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

Adele Marshall () and Barry Shaw

Computational Management Science, 2014, vol. 11, issue 1, 139-155

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

Keywords: Bayesian networks; Computational learning; Conditional phase-type distribution; Coxian phase-type distributions; Induction (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10287-012-0157-z

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