Handbook of Monte Carlo Methods


        

This homepage accompanies the book:

D.P. Kroese, T. Taimre, Z.I. Botev (2011). Handbook of Monte Carlo Methods,
Wiley Series in Probability and Statistics, John Wiley and Sons, New York.

Order Information: [ Wiley | Amazon ]

Table of Contents: pdf

Lecture notes for a graduate course on Monte Carlo methods given at the 2011 Summer School of the Australian Mathematical Sciences Institute (AMSI). The notes (176 pages) present a highly condensed version of the Handbook (772 pages).


Matlab Programs:
Chapter Title
1 Uniform Random Number Generation
2 Quasirandom Number Generation
3 Random Variable Generation
4 Probability Distributions
5 Random Process Generation
6 Markov Chain Monte Carlo
7 Discrete Event Simulation
8 Statistical Analysis of Simulation Data
9 Variance Reduction
10 Rare-Event Simulation
11 Sensitivity Analysis
12 Randomized Optimization
13 Cross-Entropy Method
14 Particle Methods
15 Applications to Finance
16 Applications to Network Reliability
17 Applications to Differential Equations
B Elements of Mathematical Statistics
Notices:
  • This code is free to use. However, when you use the code in your research, please cite the Handbook.
  • This code was tested under Matlab 7.6.0 (R2008a). We have made an effort to make the programs compatible with earlier (and later) versions of Matlab, but cannot give a guarantee that they will work with other versions.
  • Our aim was to provide simple code that is in direct correspondence with the algorithms and theory in the Handbook, rather than provide the fastest possible implementation. We have deliberatly used a mix of programming styles, to showcase the different approaches that can be used to implement Monte Carlo algorithms.
  • It is recommended to clear the workspace before running the programs (issue a "clear all").
  • Please report any errors to kroese@maths.uq.edu.au.
    Here is a list of errata for the handbook.

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