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

FRONT

accrete

Pseudocodes,This chapter lists the pseudocodes to which the preceding chapters refer.

支架

Poisson Processes: Auxiliary Matter,ves some computations needed to complete the proof of Proposition 3.2.2. This entire chapter can be skimmed by those more interested in the structure of strategies for quasi-Monte Carlo than by the details indicated above.

maverick

Wernickes-area

Background on Randomized QMC,r associated variance, and show how to simultaneously increase dynamically their size and the size of the (.)-net(s) corresponding to . without throwing anything away. Finally, we deal with Latin supercubes, with an extended example, and their relation to a functional ANOVA.

Ambiguous

讓你明白

Introduction,s have some common features, both in design and analysis, we aim to be specific. To fix ideas, our initial illustrations are for Poisson processes. The point is that these processes as well as the others we consider are . generated in isolation but rather as part of a simulation to estimate the expe

女歌星

小隔間

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

commensurate

Smoothing Variate Generation,we make precise. To do this, we use a procedure somewhat analogous to our smoothing of discrete summmation in Section 7.1. Though it is not practical to use this method in wholesale fashion to generate many discrete variates in a single simulation run, it is practical when applied only to a few vari