Cluster-based fitting of phase-type distributions to empirical data
Cluster-based fitting of phase-type distributions to empirical data
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DOI：
10.1016/j.camwa.2012.03.016
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
P. Reinecke;Tilman Krauss;K. Wolter
中科院分区：
文献类型：
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作者：
P. Reinecke;Tilman Krauss;K. Wolter
我们提出了一种基于聚类的相型分布拟合方法，该方法特别适合捕获经验数据集的共同特征。通过这种方法拟合的分布在高效的模拟方法中特别有用。我们描述了 Hyper-* 工具，它实现了算法并提供了一个用户友好的界面来实现高效的相位类型拟合。我们将基于聚类的拟合与基于分割的方法和其他算法进行了比较，并表明聚类为典型的经验数据集提供了良好的结果。
We present a clustering-based fitting approach for phase-type distributions that is particularly suited to capture common characteristics of empirical data sets. The distributions fitted by this approach are especially useful in efficient simulation approaches. We describe the Hyper-* tool, which implements the algorithm and offers a user-friendly interface to efficient phase-type fitting. We provide a comparison of cluster-based fitting with segmentation-based approaches and other algorithms and show that clustering provides good results for typical empirical data sets.