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Computational learning of the conditional phase-type (C-Ph) distribution

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  • Adele Marshall
  • Barry Shaw

Abstract

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a data set as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output. The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Adele Marshall & Barry Shaw, 2014. "Computational learning of the conditional phase-type (C-Ph) distribution," Computational Management Science, Springer, vol. 11(1), pages 139-155, January.
    • Handle: RePEc:spr:comgts:v:11:y:2014:i:1:p:139-155
    DOI: 10.1007/s10287-012-0157-z
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    References listed on IDEAS

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    1. Hojsgaard, Soren & Thiesson, Bo, 1995. "BIFROST -- Block recursive models induced from relevant knowledge, observations, and statistical techniques," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 155-175, February.
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