Exploring Monte Carlo Methods 1st Edition by William Dunn, Kenneth Shultis ISBN 0080930611 9780080930619

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Product details:

ISBN 10: 0080930611

ISBN 13: 9780080930619

Author:  William L. Dunn, J. Kenneth Shultis 

Exploring Monte Carlo Methods 1st Edition: 

Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as “Monte Carlo.” The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous “Buffon’s needle problem” provides a unifying theme as it is repeatedly used to illustrate many features of Monte Carlo methods.

This book provides the basic detail necessary to learn how to apply Monte Carlo methods and thus should be useful as a text book for undergraduate or graduate courses in numerical methods. It is written so that interested readers with only an understanding of calculus and differential equations can learn Monte Carlo on their own. Coverage of topics such as variance reduction, pseudo-random number generation, Markov chain Monte Carlo, inverse Monte Carlo, and linear operator equations will make the book useful even to experienced Monte Carlo practitioners.

• Provides a concise treatment of generic Monte Carlo methods
• Proofs for each chapter
• Appendixes include Certain mathematical functions; Bose Einstein functions, Fermi Dirac functions, Watson functions

 

Exploring Monte Carlo Methods 1st Edition Table of contents:

• Chapter 1: Introduction

  • What is Monte Carlo?
  • A brief history of Monte Carlo
  • Monte Carlo as quadrature
  • Monte Carlo as simulation
  • Preview of things to come
  • Summary
  • Problems

• Chapter 2: The basis of Monte Carlo

  • Single continuous random variables
  • Discrete random variables
  • Multiple random variables
  • The law of large numbers
  • The central limit theorem
  • Monte Carlo quadrature
  • Monte Carlo simulation
  • Summary
  • Problems

• Chapter 3: Pseudorandom number generators

  • Linear congruential generators
  • Structure of the generated random numbers
  • Characteristics of good random number generators
  • Tests for congruential generators
  • Practical multiplicative congruential generators
  • Shuffling a generator’s output
  • Skipping ahead
  • Combining generators
  • Other random number generators
  • Summary
  • Problems

• Chapter 4: Sampling, scoring, and precision

  • Sampling
  • Scoring
  • Accuracy and precision
  • Summary
  • Problems

• Chapter 5: Variance reduction techniques

  • Use of transformations
  • Importance sampling
  • Systematic sampling
  • Stratified sampling
  • Correlated sampling
  • Partition of the integration volume
  • Reduction of dimensionality
  • Russian roulette and splitting
  • Combinations of different variance reduction techniques
  • Biased estimators
  • Improved Monte Carlo integration schemes
  • Summary
  • Problems

• Chapter 6: Markov chain Monte Carlo

  • Markov chains to the rescue
  • Brief review of probability concepts
  • Bayes theorem
  • Inference and decision applications
  • Summary
  • Problems

• Chapter 7: Inverse Monte Carlo

  • Formulation of the inverse problem
  • Inverse Monte Carlo by iteration
  • Symbolic Monte Carlo
  • Inverse Monte Carlo by simulation
  • General applications of IMC
  • Summary
  • Problems

• Chapter 8: Linear operator equations

  • Linear algebraic equations
  • Linear integral equations
  • Linear differential equations
  • Eigenvalue problems
  • Summary
  • Problems

• Chapter 9: The fundamentals of neutral particle transport

  • Description of the radiation field
  • Radiation interactions with the medium
  • Transport equation
  • Adjoint transport equation
  • Summary
  • Problems

• Chapter 10: Monte Carlo simulation of neutral particle transport

  • Basic approach for Monte Carlo transport simulations
  • Geometry
  • Sources
  • Path-length estimation
  • Purely absorbing media
  • Type of collision
  • Time dependence
  • Particle weights
  • Scoring and tallies
  • An example of one-speed particle transport
  • Monte Carlo based on the integral transport equation
  • Variance reduction and nonanalog methods
  • Summary

 

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