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Predictive Discretization during Model Selection

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by Harald Steck
Citations:1 - 1 self
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BibTeX

@MISC{Steck_predictivediscretization,
    author = {Harald Steck},
    title = {Predictive Discretization during Model Selection},
    year = {}
}

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Abstract

We present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also invariant under monotonic transformations of the continuous space. Our experiments show that the discretization method can substantially impact the resulting graph structure. 1

Citations

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77 Combining location and expression data for principled discovery of genetic regulatory network models - Hartemink, Gifford, et al. - 2002
50 Discretization of continuous attributes while learning Bayesian networks - Friedman, Goldszmidt - 1996
41 A.: Data analysis with Bayesian networks: A bootstrap approach - Friedman, Goldszmidt, et al. - 1999
20 On the Dirichlet prior and Bayesian regularization - Steck, Jaakkola - 2002
16 A multivariate discretization method for learning Bayesian networks from mixed data - Monti, Cooper - 1998
11 A latent variable model for multivariate discretization - Monti, GF - 1999
9 On the Application of the Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks - Friedman, Goldszmidt, et al. - 1999
8 Bias-corrected bootstrap and model uncertainty - Steck, Jaakkola - 2003
6 Semi-)predictive discretization during model selection - Steck, Jaakkola - 2003
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