Download Markov Processes for Stochastic Modeling Ebook PDF

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
A Book

by Oliver Ibe

  • Publisher : Newnes
  • Release : 2013-05-22
  • Pages : 514
  • ISBN : 0124078397
  • Language : En, Es, Fr & De
GET BOOK

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

Student Solutions Manual for Markov Processes for Stochastic Modeling

Student Solutions Manual for Markov Processes for Stochastic Modeling
A Book

by Oliver Ibe

  • Publisher : Academic Press
  • Release : 2008-11-21
  • Pages : 120
  • ISBN : 0080952143
  • Language : En, Es, Fr & De
GET BOOK

Student Solutions Manual for Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
A Book

by Masaaki Kijima

  • Publisher : Springer
  • Release : 2013-12-19
  • Pages : 341
  • ISBN : 1489931325
  • Language : En, Es, Fr & De
GET BOOK

This book presents an algebraic development of the theory of countable state space Markov chains with discrete- and continuous-time parameters. A Markov chain is a stochastic process characterized by the Markov prop erty that the distribution of future depends only on the current state, not on the whole history. Despite its simple form of dependency, the Markov property has enabled us to develop a rich system of concepts and theorems and to derive many results that are useful in applications. In fact, the areas that can be modeled, with varying degrees of success, by Markov chains are vast and are still expanding. The aim of this book is a discussion of the time-dependent behavior, called the transient behavior, of Markov chains. From the practical point of view, when modeling a stochastic system by a Markov chain, there are many instances in which time-limiting results such as stationary distributions have no meaning. Or, even when the stationary distribution is of some importance, it is often dangerous to use the stationary result alone without knowing the transient behavior of the Markov chain. Not many books have paid much attention to this topic, despite its obvious importance.

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
A Book

by Mark Pinsky,Samuel Karlin

  • Publisher : Academic Press
  • Release : 2011
  • Pages : 563
  • ISBN : 0123814162
  • Language : En, Es, Fr & De
GET BOOK

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Fourth Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems. New to this edition: Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications Plentiful, completely updated problems Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers New chapters of stochastic differential equations and Brownian motion and related processes Additional sections on Martingale and Poisson process Realistic applications from a variety of disciplines integrated throughout the text Extensive end of chapter exercises sets, 250 with answers Chapter 1-9 of the new edition are identical to the previous edition New! Chapter 10 - Random Evolutions New! Chapter 11- Characteristic functions and Their Applications

Stochastic Modeling and Analysis of Telecom Networks

Stochastic Modeling and Analysis of Telecom Networks
A Book

by Laurent Decreusefond,Pascal Moyal

  • Publisher : John Wiley & Sons
  • Release : 2012-12-27
  • Pages : 387
  • ISBN : 1118563018
  • Language : En, Es, Fr & De
GET BOOK

This book addresses the stochastic modeling of telecommunicationnetworks, introducing the main mathematical tools for that purpose,such as Markov processes, real and spatial point processes andstochastic recursions, and presenting a wide list of results onstability, performances and comparison of systems. The authors propose a comprehensive mathematical construction ofthe foundations of stochastic network theory: Markov chains,continuous time Markov chains are extensively studied using anoriginal martingale-based approach. A complete presentation ofstochastic recursions from an ergodic theoretical perspective isalso provided, as well as spatial point processes. Using these basic tools, stability criteria, performance measuresand comparison principles are obtained for a wide class of models,from the canonical M/M/1 and G/G/1 queues to more sophisticatedsystems, including the current “hot topics” of spatialradio networking, OFDMA and real-time networks. Contents 1. Introduction. Part 1: Discrete-time Modeling 2. Stochastic Recursive Sequences. 3. Markov Chains. 4. Stationary Queues. 5. The M/GI/1 Queue. Part 2: Continuous-time Modeling 6. Poisson Process. 7. Markov Process. 8. Systems with Delay. 9. Loss Systems. Part 3: Spatial Modeling 10. Spatial Point Processes.

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
A Book

by Howard M. Taylor,Samuel Karlin

  • Publisher : Academic Press
  • Release : 2014-05-10
  • Pages : 578
  • ISBN : 1483220443
  • Language : En, Es, Fr & De
GET BOOK

An Introduction to Stochastic Modeling, Revised Edition provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Stochastic Modelling of Social Processes

Stochastic Modelling of Social Processes
A Book

by Andreas Diekmann,Peter Mitter

  • Publisher : Academic Press
  • Release : 2014-05-10
  • Pages : 352
  • ISBN : 1483266567
  • Language : En, Es, Fr & De
GET BOOK

Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.

