Mining and visualising ordinal data with non-parametric continuous BBNs

@article{Hanea2010MiningAV,
  title={Mining and visualising ordinal data with non-parametric continuous BBNs},
  author={Anca M. Hanea and Dorota Kurowicka and Roger M. Cooke and D. A. Ababei},
  journal={Computational Statistics & Data Analysis},
  year={2010},
  volume={54},
  pages={668-687}
}

• Anca M. Hanea, Dorota Kurowicka, +1 author D. A. Ababei
• Published in Computational Statistics & Data Analysis 2010
• DOI:10.1016/j.csda.2008.09.032

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… CONTINUE READING

Save to Library

Create Alert

Cite

View via Publisher