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Markov Processes For Stochastic Modeling

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Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling Book
Author : Oliver Ibe
Publisher : Newnes
Release : 2013-05-22
ISBN : 0124078397
Language : En, Es, Fr & De

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Book Description :

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.

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling Book
Author : Masaaki Kijima
Publisher : Springer
Release : 2013-12-19
ISBN : 1489931325
Language : En, Es, Fr & De

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Book Description :

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.

Markov processes for stochastic modeling

Markov processes for stochastic modeling Book
Author : Oliver C. Ibe
Publisher : Unknown
Release : 2013
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Markov processes for stochastic modeling book written by Oliver C. Ibe, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Markov Processes in Stochastic Modeling of TransportPhenomena

Markov Processes in Stochastic Modeling of TransportPhenomena Book
Author : Timo Gottschalk
Publisher : VDM Publishing
Release : 2009-04-01
ISBN : 9783838105680
Language : En, Es, Fr & De

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Book Description :

The present work discusses the development of mathematical theory in order to satisfy the need for rigorous and applicable modeling of transport phenomena in chemical engineering science. An underlying background in applications and examples are common to all the different following topics. The first object of investigation is Danckwerts' law. It states that the expected residence time of a particle in a processing vessel with steady and constant in- and outflow is given by the volume of the vessel divided by the in-/outflowrate. Its implementation for discrete Markov chains and onedimensional diffusion processes is shown. Therefore relations of the theory of strongly continuous semigroups and their generators to diffusion processes are presented and used. Furthermore multiphase processes are introduced and characterized. A limit theorem for these multiphase processes is formulated and proved. Finally a heterogeneous stochastic model for transport in slugging fluidized bed reactors is illustrated.

From Application to Theory Markov Processes in Stochastic Modeling of Transport

From Application to Theory  Markov Processes in Stochastic Modeling of Transport Book
Author : Timo Gottschalk
Publisher : Unknown
Release : 2007
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download From Application to Theory Markov Processes in Stochastic Modeling of Transport book written by Timo Gottschalk, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Elements of Stochastic Modelling

Elements of Stochastic Modelling Book
Author : K. A. Borovkov
Publisher : World Scientific
Release : 2003
ISBN : 9789812383013
Language : En, Es, Fr & De

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Book Description :

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.

Stochastic Modeling and Analysis of Telecom Networks

Stochastic Modeling and Analysis of Telecom Networks Book
Author : Laurent Decreusefond,Pascal Moyal
Publisher : John Wiley & Sons
Release : 2012-12-27
ISBN : 1118563018
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Mark Pinsky,Samuel Karlin
Publisher : Academic Press
Release : 2011
ISBN : 0123814162
Language : En, Es, Fr & De

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Book Description :

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

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling Book
Author : Howard M. Taylor,Samuel Karlin
Publisher : Academic Press
Release : 2014-05-10
ISBN : 1483220443
Language : En, Es, Fr & De

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Book Description :

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 Modeling

Stochastic Modeling Book
Author : Barry L. Nelson
Publisher : Courier Corporation
Release : 2012-10-11
ISBN : 0486139948
Language : En, Es, Fr & De

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Book Description :

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 Models Analysis and Applications

Stochastic Models  Analysis and Applications Book
Author : B. R. Bhat
Publisher : New Age International
Release : 2004
ISBN : 9788122412284
Language : En, Es, Fr & De

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Book Description :

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.

Applied Stochastic System Modeling

Applied Stochastic System Modeling Book
Author : Shunji Osaki
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 3642846815
Language : En, Es, Fr & De

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Book Description :

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.

Stochastic Modeling of Scientific Data

Stochastic Modeling of Scientific Data Book
Author : Peter Guttorp,Vladimir N. Minin
Publisher : CRC Press
Release : 2018-03-29
ISBN : 1351413651
Language : En, Es, Fr & De

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Book Description :

Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.

Optimization of Stochastic Models

Optimization of Stochastic Models Book
Author : Georg Ch. Pflug
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 1461314496
Language : En, Es, Fr & De

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Book Description :

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts. To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. Simulation is the only practicable way of getting insight into such models. Thus, the problem of optimal decisions can be seen as getting simulation and optimization effectively combined. The field is quite new and yet the number of publications is enormous. This book does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated. Optimization of Stochastic Models: The Interface Between Simulation and Optimization is suitable as a text for a graduate level course on Stochastic Models or as a secondary text for a graduate level course in Operations Research.

Stochastic Modeling

Stochastic Modeling Book
Author : Nicolas Lanchier
Publisher : Springer
Release : 2017-01-27
ISBN : 3319500384
Language : En, Es, Fr & De

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Book Description :

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.

Probability and Stochastic Modeling

Probability and Stochastic Modeling Book
Author : Vladimir I. Rotar
Publisher : CRC Press
Release : 2012-08-25
ISBN : 1439872074
Language : En, Es, Fr & De

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Book Description :

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.

Studyguide for Markov Processes for Stochastic Modeling by Ibe Oliver

Studyguide for Markov Processes for Stochastic Modeling by Ibe  Oliver Book
Author : Cram101 Textbook Reviews
Publisher : Cram101
Release : 2013-05
ISBN : 9781478491736
Language : En, Es, Fr & De

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Book Description :

Never HIGHLIGHT a Book Again Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9780872893795. This item is printed on demand.

Introduction to Modeling and Analysis of Stochastic Systems

Introduction to Modeling and Analysis of Stochastic Systems Book
Author : V. G. Kulkarni
Publisher : Springer
Release : 2010-11-03
ISBN : 9781441917720
Language : En, Es, Fr & De

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Book Description :

This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.