Finite Markov Chains and Algorithmic Applications

Finite Markov Chains and Algorithmic Applications

Author: Olle Häggström

Publisher: Cambridge University Press

Published: 2002-05-30

Total Pages: 132

ISBN-13: 9780521890014

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Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.


Book Synopsis Finite Markov Chains and Algorithmic Applications by : Olle Häggström

Download or read book Finite Markov Chains and Algorithmic Applications written by Olle Häggström and published by Cambridge University Press. This book was released on 2002-05-30 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.

Finite Markov Processes and Their Applications

Finite Markov Processes and Their Applications

Author: Marius Iosifescu

Publisher: Courier Corporation

Published: 2014-07-01

Total Pages: 305

ISBN-13: 0486150585

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A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu, vice president of the Romanian Academy and director of its Center for Mathematical Statistics, begins with a review of relevant aspects of probability theory and linear algebra. Experienced readers may start with the second chapter, a treatment of fundamental concepts of homogeneous finite Markov chain theory that offers examples of applicable models. The text advances to studies of two basic types of homogeneous finite Markov chains: absorbing and ergodic chains. A complete study of the general properties of homogeneous chains follows. Succeeding chapters examine the fundamental role of homogeneous infinite Markov chains in mathematical modeling employed in the fields of psychology and genetics; the basics of nonhomogeneous finite Markov chain theory; and a study of Markovian dependence in continuous time, which constitutes an elementary introduction to the study of continuous parameter stochastic processes.


Book Synopsis Finite Markov Processes and Their Applications by : Marius Iosifescu

Download or read book Finite Markov Processes and Their Applications written by Marius Iosifescu and published by Courier Corporation. This book was released on 2014-07-01 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu, vice president of the Romanian Academy and director of its Center for Mathematical Statistics, begins with a review of relevant aspects of probability theory and linear algebra. Experienced readers may start with the second chapter, a treatment of fundamental concepts of homogeneous finite Markov chain theory that offers examples of applicable models. The text advances to studies of two basic types of homogeneous finite Markov chains: absorbing and ergodic chains. A complete study of the general properties of homogeneous chains follows. Succeeding chapters examine the fundamental role of homogeneous infinite Markov chains in mathematical modeling employed in the fields of psychology and genetics; the basics of nonhomogeneous finite Markov chain theory; and a study of Markovian dependence in continuous time, which constitutes an elementary introduction to the study of continuous parameter stochastic processes.

Markov Chains: Models, Algorithms and Applications

Markov Chains: Models, Algorithms and Applications

Author: Wai-Ki Ching

Publisher: Springer Science & Business Media

Published: 2006-06-05

Total Pages: 212

ISBN-13: 038729337X

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Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.


Book Synopsis Markov Chains: Models, Algorithms and Applications by : Wai-Ki Ching

Download or read book Markov Chains: Models, Algorithms and Applications written by Wai-Ki Ching and published by Springer Science & Business Media. This book was released on 2006-06-05 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.

Markov Chains and Decision Processes for Engineers and Managers

Markov Chains and Decision Processes for Engineers and Managers

Author: Theodore J. Sheskin

Publisher: CRC Press

Published: 2016-04-19

Total Pages: 478

ISBN-13: 1420051121

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Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms u


Book Synopsis Markov Chains and Decision Processes for Engineers and Managers by : Theodore J. Sheskin

Download or read book Markov Chains and Decision Processes for Engineers and Managers written by Theodore J. Sheskin and published by CRC Press. This book was released on 2016-04-19 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms u

Finite Markov Chains and Their Applications

Finite Markov Chains and Their Applications

Author: Raymond Somers

Publisher:

Published: 1963

Total Pages: 0

ISBN-13:

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Book Synopsis Finite Markov Chains and Their Applications by : Raymond Somers

Download or read book Finite Markov Chains and Their Applications written by Raymond Somers and published by . This book was released on 1963 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Discrete-Time Markov Chains

