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Finite Markov Chains And Algorithmic Applications
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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: In this 2002 book, the author develops the necessary background in probability theory and Markov chains then discusses important computing applications.
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.
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.
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.
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.
Download or read book Markov Chains written by Dean L. Isaacson and published by John Wiley & Sons. This book was released on 1976-03-05 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamental concepts of Markov chains; The classical approach to markov chains; The algebraic approach to Markov chains; Nonstationary Markov chains and the ergodic coeficient; Analysis of a markov chain on a computer; Continuous time Markov chains.
Book Synopsis General Irreducible Markov Chains and Non-Negative Operators by : Esa Nummelin
Download or read book General Irreducible Markov Chains and Non-Negative Operators written by Esa Nummelin and published by Cambridge University Press. This book was released on 2004-06-03 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the theory of general irreducible Markov chains and its connection to the Perron-Frobenius theory of nonnegative operators.
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.
Book Synopsis Markov Processes and Applications by : Etienne Pardoux
Download or read book Markov Processes and Applications written by Etienne Pardoux and published by John Wiley & Sons. This book was released on 2008-11-20 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This well-written book provides a clear and accessible treatment of the theory of discrete and continuous-time Markov chains, with an emphasis towards applications. The mathematical treatment is precise and rigorous without superfluous details, and the results are immediately illustrated in illuminating examples. This book will be extremely useful to anybody teaching a course on Markov processes." Jean-François Le Gall, Professor at Université de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields. After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance. Features include: The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes. An introduction to diffusion processes, mathematical finance and stochastic calculus. Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science. Numerous exercises and problems with solutions to most of them
Book Synopsis Handbook of Research on Applied AI for International Business and Marketing Applications by : Christiansen, Bryan
Download or read book Handbook of Research on Applied AI for International Business and Marketing Applications written by Christiansen, Bryan and published by IGI Global. This book was released on 2020-09-25 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students.
Download or read book Markov Chains written by Bruno Sericola and published by John Wiley & Sons. This book was released on 2013-08-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational research, computer science, communication networks and manufacturing systems. The success of Markov chains is mainly due to their simplicity of use, the large number of available theoretical results and the quality of algorithms developed for the numerical evaluation of many metrics of interest. The author presents the theory of both discrete-time and continuous-time homogeneous Markov chains. He carefully examines the explosion phenomenon, the Kolmogorov equations, the convergence to equilibrium and the passage time distributions to a state and to a subset of states. These results are applied to birth-and-death processes. He then proposes a detailed study of the uniformization technique by means of Banach algebra. This technique is used for the transient analysis of several queuing systems. Contents 1. Discrete-Time Markov Chains 2. Continuous-Time Markov Chains 3. Birth-and-Death Processes 4. Uniformization 5. Queues About the Authors Bruno Sericola is a Senior Research Scientist at Inria Rennes – Bretagne Atlantique in France. His main research activity is in performance evaluation of computer and communication systems, dependability analysis of fault-tolerant systems and stochastic models.
Book Synopsis Markov Chain Monte Carlo by : W. S. Kendall
Download or read book Markov Chain Monte Carlo written by W. S. Kendall and published by World Scientific. This book was released on 2005 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary program at the Institute for Mathematical Sciences, Singapore, which exploited the exciting ways in which MCMC spreads across different disciplines.
Book Synopsis Markov Chains and Mixing Times: Second Edition by : David A. Levin
Download or read book Markov Chains and Mixing Times: Second Edition written by David A. Levin and published by American Mathematical Soc.. This book was released on 2017-10-31 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the modern theory of Markov chains, whose goal is to determine the rate of convergence to the stationary distribution, as a function of state space size and geometry. This topic has important connections to combinatorics, statistical physics, and theoretical computer science. Many of the techniques presented originate in these disciplines. The central tools for estimating convergence times, including coupling, strong stationary times, and spectral methods, are developed. The authors discuss many examples, including card shuffling and the Ising model, from statistical mechanics, and present the connection of random walks to electrical networks and apply it to estimate hitting and cover times. The first edition has been used in courses in mathematics and computer science departments of numerous universities. The second edition features three new chapters (on monotone chains, the exclusion process, and stationary times) and also includes smaller additions and corrections throughout. Updated notes at the end of each chapter inform the reader of recent research developments.
Book Synopsis Finite Markov Chains by : John George Kemeny
Download or read book Finite Markov Chains written by John George Kemeny and published by . This book was released on 1969 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithms and Computation by : Toshihide Ibaraki
Download or read book Algorithms and Computation written by Toshihide Ibaraki and published by Springer. This book was released on 2003-11-24 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the 14th Annual International S- posium on Algorithms and Computation (ISAAC 2003), held in Kyoto, Japan, 15–17 December 2003. In the past, it was held in Tokyo (1990), Taipei (1991), Nagoya (1992), Hong Kong (1993), Beijing (1994), Cairns (1995), Osaka (1996), Singapore (1997), Taejon (1998), Chennai (1999), Taipei (2000), Christchurch (2001), and Vancouver (2002). ISAACisanannualinternationalsymposiumthatcoverstheverywiderange of topics in algorithms and computation. The main purpose of the symposium is to provide a forum for researchers working in algorithms and the theory of computation where they can exchange ideas in this active research community. In response to our call for papers, we received unexpectedly many subm- sions, 207 papers. The task of selecting the papers in this volume was done by our program committee and referees. After a thorough review process, the committee selected 73 papers. The selection was done on the basis of originality and relevance to the ?eld of algorithms and computation. We hope all accepted papers will eventally appear in scienti?c journals in more polished forms. The best paper award was given for “On the Geometric Dilation of Finite Point Sets” to Annette Ebbers-Baumann, Ansgar Grune ̈ and Rolf Klein. Two eminent invited speakers, Prof. Andrew Chi-Chih Yao of Princeton University and Prof. Takao Nishizeki of Tohoku University, contributed to this proceedings.
Download or read book Markov Chains written by Pierre Brémaud and published by Springer Nature. This book was released on 2020-05-23 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Primarily an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level, the primary objective of this book is to initiate students in the art of stochastic modelling. However it is motivated by significant applications and progressively brings the student to the borders of contemporary research. Examples are from a wide range of domains, including operations research and electrical engineering. Researchers and students in these areas as well as in physics, biology and the social sciences will find this book of interest.
Download or read book Markov Chains written by Wai-Ki Ching and published by Springer Science & Business Media. This book was released on 2013-03-27 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to have some basic knowledge of probability theory, Markov processes and matrix theory.
