Discrete Approximations of Probability Distributions
指出常用离散近似方法会系统低估原始分布的矩，并基于高斯求积法提出新方法，可任意降低近似误差，对需要简化概率模型的学者有用。
Practical limits on the size of most probabilistic models require that probability distributions be approximated by a few representative values and associated probabilities. This paper demonstrates that methods commonly used to determine discrete approximations of probability distributions systematically underestimate the moments of the original distribution. A new procedure based on gaussian quadrature is developed in this paper. It can be used to decrease the error in the approximation to any desired level.