Mining and visualising ordinal data with non-parametric continuous BBNs
Hanea, AM and Kurowicka, Dorota and Cooke, Roger M and Ababei, DA (2010) Mining and visualising ordinal data with non-parametric continuous BBNs. Computational Statistics and Data Analysis, 54 (3). pp. 668-687.
Full text not available in this repository.Request a copy from the Strathclyde authorAbstract
Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user's standpoint. © 2008 Elsevier B.V. All rights reserved.
| Author(s): | Hanea, AM, Kurowicka, Dorota, Cooke, Roger M and Ababei, DA | Item type: | Article |
|---|---|
| ID code: | 45251 |
| Keywords: | conditional independence statements, continuous variables , graphical modelling, probabilistic modelling, probability density function, Bayesian networks, Risk Management, Management Science and Operations Research |
| Subjects: | Social Sciences > Industries. Land use. Labor > Risk Management |
| Department: | Strathclyde Business School > Management Science |
| Depositing user: | Pure Administrator |
| Date deposited: | 18 Oct 2013 10:59 |
| Last modified: | 12 Jul 2019 02:58 |
| URI: | https://strathprints.strath.ac.uk/id/eprint/45251 |
| Export data: |
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)