Controlled Markov Chains With Risk Sensitive Average Cost Criterion Phd
Download Controlled Markov Chains With Risk Sensitive Average Cost Criterion Phd full books in PDF, epub, and Kindle. Read online free Controlled Markov Chains With Risk Sensitive Average Cost Criterion Phd ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Selected Topics on Continuous-time Controlled Markov Chains and Markov Games
| Author | : Tomás Prieto-Rumeau |
| Publisher | : World Scientific |
| Total Pages | : 292 |
| Release | : 2012 |
| Genre | : Mathematics |
| ISBN | : 1848168489 |
This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where two decision-makers (or players) try to optimize their own objective function. Both decision-making processes appear in a large number of applications in economics, operations research, engineering, and computer science, among other areas.An extensive, self-contained, up-to-date analysis of basic optimality criteria (such as discounted and average reward), and advanced optimality criteria (e.g., bias, overtaking, sensitive discount, and Blackwell optimality) is presented. A particular emphasis is made on the application of the results herein: algorithmic and computational issues are discussed, and applications to population models and epidemic processes are shown.This book is addressed to students and researchers in the fields of stochastic control and stochastic games. Moreover, it could be of interest also to undergraduate and beginning graduate students because the reader is not supposed to have a high mathematical background: a working knowledge of calculus, linear algebra, probability, and continuous-time Markov chains should suffice to understand the contents of the book.
Markov Processes and Controlled Markov Chains
| Author | : Zhenting Hou |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 501 |
| Release | : 2013-12-01 |
| Genre | : Mathematics |
| ISBN | : 146130265X |
The general theory of stochastic processes and the more specialized theory of Markov processes evolved enormously in the second half of the last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas. However, this may be the first volume dedicated to highlighting these synergies and, almost certainly, it is the first volume that emphasizes the contributions of the vibrant and growing Chinese school of probability. The chapters that appear in this book reflect both the maturity and the vitality of modern day Markov processes and controlled Markov chains. They also will provide an opportunity to trace the connections that have emerged between the work done by members of the Chinese school of probability and the work done by the European, US, Central and South American and Asian scholars.
Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains
| Author | : Xi-Ren Cao |
| Publisher | : Springer Nature |
| Total Pages | : 120 |
| Release | : 2020-09-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3030566781 |
This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It develops new insights and new methodologies for systems in which concepts such as stationarity, ergodicity, periodicity and connectivity do not apply. This brief introduces the novel concept of confluencity and applies a relative optimization approach. It develops a comprehensive theory for optimization of the long-run average of time-nonhomogeneous Markov chains. The book shows that confluencity is the most fundamental concept in optimization, and that relative optimization is more suitable for treating the systems under consideration than standard ideas of dynamic programming. Using confluencity and relative optimization, the author classifies states as confluent or branching and shows how the under-selectivity issue of the long-run average can be easily addressed, multi-class optimization implemented, and Nth biases and Blackwell optimality conditions derived. These results are presented in a book for the first time and so may enhance the understanding of optimization and motivate new research ideas in the area.
On the Risk Sensitive Optimality Criteria for Markov Decision Processes
| Author | : Stanford University. Department of Operations Research |
| Publisher | : |
| Total Pages | : 28 |
| Release | : 1975 |
| Genre | : |
| ISBN | : |
Discrete dynamic programming models with an exponential utility function are studied with respect to the asymptotic behavior of the dynamic programming recursion for the expected utility. Preliminary results on maximizing the asymptotic growth of the expected utility in the class of stationary policies are presented. Under the condition that there exists a stationary 'optimal' policy with an irreducible, aperiodic transition probability matrix, some nice limiting properties for the maximum expected utilities are established. Moreover, it is shown how to generate a monotonic sequence of lower and upper bounds on the maximum growth rate of the expected utility. Under certain additional assumptions it is possible to extend the obtained results to Markov decision processes with a denumerable state space.
Markov Decision Processes
| Author | : Martin L. Puterman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 544 |
| Release | : 2014-08-28 |
| Genre | : Mathematics |
| ISBN | : 1118625870 |
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 treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association
Hidden Markov Models
| Author | : Robert J Elliott |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 374 |
| Release | : 2008-09-27 |
| Genre | : Science |
| ISBN | : 0387848541 |
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.
