Margaritis, Dimitris (2004) Distribution-Free Learning of Graphical Model Structure in Continuous Domains. Technical Report TR-ISU-CS-04-06, Computer Science, Iowa State University.
In this paper we present a probabilistic non-parametric
conditional independence test of $X$ and $Y$ given a third
variable $Z$ in domains where $X$, $Y$, and $Z$ are continuous.
This test can be used for the induction of the structure of
a graphical model (such as a Bayesian or Markov network) from
experimental data. We also provide an effective method for
calculating it from data. We show that our method works well in
practice on artificial benchmark data sets constructed from a
diverse set of functions. We also demonstrate learning of the
structure of a graphical model in a continuous domain from
real-world data, to our knowledge for the first time using
independence-based methods and without any distributional
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