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Introduction to Stochastic Dynamic Programming

随机动态规划

动态规划

stochastic

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“Introduction to Stochastic Dynamic Programming”介绍了随机动态规划的理论、应用、方法论等一系列。本书由学术泰斗 Sheldon Ross 编写，是学习随机动态规划的极佳教材。另外，本书的扫描质量很高，已经经过 OCR，支持文本搜索

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This is a volume in

PROBABILITY AND MATHEMATICAL STATISTICS

A Series of Monographs and Textbooks

Editors: Z. W. Bimbaum and E. Lukacs

A complete list of titles in this series appears at the end of this volume.

f { ) L 5 2 , S 5 ?

Introduction to

Stochastic Dynamic

Programming

Sheldon Ross

University of California

Berkeley, California

A C A D E M IC P R E S S

A Subsidiary of Harcourt Brace Jovanovich, Publishers

New York London

Paris San Diego San Francisco Sao Paulo Sydney Tokyo Toronto

NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR

TRANSMITTED IN ANY FORM OR BY ANY MEANS, ELECTRONIC

OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY

INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT

PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS, INC.

Ill Fifth Avenue, New York, New York 10003

United Kingdom Edition published by

ACADEMIC PRESS, INC. (LONDON) LTD.

24/28 Oval Road, London NW1 7DX

Library of Congress Cataloging in Publication Data

Ross, Sheldon M.

Introduction to stochastic dynamic programming.

(Probability and mathematical statistics)

Includes bibliographies and index.

1. Dynamic programming. 2. Stochastic programming.

I. Title. II. Series .

T57.83.R67 1382 513 - 7 103 82-18l63

ISBN 0-12-598*420-0

PRINTED IN THE UNITED STATES OF AMERICA

83 84 85 86

987654321

To Celine

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hunanhouchen

2018-09-27

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翻译成英文。基于两阶段随机动态规划

My answer would be: Based on two-stage stochastic dynamic programming, I can help you with your translation needs.

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Stochastic Learning and Optimization; A SENSITIVITY-BASED APPROACH Xi-Ren Cao Hong Kong University of Science and Technology

Stochastic Network Optimization with Application to Communication and Queueing S

本文介绍了动态网络的分析、控制和优化的现代理论。发展了李雅普诺夫漂移和李雅普诺夫优化的数学技术，证明了在一般随机系统中，时间平均的约束优化是可行的。重点是通信和排队系统，包括具有时变信道、移动性和随机到达流量的无线网络。一个简单的漂移-加-惩罚框架用于优化时间平均，如吞吐量、吞吐量-效用、功率和失真。提供了显式的性能延迟权衡，以说明接近最优的代价。这一理论也适用于运筹学和经济学的问题，在运筹学和经

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McGraw-Hil - Probability Random Variables and Stochastic Processes - Papoulis - 2002 答案

[Mourad.Barkat].Signal.Detection.And.Estimation.2nd.Ed.[Artech.House].2005

This book provides an overview and introduction to signal detection and estimation. The book contains numerous examples solved in detail. Since some material on signal detection could be very complex

robert g. gallager stochastic processes引用

Robert G. Gallager是一位出色的电信工程学家和数学家，他在随机过程研究领域有着广泛的贡献。在他的经典著作《随机过程》（Stochastic Processes）中，他对随机过程的基本概念进行了详细的解释，并通过一系列有趣的例子来阐述这些概念的实际应用。在这本书中，Gallager讨论了马尔可夫过程、泊松过程、随机游走等常见的随机过程，并详细讲解了随机过程的状态转移概率、平稳过程

stochastic processes

随机过程（stochastic processes）是指一类随机变量的集合，这些随机变量的取值是随机的，并且随时间的推移而变化。随机过程可以用来描述许多自然现象和社会现象，如股票价格、天气变化、人口增长等。在数学和统计学中，随机过程是一个重要的研究领域，它涉及到概率论、随机分析、马尔可夫过程等多个分支学科。

A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals

这是一个非常有趣的问题。我们可以使用循环分析这种非线性方法来区分噪声污染的确定性混沌动力学和时间相关或不相关的随机波动。在有限的时间序列中，这种方法可以有效地识别出随机和确定性的波动，从而为多个领域的科学技术提供支持。此外，对于确定性信号，量化混沌行为可能具有基本重要性，因为据认为，一些系统的混沌特性扮演着重要的功能角色，为更好地理解和/或控制产生信号的物理机制打开了大门。

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Introduction to Stochastic Dynamic Programming

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Introduction to Stochastic Dynamic Programming

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