When a plain Monte Carlo estimate on $n$ samples has variance $\sigma^2/n$, then scrambled digital nets attain a variance that is $o(1/n)$ as $n\to\infty$. For finite $n$ and an adversarially selected integrand, the variance of a scrambled $(t,m,s)$-net can be at most $\Gamma\sigma^2/n$ for a maximal gain coefficient $\Gamma<\infty$. The most widely used digital nets and sequences are those of Sobol'. It was previously known that $\Gamma\leqslant 2^t3^s$ for Sobol' points as well as Niederreiter-Xing points. In this paper we study nets in base $2$. We show that $\Gamma \leqslant2^{t+s-1}$ for nets. This bound is a simple, but apparently unnoticed, consequence of a microstructure analysis in Niederreiter and Pirsic (2001). We obtain a sharper bound that is smaller than this for some digital nets. We also show that all nonzero gain coefficients must be powers of two. A consequence of this latter fact is a simplified algorithm for computing gain coefficients of nets in base $2$.
翻译:当一个平淡的蒙特卡洛对美元样品的估算值出现差异时,当一个平淡的蒙特卡洛对美元样品的估算值出现差异时,最广泛使用的数字网和序列是索波尔的。以前已知索波尔点和Niederreiter-Xing点的基值为$(t,m,s)-net。在本文中,我们研究的基值为$(t,m,s)-net的差异最多为$(gamma\sigma2,n)美元,以换取最大增益系数$\Gamma\gma ⁇ /infty$。由于对Niederreiter和Pirsic的微结构分析,这一约束非常简单,但显然不为人们所注意。我们以前知道,索波尔点点和Niederreqslant 2,t3$(n)和Niedrerereiter-Xing点的基值为$。在本文中,我们以基值为基值的基值为2美元的研究网值的基值的基值为基数。我们还要显示,这个基数的基数的基数将获得的不精确值为2。