Stochastic Modeling

Stochastic Modeling
A Book

by Nicolas Lanchier

  • Publisher : Springer
  • Release : 2017-01-27
  • Pages : 303
  • ISBN : 3319500384
  • Language : En, Es, Fr & De
GET BOOK

Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes. The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the gambler’s ruin chain, branching processes, symmetric random walks, and queueing systems. The third, more research-oriented part of the text, discusses special stochastic processes of interest in physics, biology, and sociology. Additional emphasis is placed on minimal models that have been used historically to develop new mathematical techniques in the field of stochastic processes: the logistic growth process, the Wright –Fisher model, Kingman’s coalescent, percolation models, the contact process, and the voter model. Further treatment of the material explains how these special processes are connected to each other from a modeling perspective as well as their simulation capabilities in C and MatlabTM.

Stochastic Models: Analysis and Applications

Stochastic Models: Analysis and Applications
A Book

by B. R. Bhat

  • Publisher : New Age International
  • Release : 2004
  • Pages : 408
  • ISBN : 9788122412284
  • Language : En, Es, Fr & De
GET BOOK

The Book Presents A Systematic Exposition Of The Basic Theory And Applications Of Stochastic Models.Emphasising The Modelling Rather Than Mathematical Aspects Of Stochastic Processes, The Book Bridges The Gap Between The Theory And Applications Of These Processes.The Basic Building Blocks Of Model Construction Are Explained In A Step By Step Manner, Starting From The Simplest Model Of Random Walk And Proceeding Gradually To More Complicated Models. Several Examples Are Given Throughout The Text To Illustrate Important Analytical Properties As Well As To Provide Applications.The Book Also Includes A Detailed Chapter On Inference For Stochastic Processes. This Chapter Highlights Some Of The Recent Developments In The Subject And Explains Them Through Illustrative Examples.An Important Feature Of The Book Is The Complements And Problems Section At The End Of Each Chapter Which Presents (I) Additional Properties Of The Model, (Ii) Extensions Of The Model, And (Iii) Applications Of The Model To Different Areas.With All These Features, This Is An Invaluable Text For Post-Graduate Students Of Statistics, Mathematics And Operation Research.

Basics of Applied Stochastic Processes

Basics of Applied Stochastic Processes
A Book

by Richard Serfozo

  • Publisher : Springer Science & Business Media
  • Release : 2009-01-24
  • Pages : 443
  • ISBN : 3540893326
  • Language : En, Es, Fr & De
GET BOOK

Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of large numbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerative increments. Extensive examples and exercises show how to formulate stochastic models of systems as functions of a system’s data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brownian approximations, and varied Markovian models. The technical level of the volume is between that of introductory texts that focus on highlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes.

Applied Stochastic System Modeling

Applied Stochastic System Modeling
A Book

by Shunji Osaki

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 269
  • ISBN : 3642846815
  • Language : En, Es, Fr & De
GET BOOK

This book was written for an introductory one-semester or two-quarter course in stochastic processes and their applications. The reader is assumed to have a basic knowledge of analysis and linear algebra at an undergraduate level. Stochastic models are applied in many fields such as engineering systems, physics, biology, operations research, business, economics, psychology, and linguistics. Stochastic modeling is one of the promising kinds of modeling in applied probability theory. This book is intended to introduce basic stochastic processes: Poisson pro cesses, renewal processes, discrete-time Markov chains, continuous-time Markov chains, and Markov-renewal processes. These basic processes are introduced from the viewpoint of elementary mathematics without going into rigorous treatments. This book also introduces applied stochastic system modeling such as reliability and queueing modeling. Chapters 1 and 2 deal with probability theory, which is basic and prerequisite to the following chapters. Many important concepts of probabilities, random variables, and probability distributions are introduced. Chapter 3 develops the Poisson process, which is one of the basic and im portant stochastic processes. Chapter 4 presents the renewal process. Renewal theoretic arguments are then used to analyze applied stochastic models. Chapter 5 develops discrete-time Markov chains. Following Chapter 5, Chapter 6 deals with continuous-time Markov chains. Continuous-time Markov chains have im portant applications to queueing models as seen in Chapter 9. A one-semester course or two-quarter course consists of a brief review of Chapters 1 and 2, fol lowed in order by Chapters 3 through 6.

Analytical and Stochastic Modeling Techniques and Applications

Analytical and Stochastic Modeling Techniques and Applications
15th International Conference, ASMTA 2008 Nicosia, Cyprus, June 4-6, 2008 Proceedings

by Khalid Al-Begain,Armin Heindl,Miklos Telek

  • Publisher : Springer Science & Business Media
  • Release : 2008-05-26
  • Pages : 323
  • ISBN : 354068980X
  • Language : En, Es, Fr & De
GET BOOK

This book constitutes the refereed proceedings of the 15th International Conference on Analytical and Stochastic Modeling Techniques and Applications, ASMTA 2008, held in Nicosia, Cyprus, in June 2008 in conjunction with ECMS 2008, the 22nd European Conference on Modeling and Simulation. The 22 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on traffic modeling, queueing systems, analytical methods and applications, distributions in stochastic modeling, queueing networks, simulation and model checking, as well as wireless networks.

