Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
Author: Masaaki Kijima
Publsiher: Springer
Total Pages: 341
Release: 2013-12-19
ISBN 10: 1489931325
ISBN 13: 9781489931320
Language: EN, FR, DE, ES & NL

Markov Processes for Stochastic Modeling Book Review:

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
Author: Oliver Ibe
Publsiher: Newnes
Total Pages: 514
Release: 2013-05-22
ISBN 10: 0124078397
ISBN 13: 9780124078390
Language: EN, FR, DE, ES & NL

Markov Processes for Stochastic Modeling Book Review:

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
Author: Oliver Ibe
Publsiher: Academic Press
Total Pages: 512
Release: 2008-09-02
ISBN 10: 0080922457
ISBN 13: 9780080922454
Language: EN, FR, DE, ES & NL

Markov Processes for Stochastic Modeling Book Review:

Markov processes are used to model systems with limited memory. They are used in many areas 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. This book, which is written for upper level undergraduate and graduate students, and researchers, presents a unified presentation of Markov processes. In addition to traditional topics such as Markovian queueing system, the book discusses such topics as continuous-time random walk,correlated random walk, Brownian motion, diffusion processes, hidden Markov models, Markov random fields, Markov point processes and Markov chain Monte Carlo. Continuous-time random walk is currently used in econophysics to model the financial market, which has traditionally been modelled as a Brownian motion. Correlated random walk is popularly used in ecological studies to model animal and insect movement. Hidden Markov models are used in speech analysis and DNA sequence analysis while Markov random fields and Markov point processes are used in image analysis. Thus, the book is designed to have a very broad appeal. - Provides the practical, current applications of Markov processes - Coverage of HMM, Point processes, and Monte Carlo - Includes enough theory to help students gain throrough understanding of the subject - Principles can be immediately applied in many specific research projects, saving researchers time - End of chapter exercises provide reinforcement, practice and increased understanding to the student

Student Solutions Manual for Markov Processes for Stochastic Modeling

Student Solutions Manual for Markov Processes for Stochastic Modeling
Author: Oliver Ibe
Publsiher: Academic Press
Total Pages: 120
Release: 2008-11-21
ISBN 10: 0080952143
ISBN 13: 9780080952147
Language: EN, FR, DE, ES & NL

Student Solutions Manual for Markov Processes for Stochastic Modeling Book Review:

Student Solutions Manual for Markov Processes for Stochastic Modeling

Markov processes for stochastic modeling

Markov processes for stochastic modeling
Author: Oliver C. Ibe
Publsiher: Unknown
Total Pages: 494
Release: 2013
ISBN 10: 1928374650XXX
ISBN 13: OCLC:849916685
Language: EN, FR, DE, ES & NL

Markov processes for stochastic modeling Book Review:

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
Author: Howard M. Taylor,Samuel Karlin
Publsiher: Academic Press
Total Pages: 410
Release: 2014-05-10
ISBN 10: 1483269272
ISBN 13: 9781483269276
Language: EN, FR, DE, ES & NL

An Introduction to Stochastic Modeling Book Review:

An Introduction to Stochastic Modeling 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.

Markov Processes in Stochastic Modeling of TransportPhenomena

Markov Processes in Stochastic Modeling of TransportPhenomena
Author: Timo Gottschalk
Publsiher: VDM Publishing
Total Pages: 136
Release: 2009-04-01
ISBN 10: 9783838105680
ISBN 13: 3838105680
Language: EN, FR, DE, ES & NL

Markov Processes in Stochastic Modeling of TransportPhenomena Book Review:

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
Author: Timo Gottschalk
Publsiher: Unknown
Total Pages: 124
Release: 2007
ISBN 10: 1928374650XXX
ISBN 13: OCLC:254674679
Language: EN, FR, DE, ES & NL

From Application to Theory Markov Processes in Stochastic Modeling of Transport Book Review:

Stochastic Modelling in Process Technology

Stochastic Modelling in Process Technology
Author: Herold G. Dehling,Timo Gottschalk,Alex C. Hoffmann
Publsiher: Elsevier
Total Pages: 290
Release: 2007-07-03
ISBN 10: 9780080548975
ISBN 13: 0080548970
Language: EN, FR, DE, ES & NL

Stochastic Modelling in Process Technology Book Review:

There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. Introduction to stochastic process modelling as an alternative modelling technique Shows how stochastic modelling may be succesful where the traditional technique fails Overview of stochastic modelling in process technology in the research literature Illustration of the principle by a wide range of practical examples In-depth and self-contained discussions Points the way to both mathematical and technological research in a new, rewarding field

