@inproceedings{5253,
  title = {Distribution-free Learning of Bayesian Network Structure},
  journal = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008},
  booktitle = {ECML PKDD 2008},
  abstract = {We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains. Even mixed continuous-categorical domains and structures containing vectorial variables can be handled. We address the problem by developing a non-parametric conditional independence test based on the so-called kernel dependence measure, which can be readily used by any existing independence-based BN structure learning algorithm. We demonstrate the structure learning of graphical models in continuous and mixed domains from real-world data without distributional assumptions. We also experimentally show that our test is a good alternative, in particular in case of small sample sizes, compared to existing tests, which can only be used in purely categorical or continuous domains.},
  pages = {423-439},
  editors = {Daelemans, W. , B. Goethals, K. Morik},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2008},
  slug = {5253},
  author = {Sun, X.},
  month_numeric = {9}
}