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2020 | OriginalPaper | Buchkapitel

An Efficient Brute Force Approach to Fit Finite Mixture Distributions

verfasst von : Falko Bause

Erschienen in: Measurement, Modelling and Evaluation of Computing Systems

Verlag: Springer International Publishing

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Abstract

This paper presents a brute force approach to fit finite mixtures of distributions considering the empirical probability density and cumulative distribution functions as well as the empirical moments. The fitting problem is solved using a non-negative least squares method determining a mixture from a larger set of distributions.

The approach is experimentally validated for finite mixtures of Erlang distributions. The results show that a feasible number of component distributions, which accurately fit to the empirical data, is obtained within a short CPU time.

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Vorheriges Kapitel It Sometimes Works: A Lifting Algorithm for Repair of Stochastic Process Algebra Models

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\(g(x|\cdot )\) denotes \(g(x|({{\varvec{\pi }}},{{\varvec{\theta }}}))\) for arbitrary parameters \(({{\varvec{\pi }}},{{\varvec{\theta }}})\).

\(\pi ,\mathrm {e}\) the transcendental numbers.

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Titel: An Efficient Brute Force Approach to Fit Finite Mixture Distributions
verfasst von: Falko Bause
Verlag: Springer International Publishing
Buch: Measurement, Modelling and Evaluation of Computing Systems
Print ISBN: 978-3-030-43023-8

Electronic ISBN: 978-3-030-43024-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-43024-5_13

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