Phase-Type Fitting Using HyperStar: In this paper we provide a hands-on discussion of the use of the HyperStar phase-type fitting tool in common application scenarios. HyperStar allows fitting Hyper-Erlang distributions to empirical data, using a variety of algorithms and operation modes. We describe simple cluster-based fitting, a new graphical method for refining the density approximation, a new command-line interface, and the integration of HyperStar with a Mathematica implementation of a fitting algorithm. Furthermore, we describe the use of Hyper-Erlang distributions in simulation. Throughout our discussion we illustrate the concepts on a data set which has been shown to be difficult to fit with a PH distribution.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Hurtado, Paul J.; Kirosingh, Adam S.: Generalizations of the `linear chain trick’: incorporating more flexible dwell time distributions into mean field ODE models (2019)
- Cao, Jianyu; Xie, Weixin: Stability of a two-queue cyclic polling system with BMAPs under gated service and state-dependent time-limited service disciplines (2017)
- Juan F. Pérez; Daniel F. Silva; Julio C. Góez; Andrés Sarmiento; Andrés Sarmiento-Romero; Raha Akhavan-Tabatabaei; Germán Riaño: Algorithm 972: jMarkov: An Integrated Framework for Markov Chain Modeling (2017) not zbMATH
- Pérez, Juan F.; Silva, Daniel F.; Góez, Julio C.; Sarmiento, Andrés; Sarmiento-Romero, Andrés; Akhavan-Tabatabaei, Raha; Riaño, Germán: Algorithm 972: jMarkov: an integrated framework for Markov chain modeling (2017)
- Reinecke, Philipp; Krauß, Tilman; Wolter, Katinka: Phase-type fitting using hyperstar (2013) ioport
- Reinecke, Philipp; Krauß, Tilman; Wolter, Katinka: Cluster-based fitting of phase-type distributions to empirical data (2012)