Markov Processes for Stochastic Modeling

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

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
  • Author : Masaaki Kijima
  • Publisher : Springer
  • Release : 19 December 2013
GET THIS BOOKMarkov 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.

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 BOOKAn Introduction to Stochastic Modeling

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

Statistical Topics and Stochastic Models for Dependent Data with Applications

Statistical Topics and Stochastic Models for Dependent Data with Applications
  • Author : Vlad Stefan Barbu,Nicolas Vergne
  • Publisher : John Wiley & Sons
  • Release : 03 December 2020
GET THIS BOOKStatistical Topics and Stochastic Models for Dependent Data with Applications

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

Stochastic Modelling of Social Processes

Stochastic Modelling of Social Processes
  • Author : Andreas Diekmann,Peter Mitter
  • Publisher : Academic Press
  • Release : 10 May 2014
GET THIS BOOKStochastic Modelling of Social Processes

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

Markov Chain Monte Carlo

Markov Chain Monte Carlo
  • Author : Dani Gamerman,Hedibert F. Lopes
  • Publisher : CRC Press
  • Release : 10 May 2006
GET THIS BOOKMarkov Chain Monte Carlo

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site

Markov Chains and Stochastic Stability

Markov Chains and Stochastic Stability
  • Author : Sean Meyn,Richard L. Tweedie
  • Publisher : Cambridge University Press
  • Release : 02 April 2009
GET THIS BOOKMarkov Chains and Stochastic Stability

New up-to-date edition of this influential classic on Markov chains in general state spaces. Proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. New commentary by Sean Meyn, including updated references, reflects developments since 1996.

Markov Chains and Decision Processes for Engineers and Managers

Markov Chains and Decision Processes for Engineers and Managers
  • Author : Theodore J. Sheskin
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKMarkov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms used to solve Markov models. Providing a unified treatment of Markov chains and Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers

Markov Processes

Markov Processes
  • Author : Daniel T. Gillespie,S. GILLESPIE
  • Publisher : Gulf Professional Publishing
  • Release : 18 May 1992
GET THIS BOOKMarkov Processes

Markov process theory provides a mathematical framework for analyzing the elements of randomness that are involved in most real-world dynamical processes. This introductory text, which requires an understanding of ordinary calculus, develops the concepts and results of random variable theory.

Semi-Markov Processes: Applications in System Reliability and Maintenance

Semi-Markov Processes: Applications in System Reliability and Maintenance
  • Author : Franciszek Grabski
  • Publisher : Elsevier
  • Release : 25 September 2014
GET THIS BOOKSemi-Markov Processes: Applications in System Reliability and Maintenance

Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time semi-Markov processes and their applications in reliability and maintenance. The book explains how to construct semi-Markov models and discusses the different reliability parameters and characteristics that can be obtained from those models. The book is a useful resource for mathematicians, engineering practitioners, and PhD and MSc students who want to understand the basic concepts and results of semi-Markov process theory. Clearly

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
  • Publisher : Cram101
  • Release : 01 July 2012
GET THIS BOOKStudyguide for Markov Processes for Stochastic Modeling by Oliver Ibe, Isbn 9780123744517

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 .

Stochastic Modeling

Stochastic Modeling
  • Author : Nicolas Lanchier
  • Publisher : Springer
  • Release : 27 January 2017
GET THIS BOOKStochastic Modeling

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

Studyguide for Markov Processes for Stochastic Modeling by Ibe, Oliver

Studyguide for Markov Processes for Stochastic Modeling by Ibe, Oliver
  • Author : Cram101 Textbook Reviews
  • Publisher : Cram101
  • Release : 01 May 2013
GET THIS BOOKStudyguide for Markov Processes for Stochastic Modeling by Ibe, Oliver

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.