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By Kardi Teknomo, PhD.
Monte Carlo methods include all methods that are
r elated to the use of random number. This take account of many well know methods such as Importance Sampling, Bootstrap Sampling, Monte Carlo Simulation, Monte Carlo Integration, Genetic Algorithm, Simulated Annealing, Hasting-Metropolis Algorithm, Percolation, Random walk, Ballistic Deposition, just to name a few of them.
This tutorial will introduce the practical way of Monte Carlo Simulation, a subset of Monte Carlo Method. Monte Carlo simulation is one of the most often use method for computer simulations or numerical experiments. It has been applied in wide range of applications from scientific functions such as statistical physics to financial, engineering until military and game.
Click on the topics below to enter to the tutorial
What is Monte Carlo Simulation?
What is simulation?
Why do we need simulation?
Do not use simulation if …
What are the characteristics of Monte Carlo Simulation?
How the Monte Carlo Algorithm works?
What are the advantages of Monte Carlo Simulation?
What are the weaknesses of Monte Carlo Simulation?
Monte Carlo Simulation Nuts and Bolts
Pseudo Random Number Generation
Fair Coins
Unfair or bias coins
Dice
Observation Based Distribution Generation
Are there any other resources for Monte Carlo Simulation?
Simulation related Journals
Give your comments, questions or suggestions
for this tutorial
See Also:
Bootstrap sampling tutorial, Machine Learning Algoritm tutorial, Recursive statistics
This tutorial is copyrighted.
Preferable reference for this tutorial is
Teknomo, Kardi. Monte Carlo Simulation Tutorial. http:\\people.revoledu.com\kardi\
tutorial\Simulation\
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