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Markov Processes: An Introduction for Physical Scientists

Autor Daniel T. Gillespie
en Limba Engleză Hardback – 2 dec 1991
Markov process theory is basically an extension of ordinary calculus to accommodate functions whos time evolutions are not entirely deterministic. It is a subject that is becoming increasingly important for many fields of science. This book develops the single-variable theory of both continuous and jump Markov processes in a way that should appeal especially to physicists and chemists at the senior and graduate level.

  • A self-contained, prgamatic exposition of the needed elements of random variable theory
  • Logically integrated derviations of the Chapman-Kolmogorov equation, the Kramers-Moyal equations, the Fokker-Planck equations, the Langevin equation, the master equations, and the moment equations
  • Detailed exposition of Monte Carlo simulation methods, with plots of many numerical examples
  • Clear treatments of first passages, first exits, and stable state fluctuations and transitions
  • Carefully drawn applications to Brownian motion, molecular diffusion, and chemical kinetics
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Specificații

ISBN-13: 9780122839559
ISBN-10: 0122839552
Pagini: 592
Dimensiuni: 152 x 229 x 34 mm
Greutate: 1.08 kg
Editura: ELSEVIER SCIENCE

Public țintă

Professionals/scientists without training in probability and statistics (using books as a "self-help" guide), senior undergraduate and graduate level students in physics and chemistry and mathematicians specializing in game theory, and finite math.

Cuprins

Random Variable Theory
General Features of a Markov Process
Continuous Markov Processes
Jump Markov Processes with Continuum States
Jump Markov Processes with Discrete States
Temporally Homogeneous Birth-Death Markov Processes
Appendixes: Some Useful Integral Identities
Integral Representations of the Delta Functions
An Approximate Solution Procedure for "Open" Moment Evolution Equations
Estimating the Width and Area of a Function Peak
Can the Accuracy of the Continuous Process Simulation Formula Be Improved?
Proof of the Birth-Death Stability Theorem
Solution of the Matrix Differential Equation