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Handbook of Markov Decision Processes / Methods and Applications, Feinberg Eugene A., Shwartz Adam



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Автор: Feinberg Eugene A., Shwartz Adam
Название:  Handbook of Markov Decision Processes / Methods and Applications
Издательство: Springer
Классификация:
Бизнес и управление
Управление и методы управления
Дифферунциально и интегральное исчисление и математический анализ
Вероятность и статистика
Прикладная математика
Инженерная механика

ISBN: 0792374592
ISBN-13(EAN): 9780792374596
ISBN: 0-7923-7459-2
ISBN-13(EAN): 978-0-7923-7459-6
Обложка/Формат: Hardback
Страницы: 576
Вес: 0.984 кг.
Дата издания: 01.09.2001
Серия: International Series in Operations Research & Management Science
Язык: English
Иллюстрации: Biography
Размер: 24.33 x 16.61 x 3.20
Читательская аудитория: Postgraduate, research & scholarly
Подзаголовок: Methods and applications
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The theory of Markov Decision Processes - also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming - studies sequential optimization of discrete time stochastic systems. Fundamentally, this is a methodology that examines and analyzes a discrete-time stochastic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. Its objective is to select a good control policy. In real life, decisions that humans and computers make on all levels usually have two types of impacts: (i) they cost or save time, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view of future events. Markov Decision Processes (MDPs) model this paradigm and provide results on the structure and existence of good policies and on methods for their calculations. MDPs are attractive to many researchers because they are important both from the practical and the intellectual points of view. MDPs provide tools for the solution of important real-life problems. In particular, many business and engineering applications use MDP models. Analysis of various problems arising in MDPs leads to a large variety of interesting mathematical and computational problems. Accordingly, the Handbook of Markov Decision Processes is split into three parts: Part I deals with models with finite state and action spaces and Part II deals with infinite state problems, and Part III examines specific applications. Individual chapters are written by leading experts on the subject.
Дополнительное описание: Язык: eng
Оглавление: 1. Introduction; E.A. Feinberg, A. Shwartz. Part I: Finite State and Action Models. 2. Finite State and Action MDPs; L. Kallenberg. 3. Bias Optimality; M.E. Lewis, M.L. Puterman. 4. Singular Perturbations of Markov Chains and Decision Processes; K.E. Avrachenkov, et al. Part II: Infinite State Models. 5. Average Reward Optimization Theory for Denumerable State Spaces; L.I. Sennott. 6. Total Reward Criteria; E.A. Feinberg. 7. Mixed Criteria; E.A. Feinberg, A. Shwartz. 8. Blackwell Optimality; A. Hordijk, A.A. Yushkevich. 9. The Poisson Equation for Countable Markov Chains: Probabilistic Methods and Interpretations; A.M. Makowski, A. Shwartz. 10. Stability, Performance Evaluation, and Optimization; S.P. Meyn. 11. Convex Analytic Methods in Markov Decision Processes; V.S. Borkar. 12. The Linear Programming Approach; O. HernГЎndez-Lerma, J.B. Lasserre. 13. Invariant Gambling Problems and Markov Decision Processes; L.E. Dubins, et al. Part III: Applications. 14. Neuro-Dynamic Programming: Overview and Recent Trends; B. Van Roy. 15. Markov Decision Processes in Finance and Dynamic Options; M. SchГ¤l. 16. Applications of Markov Decision Processes in Communication Networks; E. Altman. 17. Water Reservoir Applications of Markov Decision Processes; B.F. Lamond, A. Boukhtouta. Index.





Markov Decision Processes: Discrete Stochastic Dynamic Programming

Автор: Martin L. Puterman
Название: Markov Decision Processes: Discrete Stochastic Dynamic Programming
ISBN: 0471727822 ISBN-13(EAN): 9780471727828
Издательство: Wiley
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Цена: 14207 р.
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Описание: The Wiley--Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers...The text is fairly self--contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." - Zentralblatt fur Mathematik "...it is of great value to advanced--level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic...Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up--to--date, unified, and rigorous tre " - Journal of the American Statistical Association

Semi-Markov Processes: Applications in System Reliability and Maintenance

Автор: Franciszek Grabski
Название: Semi-Markov Processes: Applications in System Reliability and Maintenance
ISBN: 0128005181 ISBN-13(EAN): 9780128005187
Издательство: Elsevier Science
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Цена: 12331 р.
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Описание: Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time semi-Markov processes and their applications in reliability and maintenance. The bookexplains how to construct semi-Markov models and discusses the different reliability parameters and characteristics that can be obtained from those models.The book is a useful resource for mathematicians, engineering practitioners, and PhD and MSc students who want to understand the basic concepts and results of semi-Markov process theory.

Diffusions, Markov Processes and Martingales

Автор: L. C. G. Rogers
Название: Diffusions, Markov Processes and Martingales
ISBN: 0521775930 ISBN-13(EAN): 9780521775939
Издательство: Cambridge Academ
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Цена: 6901 р.
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Описание: Now available in paperback, this celebrated book has been prepared with readers’ needs in mind, remaining a systematic treatment of the subject whilst retaining its vitality. The second volume follows on from the first, concentrating on stochastic integrals, stochastic differential equations, excursion theory and the general theory of processes. Much effort has gone into making these subjects as accessible as possible by providing many concrete examples that illustrate techniques of calculation, and by treating all topics from the ground up, starting from simple cases. Many of the examples and proofs are new; some important calculational techniques appeared for the first time in this book. Together with its companion volume, this book helps equip graduate students for research into a subject of great intrinsic interest and wide application in physics, biology, engineering, finance and computer science.

