摘要翻译：
在许多应用中，生成多元泊松数据是必不可少的。目前的仿真方法存在着从计算复杂度到相关矩阵结构的限制等方面的局限性。我们提出了一种计算效率高、概念上有吸引力的生成多元泊松数据的方法。该方法基于模拟多元正态数据，并将其转换为特定的相关矩阵和泊松率向量。这允许生成具有正相关或负相关以及不同速率的数据。
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英文标题：
《An Elegant Method for Generating Multivariate Poisson Random Variable》
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作者：
Inbal Yahav and Galit Shmueli
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最新提交年份：
2008
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  Generating multivariate Poisson data is essential in many applications. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix. We propose a computationally efficient and conceptually appealing method for generating multivariate Poisson data. The method is based on simulating multivariate Normal data and converting them to achieve a specific correlation matrix and Poisson rate vector. This allows for generating data that have positive or negative correlations as well as different rates.
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PDF链接：
https://arxiv.org/pdf/710.567