2015. február 27., péntek

Monte Carlo Methods

Multiplicative, congruental and other random number generators. The periodic and aperiodic sequences of a random number series. Statistical tests on random number sequences. Tests of fit, test of independence, khi-squared and Kolmogorov tests. Empirical tests for uniform random numbers. Special methods for generating non-uniform samples. Generating samples for Gauss, Exponential, Gamma, Beta, and Poisson distributions. Sampling polynoms. Generating random vectors. Sampling isotropic solid angle distribution. Simulating discrete events of given probability using Monte Carlo. Techniques for simulation efficiency increase. Simulating processes with continuous distributions. Sampling algorithms for general distributions. Inverse cumulative distribution, rejection methods, table look-up method, composition method. The generalization of the rejection method. Variance reduction techniques for particle transport. Statistical weight, Russian roulette, method of trajectory splitting. A computer-simulation technique that uses random samples and other statistical methods to find approximate solutions to mathematical or physical problems. In 1993, I tried and it worked. The importance of keeping accurate statistics. I worked as a casino, just as you could collect money in Kosice.Since I knew sooner or later come to the unexpected result stopped..Every time you come out after the 16th one of the unexpected numbers, everything you fail. Need to improve, develop and improve the bugs! If we could push the end of the series will be excellent.

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