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  1. Markov Processes: An Introduction for Physical Scientists
    Markov Processes: An Introduction for Physical Scientists
    Markov Processes: An Introduction for Physical Scientists
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  2. Markov Processes: An Introduction for Physical Scientists

    Delivery: 10-20 Working Days
    Customer Ratings (2 reviews)
    Price R2024.00

Additional Information

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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Daniel T. Gillespie
Academic Press
Academic Press
Academic Press
Academic Press
Most Helpful Customer Reviews

I have read and worked through much of Gillespie's book. It was exactly what I needed in the way of a meaty but clear and accessible introduction to the subject. I know of no other book like it. There are good books that are more mathematically oriented (Okesendahl), that outline rather than narrate (Gardiner), and that introduce a more limited content (my own), but there is nothing like Gillespie's book: introductory yet wide-ranging content. It was perfect for this physicist who wanted to be introduced to the details of the subject.
This is the most readable introduction to Markov processes I have found.
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