Discrete-Time Markov Chains

Author: George Yin

Publisher: Springer Science & Business Media

Published: 2005

Total Pages: 372

ISBN-13: 9780387219486

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Focusing on discrete-time-scale Markov chains, the contents of this book are an outgrowth of some of the authors' recent research. The motivation stems from existing and emerging applications in optimization and control of complex hybrid Markovian systems in manufacturing, wireless communication, and financial engineering. Much effort in this book is devoted to designing system models arising from these applications, analyzing them via analytic and probabilistic techniques, and developing feasible computational algorithms so as to reduce the inherent complexity. This book presents results including asymptotic expansions of probability vectors, structural properties of occupation measures, exponential bounds, aggregation and decomposition and associated limit processes, and interface of discrete-time and continuous-time systems. One of the salient features is that it contains a diverse range of applications on filtering, estimation, control, optimization, and Markov decision processes, and financial engineering. This book will be an important reference for researchers in the areas of applied probability, control theory, operations research, as well as for practitioners who use optimization techniques. Part of the book can also be used in a graduate course of applied probability, stochastic processes, and applications.


Book Synopsis Discrete-Time Markov Chains by : George Yin

Download or read book Discrete-Time Markov Chains written by George Yin and published by Springer Science & Business Media. This book was released on 2005 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on discrete-time-scale Markov chains, the contents of this book are an outgrowth of some of the authors' recent research. The motivation stems from existing and emerging applications in optimization and control of complex hybrid Markovian systems in manufacturing, wireless communication, and financial engineering. Much effort in this book is devoted to designing system models arising from these applications, analyzing them via analytic and probabilistic techniques, and developing feasible computational algorithms so as to reduce the inherent complexity. This book presents results including asymptotic expansions of probability vectors, structural properties of occupation measures, exponential bounds, aggregation and decomposition and associated limit processes, and interface of discrete-time and continuous-time systems. One of the salient features is that it contains a diverse range of applications on filtering, estimation, control, optimization, and Markov decision processes, and financial engineering. This book will be an important reference for researchers in the areas of applied probability, control theory, operations research, as well as for practitioners who use optimization techniques. Part of the book can also be used in a graduate course of applied probability, stochastic processes, and applications.

Some Properties and Applications of Finite Markov Chains

Some Properties and Applications of Finite Markov Chains

Author: Kesava Chandrudu Vatti

Publisher:

Published: 1962

Total Pages: 80

ISBN-13:

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Book Synopsis Some Properties and Applications of Finite Markov Chains by : Kesava Chandrudu Vatti

Download or read book Some Properties and Applications of Finite Markov Chains written by Kesava Chandrudu Vatti and published by . This book was released on 1962 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Algorithms for Random Generation and Counting: A Markov Chain Approach

Algorithms for Random Generation and Counting: A Markov Chain Approach

Author: A. Sinclair

Publisher: Springer Science & Business Media

Published: 1993-02

Total Pages: 161

ISBN-13: 0817636587

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This monograph is a slightly revised version of my PhD thesis [86], com pleted in the Department of Computer Science at the University of Edin burgh in June 1988, with an additional chapter summarising more recent developments. Some of the material has appeared in the form of papers [50,88]. The underlying theme of the monograph is the study of two classical problems: counting the elements of a finite set of combinatorial structures, and generating them uniformly at random. In their exact form, these prob lems appear to be intractable for many important structures, so interest has focused on finding efficient randomised algorithms that solve them ap proxim~ly, with a small probability of error. For most natural structures the two problems are intimately connected at this level of approximation, so it is natural to study them together. At the heart of the monograph is a single algorithmic paradigm: sim ulate a Markov chain whose states are combinatorial structures and which converges to a known probability distribution over them. This technique has applications not only in combinatorial counting and generation, but also in several other areas such as statistical physics and combinatorial optimi sation. The efficiency of the technique in any application depends crucially on the rate of convergence of the Markov chain.


