Stochastic numerical methods : an introduction for students and scientists
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Bibliographic Information
Stochastic numerical methods : an introduction for students and scientists
（Physics textbook）
Wiley-VCH, c2014
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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
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