Partially Observed Markov Decision Processes

Автор: Krishnamurthy
Название: Partially Observed Markov Decision Processes
ISBN: 1107134609 ISBN-13(EAN): 9781107134607
Издательство: Cambridge Academ
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Цена: 8397 р.
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Описание: Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive decision making in multi-agent social learning where there is herding and data incest? And how can sophisticated radars and sensors adapt their sensing in real time?

Simulation-based algorithms for markov decision processes

Автор: Chang, Hyeong Soo Fu, Michael C. Hu, Jiaqiao Marcu
Название: Simulation-based algorithms for markov decision processes
ISBN: 1846286891 ISBN-13(EAN): 9781846286896
Издательство: Springer
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Цена: 6996 р.
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Описание: Often, real-world problems modeled by Markov decision processes (MDPs) are difficult to solve in practise because of the curse of dimensionality. In others, explicit specification of the MDP model parameters is not feasible, but simulation samples are available. This book provides many specific algorithms, and illustrative numerical examples.

Markov chains and decision processes for engineers and managers

Название: Markov chains and decision processes for engineers and managers
ISBN: 1420051113 ISBN-13(EAN): 9781420051117
Издательство: Taylor&Francis
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Цена: 11202 р.
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Описание: Presents an introduction to finite Markov chains and Markov decision processes, with applications in engineering and management. This book introduces discrete-time, finite-state Markov chains, and Markov decision processes. It describes both algorithms and applications, enabling students to understand the logical basis for the algorithms.

Continuous-Time Markov Decision Processes

Автор: Guo
Название: Continuous-Time Markov Decision Processes
ISBN: 3642025463 ISBN-13(EAN): 9783642025464
Издательство: Springer
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Цена: 10449 р.
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Описание: Theory and Applications. .

Markov random walks, markov additive processes and their applications

Название: Markov random walks, markov additive processes and their applications
ISBN: 9812793186 ISBN-13(EAN): 9789812793188
Издательство: World Scientific Publishing
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Цена: 11274 р.
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Описание: A collection of papers on Markov random walks and Markov additive processes and their applications. It provides the foundations of these stochastic processes underpinned by a theoretical framework based on Semiregenerative phenomena.

Probability and statistics by example markov chains - a primer in random processes and their applications

Автор: Suhov, Yuri Kelbert, Mark
Название: Probability and statistics by example markov chains - a primer in random processes and their applications
ISBN: 0521612349 ISBN-13(EAN): 9780521612340
Издательство: Cambridge Academ
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Цена: 7246 р.
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Описание: Probability and Statistics are as much about intuition and problem solving as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature. Since the subject is critical in many modern applications such as mathematical finance, quantitative management, telecommunications, signal processing, bioinformatics, as well as traditional ones such as insurance, social science andengineering, the authors have rectified deficiencies in traditional lecture-based methods by collecting together a wealth of exercises with complete solutions, adapted to needs and skills of students. Following on from the success of Probability and Statistics by Example: Basic Probability and Statistics, the authors here concentrate on random processes, particularly Markov processes, emphasising modelsrather than general constructions. Basic mathematical facts are supplied as and when they are needed andhistorical information is sprinkled throughout.

Competitive Markov Decision Processes

Автор: Filar
Название: Competitive Markov Decision Processes
ISBN: 0387948058 ISBN-13(EAN): 9780387948058
Издательство: Springer
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Цена: 14629 р.
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Описание: Competitive Markov decision processes are examined from the standpoints of modelling and optimization in this text. Topics covered include mathematical programming, summable stochastic games, average-reward stochastic games, and applications and special classes of stochastic games.

Markov Processes and Applications - Algorithms, Networks, Genome and Finance

Автор: Pardoux
Название: Markov Processes and Applications - Algorithms, Networks, Genome and Finance
ISBN: 0470772719 ISBN-13(EAN): 9780470772713
Издательство: Wiley
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Цена: 7450 р.
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Описание: The book presents a detailed mathematical introduction to the main classes of Markov processes together with their applications in various fields. It has been developed from a number of successful courses taught by the author to a wide-ranging student audience, including in Mathematicians, Engineers and Biologists.

Pseudo differential operators and markov processes, volume iii: markov processes and applications

Название: Pseudo differential operators and markov processes, volume iii: markov processes and applications
ISBN: 1860945686 ISBN-13(EAN): 9781860945687
Издательство: World Scientific Publishing
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Цена: 16220 р.
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Описание: This volume concentrates on how to construct a Markov process by starting with a suitable pseudo-differential operator. Feller processes, Hunt processes associated with Lp-sub-Markovian semigroups and processes constructed by using the Martingale problem are at the center of the considerations. The potential theory of these processes is further developed and applications are discussed.

Due to the non-locality of the generators, the processes are jump processes and their relations to Levy processes are investigated. Special emphasis is given to the symbol of a process, a notion which generalizes that of the characteristic exponent of a Levy process and provides a natural link to pseudo-differential operator theory.


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