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Continuous time Markov processes : an introduction / Thomas M. Liggett.

By: Series: Graduate studies in mathematics ; v. 113.Publisher: Providence, R.I. : American Mathematical Society, c2010Copyright date: ©2010.Description: xii, 271 p. ; 27 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780821849491
  • 0821849492
Subject(s):
Contents:
One-dimensional Brownian motion -- Continuous time Markov chains -- Feller processes -- Interacting particle systems -- Stochastic integration -- Multi-dimensional Brownian motion and the Dirichlet problem.
Summary: 'Markov processes are among the most important stochastic processes for both theory and applications. This book develops the general theory of these processes and applies this theory to various special examples. The initial chapter is devoted to the most important classical example--one-dimensional Brownian motion. This, together with a chapter on continuous time Markov chains, provides the motivation for the general setup based on semigroups and generators. Chapters on stochastic calculus and probabilistic potential theory give an introduction to some of the key areas of application of Brownian motion and its relatives. A chapter on interacting particle systems treats a more recently developed class of Markov processes that have as their origin problems in physics and biology.'--Publisher's description.
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Item type Current library Home library Call number Materials specified Copy number Status Date due Barcode
AM PERPUSTAKAAN TUN SERI LANANG PERPUSTAKAAN TUN SERI LANANG KOLEKSI AM-P. TUN SERI LANANG (ARAS 5) QA274.7.L548 (Browse shelf(Opens below)) 1 Available 00002153641

Includes bibliographical references and index.

One-dimensional Brownian motion -- Continuous time Markov chains -- Feller processes -- Interacting particle systems -- Stochastic integration -- Multi-dimensional Brownian motion and the Dirichlet problem.

'Markov processes are among the most important stochastic processes for both theory and applications. This book develops the general theory of these processes and applies this theory to various special examples. The initial chapter is devoted to the most important classical example--one-dimensional Brownian motion. This, together with a chapter on continuous time Markov chains, provides the motivation for the general setup based on semigroups and generators. Chapters on stochastic calculus and probabilistic potential theory give an introduction to some of the key areas of application of Brownian motion and its relatives. A chapter on interacting particle systems treats a more recently developed class of Markov processes that have as their origin problems in physics and biology.'--Publisher's description.

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