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29. MONTE CARLO TECHNIQUES
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. 29.1. Sampling the uniform distribution Most Monte Carlo sampling or integration techniques assume a “random number generator ” which generates uniform statistically independent values on the half open interval [0,1). There is a long history of problems with various generators on a finite digital computer, but recently, the RANLUX generator [1] has emerged with a solid theoretical basis in chaos theory. Based on the method of Lüscher, it allows the user to select different quality levels, trading off with speed. Other generators are also available which pass extensive batteries of tests for statistical independence and which have periods which are so long that, for practical purposes, values from these generators can be considered to be uniform and statistically independent. In |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Monte Carlo Technique Finite Digital Computer Monte Carlo Complicated Probability Density Function Uniform Distribution Different Quality Level Random Number Generator Independent Value Chaos Theory Probability Density Function Various Generator Solid Theoretical Basis Random Variable Statistical Independence Practical Purpose Ranlux Generator Difficult Integral Extensive Battery Integration Technique Practical Way Half Open Interval Long History |
| Content Type | Text |
