Titlebook: Random Number Generation and Monte Carlo Methods; James E. Gentle Textbook 19981st edition Springer-Verlag New York 1998 Markov chain Mont

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Springer-Verlag New York 1998

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Random Number Generation and Monte Carlo Methods978-1-4757-2960-3Series ISSN 1431-8784 Series E-ISSN 2197-1706

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Textbook 19981st editiont the heart of the rapidly developing subdisciplines of computational physics, compu­ tational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recog­ nition of computation as a third approach for advancing the na

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Transformations of Uniform Deviates: General Methods,onuniform random variable might be obtained from a single uniform variate or from a sequence of uniforms. Some methods that use a sequence of uniforms require that the sequence be independent; other methods use a random walk sequence, a Markov chain.

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Simulating Random Numbers from Specific Distributions,fference in the algorithms is their speed. A secondary difference is the size and complexity of the program to implement the algorithm. Because all of the algorithms for generating from nonuniform distributions rely on programs to generate from uniform distributions, an algorithm that uses only a sm

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Quality of Random Number Generators,ning simultaneously. Research work like this that depends so heavily on random numbers emphasizes the need for high-quality random number generators. Yet, as we have seen, not all random number generators are good ones (Park and Miller, 1988, and Ripley, 1988).

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Monte Carlo Studies in Statistics,ns we have complete knowledge of the statistic. In testing a mean of a normal distribution, we use a t statistic, and we know its exact distribution. In other cases, however, we may have a perfectly reasonable test statistic, but know very little about its distribution. For example, suppose a statis

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