From Application to Theory Markov Processes in Stochastic Modeling of Transport Phenomena

From Application to Theory  Markov Processes in Stochastic Modeling of Transport Phenomena
Author: Timo Gottschalk
Publsiher: Unknown
Total Pages: 135
Release: 2007
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1184302960
Language: EN, FR, DE, ES & NL

From Application to Theory Markov Processes in Stochastic Modeling of Transport Phenomena Book Review:

Stochastic Modelling of Social Processes

Stochastic Modelling of Social Processes
Author: Andreas Diekmann,Peter Mitter
Publsiher: Academic Press
Total Pages: 352
Release: 2014-05-10
ISBN 10: 1483266567
ISBN 13: 9781483266565
Language: EN, FR, DE, ES & NL

Stochastic Modelling of Social Processes Book Review:

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
Author: Nicolas Lanchier
Publsiher: Springer
Total Pages: 303
Release: 2017-01-27
ISBN 10: 3319500384
ISBN 13: 9783319500386
Language: EN, FR, DE, ES & NL

Stochastic Modeling Book Review:

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 Modeling of Scientific Data

Stochastic Modeling of Scientific Data
Author: Peter Guttorp
Publsiher: CRC Press
Total Pages: 384
Release: 2018-03-29
ISBN 10: 135141366X
ISBN 13: 9781351413664
Language: EN, FR, DE, ES & NL

Stochastic Modeling of Scientific Data Book Review:

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.

Stochastic Modeling

Stochastic Modeling
Author: Barry L. Nelson
Publsiher: Courier Corporation
Total Pages: 336
Release: 2012-10-11
ISBN 10: 0486139948
ISBN 13: 9780486139944
Language: EN, FR, DE, ES & NL

Stochastic Modeling Book Review:

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.

Studyguide for Markov Processes for Stochastic Modeling by Oliver Ibe Isbn 9780123744517

Studyguide for Markov Processes for Stochastic Modeling by Oliver Ibe  Isbn 9780123744517
Author: Cram101 Textbook Reviews
Publsiher: Cram101
Total Pages: 144
Release: 2012-07
ISBN 10: 9781478405344
ISBN 13: 1478405341
Language: EN, FR, DE, ES & NL

Studyguide for Markov Processes for Stochastic Modeling by Oliver Ibe Isbn 9780123744517 Book Review:

Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780123744517 .

Studyguide for Markov Processes for Stochastic Modeling by Ibe Oliver

Studyguide for Markov Processes for Stochastic Modeling by Ibe  Oliver
Author: Cram101 Textbook Reviews
Publsiher: Cram101
Total Pages: 116
Release: 2013-05
ISBN 10: 9781478491736
ISBN 13: 1478491736
Language: EN, FR, DE, ES & NL

Studyguide for Markov Processes for Stochastic Modeling by Ibe Oliver Book Review:

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.

Stochastic Models Analysis and Applications

Stochastic Models  Analysis and Applications
Author: B. R. Bhat
Publsiher: New Age International
Total Pages: 408
Release: 2004
ISBN 10: 9788122412284
ISBN 13: 8122412289
Language: EN, FR, DE, ES & NL

Stochastic Models Analysis and Applications Book Review:

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.

Probability and Stochastic Modeling

Probability and Stochastic Modeling
Author: Vladimir I. Rotar
Publsiher: CRC Press
Total Pages: 508
Release: 2012-08-25
ISBN 10: 1439872066
ISBN 13: 9781439872062
Language: EN, FR, DE, ES & NL

Probability and Stochastic Modeling Book Review:

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 and Analysis of Telecom Networks

Stochastic Modeling and Analysis of Telecom Networks
Author: Laurent Decreusefond,Pascal Moyal
Publsiher: John Wiley & Sons
Total Pages: 387
Release: 2012-12-27
ISBN 10: 1118563018
ISBN 13: 9781118563014
Language: EN, FR, DE, ES & NL

Stochastic Modeling and Analysis of Telecom Networks Book Review:

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.

Introduction to Modeling and Analysis of Stochastic Systems

Introduction to Modeling and Analysis of Stochastic Systems
Author: V. G. Kulkarni
Publsiher: Springer
Total Pages: 313
Release: 2010-11-03
ISBN 10: 1441917721
ISBN 13: 9781441917720
Language: EN, FR, DE, ES & NL

Introduction to Modeling and Analysis of Stochastic Systems Book Review:

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.