Stochastic Models in Life Insurance

Stochastic Models in Life Insurance
A Book

by Michael Koller

  • Publisher : Springer Science & Business Media
  • Release : 2012-03-22
  • Pages : 219
  • ISBN : 3642284396
  • Language : En, Es, Fr & De
GET BOOK

The book provides a sound mathematical base for life insurance mathematics and applies the underlying concepts to concrete examples. Moreover the models presented make it possible to model life insurance policies by means of Markov chains. Two chapters covering ALM and abstract valuation concepts on the background of Solvency II complete this volume. Numerous examples and a parallel treatment of discrete and continuous approaches help the reader to implement the theory directly in practice.

Modeling and Analysis of Stochastic Systems

Modeling and Analysis of Stochastic Systems
A Book

by Vidyadhar G. Kulkarni

  • Publisher : CRC Press
  • Release : 1996-05-15
  • Pages : 634
  • ISBN : 9780412049910
  • Language : En, Es, Fr & De
GET BOOK

This practical text aims to enable students in engineering, business, operations research, public policy, and computer science to model and analyze stochastic systems. The major classes of useful stochastic processes - discrete and continuous time Markov chains, renewal processes, regenerative processes, and Markov regenerative processes - are presented, with an emphasis on modelling real-life situations with stochastic elements and analyzing the resulting stochastic model.

Elements of Stochastic Modelling

Elements of Stochastic Modelling
A Book

by K. A. Borovkov

  • Publisher : World Scientific
  • Release : 2003
  • Pages : 342
  • ISBN : 9789812383013
  • Language : En, Es, Fr & De
GET BOOK

This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments ? with indications as to why a particular result holds, and also how it is connected with other results ? and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.

Probability and Stochastic Modeling

Probability and Stochastic Modeling
A Book

by Vladimir I. Rotar

  • Publisher : CRC Press
  • Release : 2012-08-25
  • Pages : 508
  • ISBN : 1439872074
  • Language : En, Es, Fr & De
GET BOOK

A First Course in Probability with an Emphasis on Stochastic Modeling Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability texts, the book also focuses on increasingly important areas, such as martingales, classification of dependency structures, and risk evaluation. Numerous examples, exercises, and models using real-world data demonstrate the practical possibilities and restrictions of different approaches and help students grasp general concepts and theoretical results. The text is suitable for majors in mathematics and statistics as well as majors in computer science, economics, finance, and physics. The author offers two explicit options to teaching the material, which is reflected in "routes" designated by special "roadside" markers. The first route contains basic, self-contained material for a one-semester course. The second provides a more complete exposition for a two-semester course or self-study.

Stochastic Modeling

Stochastic Modeling
Analysis and Simulation

by Barry L. Nelson

  • Publisher : Courier Corporation
  • Release : 2012-10-11
  • Pages : 336
  • ISBN : 0486139948
  • Language : En, Es, Fr & De
GET BOOK

Coherent introduction to techniques also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Includes formulation of models, analysis, and interpretation of results. 1995 edition.

Stochastic Processes and Models

Stochastic Processes and Models
A Book

by David Stirzaker

  • Publisher : Oxford University Press, USA
  • Release : 2005
  • Pages : 331
  • ISBN : 9780198568148
  • Language : En, Es, Fr & De
GET BOOK

An introduction to simple stochastic processes and models, this text includes numerous exercises, problems and solutions, as well as covering key concepts and tools.

Statistical Topics and Stochastic Models for Dependent Data with Applications

Statistical Topics and Stochastic Models for Dependent Data with Applications
A Book

by Vlad Stefan Barbu,Nicolas Vergne

  • Publisher : John Wiley & Sons
  • Release : 2020-12-03
  • Pages : 288
  • ISBN : 1786306034
  • Language : En, Es, Fr & De
GET BOOK

This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.

A First Course in Stochastic Models

A First Course in Stochastic Models
A Book

by Henk C. Tijms

  • Publisher : John Wiley and Sons
  • Release : 2003-07-22
  • Pages : 448
  • ISBN : 0470864281
  • Language : En, Es, Fr & De
GET BOOK

The field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models. Emphasis is placed on establishing the theoretical foundations of the subject, thereby providing a framework in which the applications can be understood. Without this solid basis in theory no applications can be solved. Provides an introduction to the use of stochastic models through an integrated presentation of theory, algorithms and applications. Incorporates recent developments in computational probability. Includes a wide range of examples that illustrate the models and make the methods of solution clear. Features an abundance of motivating exercises that help the student learn how to apply the theory. Accessible to anyone with a basic knowledge of probability. A First Course in Stochastic Models is suitable for senior undergraduate and graduate students from computer science, engineering, statistics, operations resear ch, and any other discipline where stochastic modelling takes place. It stands out amongst other textbooks on the subject because of its integrated presentation of theory, algorithms and applications.