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Mixture of Polynomials Probability Distributions for Grouped Sample Data

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Probabilistic Graphical Models (PGM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8754))

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Abstract

This paper describes techniques for developing a mixture of polynomials (MOP) probability distribution from a frequency distribution (also termed grouped data) summarized from a large dataset. To accomplish this task, a temporary dataset is produced from the grouped data and the parameters for the MOP function are estimated using a Bspline interpolation technique. Guidance is provided regarding the composition of the temporary dataset, and the selection of split points and order of the MOP approximation. Good results are obtained when using grouped data as compared to the underlying dataset, and this can be a major advantage when using a decision support system to obtain information for estimating probability density functions for random variables of interest.

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Author information

Authors and Affiliations

  1. Department of Management, Missouri State University, Springfield, Missouri, USA

    Barry R. Cobb

Authors
  1. Barry R. Cobb
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Editor information

Editors and Affiliations

  1. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht, The Netherlands

    Linda C. van der Gaag

  2. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,, The Netherlands

    Ad J. Feelders

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© 2014 Springer International Publishing Switzerland

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Cobb, B.R. (2014). Mixture of Polynomials Probability Distributions for Grouped Sample Data. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-11433-0_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11432-3

  • Online ISBN: 978-3-319-11433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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