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Monte Carlo Methods

  • Book
  • © 1964

Overview

Authors:
  1. J. M. Hammersley

    1. Oxford University Institute of Economics and Statistics, UK

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  2. D. C. Handscomb

    1. Oxford University Computing Laboratory, UK

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Monographs on Statistics and Applied Probability (MSAP)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-vii
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  2. The General Nature of Monte Carlo Methods

    • J. M. Hammersley, D. C. Handscomb
    Pages 1-9

  3. Short Resumé of Statistical Terms

    • J. M. Hammersley, D. C. Handscomb
    Pages 10-24

  4. Random, Pseudorandom, and Quasirandom Numbers

    • J. M. Hammersley, D. C. Handscomb
    Pages 25-42

  5. Direct Simulation

    • J. M. Hammersley, D. C. Handscomb
    Pages 43-49

  6. General Principles of the Monte Carlo Method

    • J. M. Hammersley, D. C. Handscomb
    Pages 50-75

  7. Conditional Monte Carlo

    • J. M. Hammersley, D. C. Handscomb
    Pages 76-84

  8. Solution of Linear Operator Equations

    • J. M. Hammersley, D. C. Handscomb
    Pages 85-96

  9. Radiation Shielding and Reactor Criticality

    • J. M. Hammersley, D. C. Handscomb
    Pages 97-112

  10. Problems in Statistical Mechanics

    • J. M. Hammersley, D. C. Handscomb
    Pages 113-126

  11. Long Polymer Molecules

    • J. M. Hammersley, D. C. Handscomb
    Pages 127-133

  12. Percolation Processes

    • J. M. Hammersley, D. C. Handscomb
    Pages 134-141

  13. Multivariable Problems

    • J. M. Hammersley, D. C. Handscomb
    Pages 142-149

  14. Back Matter

    Pages 150-178
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Keywords

About this book

This monograph surveys the present state of Monte Carlo methods. we have dallied with certain topics that have interested us Although personally, we hope that our coverage of the subject is reasonably complete; at least we believe that this book and the references in it come near to exhausting the present range of the subject. On the other hand, there are many loose ends; for example we mention various ideas for variance reduction that have never been seriously appli(:d in practice. This is inevitable, and typical of a subject that has remained in its infancy for twenty years or more. We are convinced Qf:ver­ theless that Monte Carlo methods will one day reach an impressive maturity. The main theoretical content of this book is in Chapter 5; some readers may like to begin with this chapter, referring back to Chapters 2 and 3 when necessary. Chapters 7 to 12 deal with applications of the Monte Carlo method in various fields, and can be read in any order. For the sake of completeness, we cast a very brief glance in Chapter 4 at the direct simulation used in industrial and operational research, where the very simplest Monte Carlo techniques are usually sufficient. We assume that the reader has what might roughly be described as a 'graduate' knowledge of mathematics. The actual mathematical techniques are, with few exceptions, quite elementary, but we have freely used vectors, matrices, and similar mathematical language for the sake of conciseness.

Authors and Affiliations

  • Oxford University Institute of Economics and Statistics, UK

    J. M. Hammersley

  • Oxford University Computing Laboratory, UK

    D. C. Handscomb

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