Stochastic numerical methods : an introduction for students and scientists
Author(s)
Bibliographic Information
Stochastic numerical methods : an introduction for students and scientists
(Physics textbook)
Wiley-VCH, c2014
Available at 7 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Stochastic Numerical Methods introduces at Master level the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. Examples included in the book range from phase-transitions and critical phenomena, including details of data analysis (extraction of critical exponents, finite-size effects, etc.), to population dynamics, interfacial growth, chemical reactions, etc. Program listings are integrated in the discussion of numerical algorithms to facilitate their understanding.
From the contents: * Review of Probability Concepts * Monte Carlo Integration * Generation of Uniform and Non-uniform * Random Numbers: Non-correlated Values * Dynamical Methods * Applications to Statistical Mechanics * Introduction to Stochastic Processes * Numerical Simulation of Ordinary and * Partial Stochastic Differential Equations * Introduction to Master Equations * Numerical Simulations of Master Equations * Hybrid Monte Carlo * Generation of n-Dimensional Correlated * Gaussian Variables * Collective Algorithms for Spin Systems * Histogram Extrapolation * Multicanonical Simulations
Table of Contents
- 1. Review of Probability Concepts 2. Monte Carlo Integration 3. Generation of Non-uniform Random Numbers: Non-correlated Values 4. Dynamical Methods 5. Applications to Statistical Mechanics 6. Introduction to Stochastic Processes 7. Numerical Simulation of Stochastic Differential equations 8.Introduction to Master Equations 9. Numerical Simulations of Master Equations 10. Hybrid Monte Carlo 11. Stochastic Partial Differential Equations A. Generation of Uniform ^U (0
- 1) Random Numbers B. Generation of n-dimensional Correlated Gaussian Variables C. Calculation of the Correlation Function of a Series D. Collective Algorithms for Spin Systems E. Histogram Extrapolation F. Multicanonical Simulations G. Discrete Fourier Transform
by "Nielsen BookData"