Markov Processes

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A01=James R. Kirkwood
Absorbing Markov Chains
Absorbing State
advanced stochastic process textbook
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Age Group_Uncategorized
Author_James R. Kirkwood
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Category1=Non-Fiction
Category=PBT
COP=United States
Cumulative Distribution Function
Customers Arrive
Delayed Renewal Process
Delivery_Delivery within 10-20 working days
detailed balance
Detailed Balance Equations
eq_isMigrated=2
eq_nobargain
Exponential distribution
Exponential Random Variable
Galton Watson Process
Ik Kj
Independent Geometric Random Variables
infinitesimal generators
Irreducible Markov Chain
Kolmogorov equations
Language_English
Markov
Markov Chain
Markov chains
mathematical modeling
Non-absorbing State
Nonabsorbing State
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Pi Pi
Pi Pi Pi
Pi Pi Pi Pi
Pi Pi Pi Pi Pi
Pi Pii
Poisson process
Positive Recurrent
Price_€50 to €100
Probability
Probability Vector
PS=Active
queuing theory
Reversible Markov Chain
softlaunch
Stochastic processes
stochastic simulation
Transition Matrix
Unique Equilibrium State

Product details

  • ISBN 9781482240733
  • Weight: 560g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Feb 2015
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Clear, rigorous, and intuitive, Markov Processes provides a bridge from an undergraduate probability course to a course in stochastic processes and also as a reference for those that want to see detailed proofs of the theorems of Markov processes. It contains copious computational examples that motivate and illustrate the theorems. The text is designed to be understandable to students who have taken an undergraduate probability course without needing an instructor to fill in any gaps.

The book begins with a review of basic probability, then covers the case of finite state, discrete time Markov processes. Building on this, the text deals with the discrete time, infinite state case and provides background for continuous Markov processes with exponential random variables and Poisson processes. It presents continuous Markov processes which include the basic material of Kolmogorov’s equations, infinitesimal generators, and explosions. The book concludes with coverage of both discrete and continuous reversible Markov chains.

While Markov processes are touched on in probability courses, this book offers the opportunity to concentrate on the topic when additional study is required. It discusses how Markov processes are applied in a number of fields, including economics, physics, and mathematical biology. The book fills the gap between a calculus based probability course, normally taken as an upper level undergraduate course, and a course in stochastic processes, which is typically a graduate course.

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