Monte Carlo method
Good, Irving J. Formerly, Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
- Classification of Monte Carlo methods
- Advantages and disadvantages
- Markov-chain Monte Carlo (MCMC)
- Gibbs sampling
- Random digits
- Links to Primary Literature
- Additional Readings
A technique for estimating the solution, x, of a numerical mathematical problem by means of an artificial sampling experiment. The estimate is usually given as the average value, in a sample, of some statistic whose mathematical expectation is equal to x. In many of the useful applications, the mathematical problem itself arises in a problem of probability in physics or other sciences, operational research, image analysis, general statistics, mathematical economics, or econometrics. The importance of the method arises primarily from the need to solve problems for which other methods are more expensive or impracticable, and from the increased importance of all numerical methods because of the development of the electronic digital computer.
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