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Taylor and Francis Group, Journal of Computational and Graphical Statistics, 1(26), p. 217-222, 2017

DOI: 10.1080/10618600.2016.1276840

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Discrete approximation of a mixture distribution via restricted divergence

Journal article published in 2016 by Christian Röver ORCID, Tim Friede
This paper is available in a repository.
This paper is available in a repository.

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Abstract

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.