By Kardi Teknomo, PhD .

Share this: Google+
| Next >

Monte Carlo Simulation Tutorial

Monte Carlo methods include all methods that are related 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


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 , Multi Agent simulation

This tutorial is copyrighted .

Preferable reference for this tutorial is

Teknomo, Kardi. Monte Carlo Simulation Tutorial.