Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Management Science
Journal of Multivariate Analysis
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Measuring stochastic dependence using φ -divergence
Journal of Multivariate Analysis
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Distribution-free learning of Bayesian network structure in continuous domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning Bayesian networks: a unification for discrete and Gaussian domains
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Editorial: Second special issue on statistical algorithms and software
Computational Statistics & Data Analysis
A PC algorithm variation for ordinal variables
Computational Statistics
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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.