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Random number generation
"... Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables dis ..."
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Cited by 136 (30 self)
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Random numbers are the nuts and bolts of simulation. Typically, all the randomness required by the model is simulated by a random number generator whose output is assumed to be a sequence of independent and identically distributed (IID) U(0, 1) random variables (i.e., continuous random variables distributed uniformly over the interval
Comparison of Point Sets and Sequences for QuasiMonte Carlo and for Random Number Generation
"... Algorithmic random number generators require recurring sequences with very long periods and good multivariate uniformity properties. Point sets and sequences for quasiMonte Carlo numerical integration need similar multivariate uniformity properties as well. It then comes as no surprise that both ty ..."
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Algorithmic random number generators require recurring sequences with very long periods and good multivariate uniformity properties. Point sets and sequences for quasiMonte Carlo numerical integration need similar multivariate uniformity properties as well. It then comes as no surprise that both types of applications share common (or similar) construction methods. However, there are some differences in both the measures of uniformity and the construction methods used in practice. We briefly survey these methods and explain some of the reasons for the differences.
Theoretical Biology and Medical
, 2005
"... Modeling the signaling endosome hypothesis: Why a drive to the nucleus is better than a (random) walk ..."
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Modeling the signaling endosome hypothesis: Why a drive to the nucleus is better than a (random) walk