Markov Processes for Stochastic Modeling

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  • Author : Masaaki Kijima
  • Publisher : Springer
  • Pages : 341 pages
  • ISBN : 1489931325
  • Rating : /5 from reviews
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Download or Read online Markov Processes for Stochastic Modeling full in PDF, ePub and kindle. this book written by Masaaki Kijima and published by Springer which was released on 19 December 2013 with total page 341 pages. We cannot guarantee that Markov Processes for Stochastic Modeling book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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 : Masaaki Kijima
  • Publisher : Springer
  • Release : 19 December 2013
GET THIS BOOK Markov Processes for Stochastic Modeling

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.

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
  • Author : Oliver Ibe
  • Publisher : Newnes
  • Release : 22 May 2013
GET THIS BOOK Markov Processes for Stochastic Modeling

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

Stochastic Modelling in Process Technology

Stochastic Modelling in Process Technology
  • Author : Herold G. Dehling,Timo Gottschalk,Alex C. Hoffmann
  • Publisher : Elsevier
  • Release : 03 July 2007
GET THIS BOOK Stochastic Modelling in Process Technology

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

Markov Processes in Stochastic Modeling of TransportPhenomena

Markov Processes in Stochastic Modeling of TransportPhenomena
  • Author : Timo Gottschalk
  • Publisher : VDM Publishing
  • Release : 01 April 2009
GET THIS BOOK Markov Processes in Stochastic Modeling of TransportPhenomena

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

Stochastic Modeling of Scientific Data

Stochastic Modeling of Scientific Data
  • Author : Peter Guttorp,Vladimir N. Minin
  • Publisher : CRC Press
  • Release : 29 March 2018
GET THIS BOOK Stochastic Modeling of Scientific Data

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

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
  • Author : Mark Pinsky,Samuel Karlin
  • Publisher : Academic Press
  • Release : 21 January 2022
GET THIS BOOK An Introduction to Stochastic Modeling

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

Stochastic Models Analysis and Applications

Stochastic Models  Analysis and Applications
  • Author : B. R. Bhat
  • Publisher : New Age International
  • Release : 21 January 2022
GET THIS BOOK Stochastic Models Analysis and Applications

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

Stochastic Modeling

Stochastic Modeling
  • Author : Barry L. Nelson
  • Publisher : Courier Corporation
  • Release : 11 October 2012
GET THIS BOOK Stochastic Modeling

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 Modeling and Analysis of Telecom Networks

Stochastic Modeling and Analysis of Telecom Networks
  • Author : Laurent Decreusefond,Pascal Moyal
  • Publisher : John Wiley & Sons
  • Release : 27 December 2012
GET THIS BOOK Stochastic Modeling and Analysis of Telecom Networks

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

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
  • Author : Howard M. Taylor,Samuel Karlin
  • Publisher : Academic Press
  • Release : 10 May 2014
GET THIS BOOK An Introduction to Stochastic Modeling

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

Elements of Stochastic Modelling

Elements of Stochastic Modelling
  • Author : K. A. Borovkov
  • Publisher : World Scientific Publishing Company Incorporated
  • Release : 21 January 2022
GET THIS BOOK Elements of Stochastic Modelling

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