从随机矩阵到通过高斯正交的蒙特卡罗积分

2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI:10.1109/SSP.2018.8450783

R. Bardenet, A. Hardy

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引用次数: 0

摘要

我们在[1]中引入了一种新的蒙特卡罗估计器，它依赖于确定性点过程(DPPs)。我们最初的动机是随机矩阵理论结果的特殊性质。这个动机在原始论文[1]中是不存在的，所以我们在这里开发它。然后，我们给出[1]内容的非技术概述，坚持统计信号处理受众可能感兴趣的要点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature

We introduced in [1] a new Monte Carlo estimator that relies on determinantal point processes (DPPs). We were initially motivated by peculiar properties of results from random matrix theory. This motivation is absent from the original paper [1], so we develop it here. Then, we give a non-technical overview of the contents of [1], insisting on points that may be of interest to the statistical signal processing audience.

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2018 IEEE Statistical Signal Processing Workshop (SSP)

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