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Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes - Application to Reliability, de Saporta


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Цена: 22010.00р.
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При оформлении заказа до: 2025-08-04
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Автор: de Saporta
Название:  Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes - Application to Reliability
ISBN: 9781848218390
Издательство: Wiley
Классификация:

ISBN-10: 1848218397
Обложка/Формат: Hardback
Страницы: 298
Вес: 0.59 кг.
Дата издания: 15.12.2015
Серия: Mathematics
Язык: English
Иллюстрации: Black & white illustrations
Размер: 241 x 167 x 22
Читательская аудитория: Professional & vocational
Ключевые слова: Mathematics
Основная тема: Numerical Methods
Подзаголовок: Application to reliability
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание:

Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today it is used to model a variety of complex systems in the fields of engineering, economics, management sciences, biology, Internet traffic, networks and many more. Yet, despite this, there is very little in the way of literature devoted to the development of numerical methods for PDMDs to solve problems of practical importance, or the computational control of PDMPs.

This book therefore presents a collection of mathematical tools that have been recently developed to tackle such problems. It begins by doing so through examples in several application domains such as reliability. The second part is devoted to the study and simulation of expectations of functionals of PDMPs. Finally, the third part introduces the development of numerical techniques for optimal control problems such as stopping and impulse control problems.




Advances in Stochastic and Deterministic Global Optimization

Автор: Panos M. Pardalos; Anatoly Zhigljavsky; Julius ?il
Название: Advances in Stochastic and Deterministic Global Optimization
ISBN: 3319299735 ISBN-13(EAN): 9783319299730
Издательство: Springer
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Цена: 15372.00 р.
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Описание: Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book.

This volume is dedicated to the 70th birthday of Antanas ?ilinskas who is a leading world expert in global optimization. Professor ?ilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.
Deterministic Global Optimization

Автор: Daniel Scholz
Название: Deterministic Global Optimization
ISBN: 1489995552 ISBN-13(EAN): 9781489995551
Издательство: Springer
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Цена: 16070.00 р.
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Описание: This book examines geometric branch-and-bound methods, such as in Lipschitzian optimization, d.c. programming and interval analysis, introduces a new concept for the rate of convergence and also analyzes several bounding operations reported in the literature.

Markov Processes, Gaussian Processes, and Local Times

Автор: Marcus
Название: Markov Processes, Gaussian Processes, and Local Times
ISBN: 1107403758 ISBN-13(EAN): 9781107403758
Издательство: Cambridge Academ
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Цена: 12038.00 р.
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Описание: Two foremost researchers present important advances in stochastic process theory by linking well-understood (Gaussian) and less well-understood (Markov) classes of processes. It builds to this material through `mini-courses` on the relevant ingredients, which assume only measure-theoretic probability. This original, readable 2006 book is for researchers and advanced graduate students.


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