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 language | English |
---|---|
Pages (from-to) | 139-155 |
Number of pages | 17 |
Journal | Computational Management Science |
Volume | 11 |
Issue number | 1 |
Early online date | 30 Oct 2012 |
DOIs | |
Publication status | Published - Jan 2014 |
ASJC Scopus subject areas
- Management Information Systems
- Information Systems