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 IIIexamines specific applications. Individual chapters are written by leading experts on the subject.

The first book to address timing considerations in testing and assessment. Offers actionable strategies built from cutting-edge research made possible through advances in technology-based assessments. The comprehensive approach of this book, including both theoretical and practical information, will position it as the definitive resource on the subject within the market.

The first book to address timing considerations in testing and assessment. Offers actionable strategies built from cutting-edge research made possible through advances in technology-based assessments. The comprehensive approach of this book, including both theoretical and practical information, will position it as the definitive resource on the subject within the market.

Thermodynamic degradation science is a new and exciting discipline. This book merges the science of physics of failure with thermodynamics and shows how degradation modeling is improved and enhanced when using thermodynamic principles. The author also goes beyond the traditional physics of failure methods and highlights the importance of having new tools such as Mesoscopic noise degradation measurements for prognostics of complex systems, and a conjugate work approach to solving physics of failure problems with accelerated testing applications. Key features: Demonstrates how the thermodynamics energy approach uncovers key degradation models and their application to accelerated testing. Demonstrates how thermodynamic degradation models accounts for cumulative stress environments, effect statistical reliability distributions, and are key for reliability test planning. Provides coverage of the four types of Physics of Failure processes describing aging: Thermal Activation Processes, Forced Aging, Diffusion, and complex combinations of these. Coverage of numerous key topics including: aging laws; Cumulative Accelerated Stress Test (CAST) Plans; cumulative entropy fatigue damage; reliability statistics and environmental degradation and pollution. Thermodynamic Degradation Science: Physics of Failure, Accelerated Testing, Fatigue and Reliability Applications is essential reading for reliability, cumulative fatigue, and physics of failure engineers as well as students on courses which include thermodynamic engineering and/or physics of failure coverage.

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic 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. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, 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 offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation."

Nachdruck des Originals von 1915. Segelfahrten und Walfischjagden durch die S dsee, vom Steuermann erz hlt.