Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
468327 | Computers & Mathematics with Applications | 2012 | 12 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Philipp Reinecke, Tilman Krauß, Katinka Wolter,