Discrete Approximation of a Mixture Distribution via Restricted Divergence
提出DIRECT算法，通过限制Kullback-Leibler散度，将大量或无限组分的混合分布近似为易处理的离散混合分布，并保证预设精度。
Mixture distributions arise in many application areas, for example, as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (divergence restricting conditional tesselation) algorithm is set up such that a prespecified precision, defined in terms of Kullback–Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is demonstrated in two examples. Supplementary materials for this article are available online.