Book Synopsis Algorithms for Random Generation and Counting: A Markov Chain Approach by : A. Sinclair

Download or read book Algorithms for Random Generation and Counting: A Markov Chain Approach written by A. Sinclair and published by Springer Science & Business Media. This book was released on 1993-02 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is a slightly revised version of my PhD thesis [86], com pleted in the Department of Computer Science at the University of Edin burgh in June 1988, with an additional chapter summarising more recent developments. Some of the material has appeared in the form of papers [50,88]. The underlying theme of the monograph is the study of two classical problems: counting the elements of a finite set of combinatorial structures, and generating them uniformly at random. In their exact form, these prob lems appear to be intractable for many important structures, so interest has focused on finding efficient randomised algorithms that solve them ap proxim~ly, with a small probability of error. For most natural structures the two problems are intimately connected at this level of approximation, so it is natural to study them together. At the heart of the monograph is a single algorithmic paradigm: sim ulate a Markov chain whose states are combinatorial structures and which converges to a known probability distribution over them. This technique has applications not only in combinatorial counting and generation, but also in several other areas such as statistical physics and combinatorial optimi sation. The efficiency of the technique in any application depends crucially on the rate of convergence of the Markov chain.

Self-Learning Control of Finite Markov Chains

Self-Learning Control of Finite Markov Chains

Author: A.S. Poznyak

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 315

ISBN-13: 1482273276

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Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.


Book Synopsis Self-Learning Control of Finite Markov Chains by : A.S. Poznyak

Download or read book Self-Learning Control of Finite Markov Chains written by A.S. Poznyak and published by CRC Press. This book was released on 2018-10-03 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

Algorithms for Random Generation and Counting: A Markov Chain Approach

Algorithms for Random Generation and Counting: A Markov Chain Approach

Author: A. Sinclair

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 156

ISBN-13: 1461203236

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This monograph is a slightly revised version of my PhD thesis [86], com pleted in the Department of Computer Science at the University of Edin burgh in June 1988, with an additional chapter summarising more recent developments. Some of the material has appeared in the form of papers [50,88]. The underlying theme of the monograph is the study of two classical problems: counting the elements of a finite set of combinatorial structures, and generating them uniformly at random. In their exact form, these prob lems appear to be intractable for many important structures, so interest has focused on finding efficient randomised algorithms that solve them ap proxim~ly, with a small probability of error. For most natural structures the two problems are intimately connected at this level of approximation, so it is natural to study them together. At the heart of the monograph is a single algorithmic paradigm: sim ulate a Markov chain whose states are combinatorial structures and which converges to a known probability distribution over them. This technique has applications not only in combinatorial counting and generation, but also in several other areas such as statistical physics and combinatorial optimi sation. The efficiency of the technique in any application depends crucially on the rate of convergence of the Markov chain.


Book Synopsis Algorithms for Random Generation and Counting: A Markov Chain Approach by : A. Sinclair

Download or read book Algorithms for Random Generation and Counting: A Markov Chain Approach written by A. Sinclair and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is a slightly revised version of my PhD thesis [86], com pleted in the Department of Computer Science at the University of Edin burgh in June 1988, with an additional chapter summarising more recent developments. Some of the material has appeared in the form of papers [50,88]. The underlying theme of the monograph is the study of two classical problems: counting the elements of a finite set of combinatorial structures, and generating them uniformly at random. In their exact form, these prob lems appear to be intractable for many important structures, so interest has focused on finding efficient randomised algorithms that solve them ap proxim~ly, with a small probability of error. For most natural structures the two problems are intimately connected at this level of approximation, so it is natural to study them together. At the heart of the monograph is a single algorithmic paradigm: sim ulate a Markov chain whose states are combinatorial structures and which converges to a known probability distribution over them. This technique has applications not only in combinatorial counting and generation, but also in several other areas such as statistical physics and combinatorial optimi sation. The efficiency of the technique in any application depends crucially on the rate of convergence of the Markov chain.