISBN 13: 9781786305985
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Hardcover. Condition: new. Hardcover. This book is devoted to the system analysis of statistical experiments, determined by the averaged sums of sampling random variables. The dynamics of statistical experiments are given by difference stochastic equations with a speci?ed regression function of increments linear or nonlinear. The statistical experiments are studied by the sample volume increasing (N ??), as well as in discrete-continuous time by the number of stages increasing (k ??) for different conditions imposed on the regression function of increments. The proofs of limit theorems employ modern methods for the operator and martingale characterization of Markov processes, including singular perturbation methods. Furthermore, they justify the representation of a stationary Gaussian statistical experiment with the Markov property, as a stochastic difference equation solution, applying the theorem of normal correlation. The statistical hypotheses verification problem is formulated in the classification of evolutionary processes, which determine the dynamics of the predictable component. The method of stochastic approximation is used for classifying statistical experiments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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