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Titlebook: Strategies for Quasi-Monte Carlo; Bennett L. Fox Book 1999 Springer Science+Business Media New York 1999 Operations Research.Simulation.Va

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21#
發(fā)表于 2025-3-25 04:20:03 | 只看該作者
22#
發(fā)表于 2025-3-25 09:32:08 | 只看該作者
23#
發(fā)表于 2025-3-25 11:42:38 | 只看該作者
24#
發(fā)表于 2025-3-25 18:37:17 | 只看該作者
Smoothing Summation,skimmed at first reading). We believe that, in the setting of randomized quasi-Monte Carlo, our smoothed estimator makes all others passe. The inequalities (7.1), bracketing the variance of the smoothed estimator averaged over a block, back up this assertion. Suppose that we want to estimate the sum
25#
發(fā)表于 2025-3-25 20:49:42 | 只看該作者
Analysis Of Variance, of that analysis. In particular, that analysis extends well beyond that case — though still restricted to one dimension. Theorem 9.1.2 states that extension. Under weak (checkable) conditions, it brackets the variance by an expression of the form
26#
發(fā)表于 2025-3-26 01:09:26 | 只看該作者
27#
發(fā)表于 2025-3-26 06:00:03 | 只看該作者
28#
發(fā)表于 2025-3-26 12:19:40 | 只看該作者
Permuting Order Statistics,Suppose that a given number of . random variates are to be generated as part of a simulation. A naive procedure generates these variates in the order in which they are used, generally as needed. With pure standard Monte Carlo, this is both natural and reasonable. In contrast, with RQMC it is a bad idea.
29#
發(fā)表于 2025-3-26 15:51:27 | 只看該作者
Generating Bernoulli Trials,Suppose that we want to generate a fixed number of . Bernoulli trials, where the success positions in the given set . of trials matter.
30#
發(fā)表于 2025-3-26 20:38:55 | 只看該作者
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