Moment Matching-Based Distribution Fitting with Generalized Hyper-Erlang Distributions

  • Gábor Horváth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7984)


This paper describes a novel moment matching based fitting method for phase-type (PH) distributions. A special sub-class of phase-type distributions is introduced for the fitting, called generalized hyper-Erlang distributions. The user has to provide only two parameters: the number of moments to match, and the upper bound for the sum of the multiplicities of the eigenvalues of the distribution, which is related to the maximal size of the resulting PH distribution. Given these two parameters, our method obtains all PH distributions that match the target moments and have a Markovian representation up to the given size. From this set of PH distributions the best one can be selected according to any distance function.


Relative Entropy Polynomial System Target Distribution Erlang Distribution Moment Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gábor Horváth
    • 1
    • 2
    • 3
  1. 1.Department of Networked Systems and ServicesBudapest University of Technology and EconomicsHungary
  2. 2.MTA-BME Information Systems Research GroupBudapestHungary
  3. 3.Inter-University Center of Telecommunications and InformaticsHungary

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