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

Adele H. Marshall, Barry Shaw

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 dataset 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.
Original languageEnglish
Pages (from-to)139-155
Number of pages17
JournalComputational Management Science
Volume11
Issue number1
Early online date30 Oct 2012
DOIs
Publication statusPublished - Jan 2014

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems

Fingerprint Dive into the research topics of 'Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions'. Together they form a unique fingerprint.

  • Cite this