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Markov Decision Processes with Their Applications, Hu Qiying, Yue Wuyi



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Автор: Hu Qiying, Yue Wuyi
Название:  Markov Decision Processes with Their Applications
Издательство: Springer
Классификация:
Математика
Дифферунциально и интегральное исчисление и математический анализ
Вероятность и статистика
Оптимизация
Прикладная математика
Технология производства

ISBN: 0387369503
ISBN-13(EAN): 9780387369501
ISBN: 0-387-36950-3
ISBN-13(EAN): 978-0-387-36950-1
Обложка/Формат: Hardback
Страницы: 314
Вес: 0.644 кг.
Дата издания: 21.12.2007
Серия: Advances in mechanics and mathematics
Язык: English
Иллюстрации: 6 black & white illustrations, 3 black & white tab
Размер: 244 x 163 x 23
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multiperiod and in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. MDPs have been applied in many areas, such as communications, signal processing, artificial intelligence, stochastic scheduling and manufacturing systems, discrete event systems, management and economies.This book examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents three main topics: вЂў a new methodology for MDPs with discounted total reward criterion; вЂў transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; вЂў application of MDPs in stochastic environments, which greatly extends the area where MDPs can be applied.Each topic is used to study optimal control problems or other types of problems.
Дополнительное описание: Формат: 235x155
Илюстрации: 6
Круг читателей: Researchers in operations management, operations research, management, finance, and optimal control of discrete event systems
Ключевые слова:
Язык: eng





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.
Наличие на складе: Поставка под заказ.

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

An Introduction to Markov Processes

Автор: Stroock Daniel W.
Название: An Introduction to Markov Processes
ISBN: 3540234519 ISBN-13(EAN): 9783540234517
Издательство: Springer
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Цена: 6269 р.
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Описание: This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. A whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium.

Discrete-Time Markov Control Processes

Автор: Hernandez-Lerma
Название: Discrete-Time Markov Control Processes
ISBN: 0387945792 ISBN-13(EAN): 9780387945798
Издательство: Springer
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Цена: 13584 р.
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Описание: This text provides a unified treatment of some recent theoretical developments on Markov control processes. Interest is mainly confined to MCPs with Borel state and control spaces, and possibly unbound costs and non-compact control constraint sets.

Further Topics on Discrete-Time Markov Control Processes

Автор: Hernandez-Lerma
Название: Further Topics on Discrete-Time Markov Control Processes
ISBN: 0387986944 ISBN-13(EAN): 9780387986944
Издательство: Springer
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Цена: 14629 р.
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Описание: Devotes to a systematic exposition of some developments in the theory of discrete-time Markov control processes.

Markov Processes for Stochastic Modeling,

Автор: Oliver Ibe
Название: Markov Processes for Stochastic Modeling,
ISBN: 0123744512 ISBN-13(EAN): 9780123744517
Издательство: Elsevier Science
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Цена: 6892 р.
Наличие на складе: Невозможна поставка.

Описание: Markov processes are used to model systems with limited memory. This book discusses topics such as Markovian queuing system, continuous-time random walk, correlated random walk, Brownian motion, diffusion processes, hidden Markov models, Markov random fields, Markov point processes and Markov chain Monte Carlo.

Nonlinear Markov Processes and Kinetic Equations

Автор: Kolokoltsov
Название: Nonlinear Markov Processes and Kinetic Equations
ISBN: 0521111846 ISBN-13(EAN): 9780521111843
Издательство: Cambridge Academ
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Цена: 7706 р.
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Описание: A nonlinear Markov evolution is a dynamical system generated by a measure-valued ordinary differential equation with the specific feature of preserving positivity. This feature distinguishes it from general vector-valued differential equations and yields a natural link with probability, both in interpreting results and in the tools of analysis. This brilliant book, the first devoted to the area, develops this interplay between probability and analysis. After systematically presenting both analytic and probabilistic techniques, the author uses probability to obtain deeper insight into nonlinear dynamics, and analysis to tackle difficult problems in the description of random and chaotic behavior. The book addresses the most fundamental questions in the theory of nonlinear Markov processes: existence, uniqueness, constructions, approximation schemes, regularity, law of large numbers and probabilistic interpretations. Its careful exposition makes the book accessible to researchers and graduate students in stochastic and functional analysis with applications to mathematical physics and systems biology.

Measure-Valued Branching Markov Processes

Автор: Li
Название: Measure-Valued Branching Markov Processes
ISBN: 3642150039 ISBN-13(EAN): 9783642150036
Издательство: Springer
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Цена: 8359 р.
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Описание: Measure-valued branching processes arise as high density limits of branching particle systems. The Dawson-Watanabe superprocess is a special class of those. The author constructs superprocesses with Borel right underlying motions and general branching mechanisms and shows the existence of their Borel right realizations. He then uses transformations to derive the existence and regularity of several different forms of the superprocesses. This treatment simplifies the constructions and gives useful perspectives. Martingale problems of superprocesses are discussed under Feller type assumptions. The most important feature of the book is the systematic treatment of immigration superprocesses and generalized Ornstein--Uhlenbeck processes based on skew convolution semigroups. The volume addresses researchers in measure-valued processes, branching processes, stochastic analysis, biological and genetic models, and graduate students in probability theory and stochastic processes.

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 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.

Handbook of Markov Decision Processes / Methods and Applications

Автор: Feinberg Eugene A., Shwartz Adam
Название: Handbook of Markov Decision Processes / Methods and Applications
ISBN: 0792374592 ISBN-13(EAN): 9780792374596
Издательство: Springer
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Цена: 31246 р.
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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.

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.

Examples in Markov Decision Processes

Автор: Piunovskiy A B
Название: Examples in Markov Decision Processes
ISBN: 1848167938 ISBN-13(EAN): 9781848167933
Издательство: World Scientific Publishing
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Цена: 9433 р.
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Описание: This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was to collect them together in one reference book which should be considered as a complement to existing monographs on Markov decision processes.The book is self-contained and unified in presentation.The main theoretical statements and constructions are provided, and particular examples can be read independently of others. Examples in Markov Decision Processes is an essential source of reference for mathematicians and all those who apply the optimal control theory to practical purposes. When studying or using mathematical methods, the researcher must understand what can happen if some of the conditions imposed in rigorous theorems are not satisfied. Many examples confirming the importance of such conditions were published in different journal articles which are often difficult to find. This book brings together examples based upon such sources, along with several new ones. In addition, it indicates the areas where Markov decision processes can be used. Active researchers can refer to this book on applicability of mathematical methods and theorems. It is also suitable reading for graduate and research students where they will better understand the theory.


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