Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions
Data(s) |
01/01/2014
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Direitos |
info:eu-repo/semantics/restrictedAccess |
Fonte |
Marshall , A H & Shaw , B 2014 , ' Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions ' Computational Management Science , vol 11 , no. 1 , pp. 139-155 . DOI: 10.1007/s10287-012-0157-z |
Palavras-Chave | #/dk/atira/pure/subjectarea/asjc/1400/1404 #Management Information Systems #/dk/atira/pure/subjectarea/asjc/1700/1710 #Information Systems |
Tipo |
article |