Name
Abstract | ||
|---|---|---|
The term Monte Carlo method stands for any member of a very large class of computational methods that use randomness to generate "typical" instances of a problem under investigation. Typical instances are generated because it's impractical or even impossible to generate all instances. A set of typical instances is supposed to help us learn something about a problem of interest. Most of the time, Monte Carlo works amazingly well, but when used blindly, with no firm basis in theory, it can yield some very strange results or run for many, many hours and yield nothing. One of the triumphs of the modern period in Monte Carlo methods has been a dramatic improvement in our understanding of how to speed up the computation and how to know when the method will work. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/MCSE.2006.27 | Computing in Science and Engineering |
Keywords | Field | DocType |
dramatic improvement,strange result,large class,firm basis,term monte carlo method,monte carlo methods,modern period,computational method,typical instance,monte carlo method,guest editors,monte carlo,markov chains,markov chain monte carlo,randomness,algorithm | Monte Carlo method in statistical physics,Monte Carlo method,Markov chain Monte Carlo,Computer science,Hybrid Monte Carlo,Quasi-Monte Carlo method,Theoretical computer science,Dynamic Monte Carlo method,Monte Carlo integration,Monte Carlo molecular modeling | Journal |
Volume | Issue | ISSN |
8 | 2 | 1521-9615 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beichl, Isabel | 1 | 63 | 22.58 |
| Francis Sullivan | 2 | 49 | 17.33 |