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Most of the current books on stochastic control theory are written for students in mathematics or finance. This introduction is designed, however, for those interested in the relevance and applications of the theory's mathematical principles to economics. Therefore, mathematical methods are discussed intuitively and illustrated with economic examples. More importantly, mathematical concepts are introduced in language and terminology familiar to graduate students in economics.

Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.

Acceptable/Fair condition. Book is worn, but the pages are complete, and the text is legible. Has wear to binding and pages, may be ex-library. 1.05

Biological chemistry is a major frontier of inorganic chemistry. Three special volumes devoted to Metal Sites in Proteins and Models address the questions: how unusual ("entatic") are metal sites in metalloproteins and metalloenzymes compared to those in small coordination complexes? And if they are special, how do polypeptide chains and co-factors control this? The chapters deal with iron, with metal centres acting as Lewis acids, metals in phosphate enzymes, with vanadium, and with the wide variety of transition metal ions which act as redox centres. They illustrate in particular how the combined armoury of genetics and structure determination at the molecular level are providing unprecedented new tools for molecular engineering.

An updated guide to risk analysis and modeling Although risk was once seen as something that was both unpredictable and uncontrollable, the evolution of risk analysis tools and theories has changed the way we look at this important business element. In the Second Edition of Analyzing and Modeling Risk, expert Dr. Johnathan Mun provides up-to-date coverage of risk analysis as it is applied within the realms of business risk analysis and offers an intuitive feel of what risk looks like, as well as the different ways of quantifying it. This Second Edition provides professionals in all industries a more comprehensive guide on such key concepts as risk and return, the fundamentals of model building, Monte Carlo simulation, forecasting, time-series and regression analysis, optimization, real options, and more. Includes new examples, questions, and exercises as well as updates using Excel 2007 Book supported by author's proprietary risk analysis software found on the companion CD-ROM Offers both a qualitative and quantitative description of risk Filled with in-depth insights and practical advice, this reliable resource covers all of the essential tools and techniques that risk managers need to successfully conduct risk analysis. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Most of the current books on stochastic control theory are written for students in mathematics or finance. This introduction is designed, however, for those interested in the relevance and applications of the theory's mathematical principles to economics. Therefore, mathematical methods are discussed intuitively and illustrated with economic examples. More importantly, mathematical concepts are introduced in language and terminology familiar to graduate students in economics.

This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers.

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This 2nd volume of a 2-volume set explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. It introduces myopic optimal policies, Markov decision processes, generalizations of MDPs and related computational considerations, monotone optimal policies, and sequential games. 1984 edition. Includes 43 figures and 50 tables.

This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial engineering, production planning, and supply chain management. Each contribution is aimed at graduate students working in operations research, probability, and statistics.

Carefully written to cover all necessary background material from both linear and non-linear programming as well as probability theory, the book brings together the methods and techniques previously described in disparate sources. Topics include decision trees and dynamic programming, recourse problems, probabilistic constraints, preprocessing and network problems. Emphasises the appropriate use of the techniques described. Exercises are provided at the end of each chapter.

First printing of the 2nd edition; 417 pp., some minor age-toning to top edge of pages else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

This text provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations and martingale duality methods.

A framework for online decision making under uncertainty and time constraints, with online stochastic algorithms for implementing the framework, performance guarantees, and demonstrations of a variety of applications. Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge. This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

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This 2nd volume of a 2-volume set explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. It introduces myopic optimal policies, Markov decision processes, generalizations of MDPs and related computational considerations, monotone optimal policies, and sequential games. 1984 edition. Includes 43 figures and 50 tables.

Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.

This text provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations and martingale duality methods.