Titlebook: Random and Quasi-Random Point Sets; Peter Hellekalek,Gerhard Larcher Book 1998 Springer Science+Business Media New York 1998 Generator.Ker

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0930-0325 eration. We will cover a broad spectrum of questions, from advanced metric number theory to pricing financial derivatives. The Monte Carlo method is one of the most important tools of system modeling. Deterministic algorithms, so-called uniform random number gen­ erators, are used to produce the inp

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On the Assessment of Random and Quasi-Random Point Sets, deterministic algorithms to generate finite point sets in high dimensions. The quality of the equidistribution of these point sets may have a decisive influence on our numerical results. In this contribution, we will discuss the main concepts to assess equidistribution. In particular, we will prese

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Lattice Rules: How Well Do They Measure Up?,of . points. Monte Carlo methods choose . randomly and typically obtain an error of 0(N./.). Quasi-Monte Carlo methods attempt to decrease the error by choosing . in a deterministic (or quasi-random) way so that the points are more uniformly spread over the integration domain.

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Nets, (,)-Sequences, and Algebraic Geometry,unit cube . [0, 1].. Point sets and sequences with small star discrepancy in I. are informally called ., respectively ., in . It is also customary to speak of sets, respectively sequences, of . in 7.. Such point sets and sequences play a crucial role in applications of numerical quasi-Monte Carlo me

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Financial Applications of Monte Carlo and Quasi-Monte Carlo Methods,loped an elegant mathematical theory for option pricing, financial modeling based on stochastic differential equations has become the theoretical underpinning for the practice of trading financial derivatives. Why theoretical prices derived from such stochastic models are so important in practice ca

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