
Embedded Bayesian Network Classifiers
Lowdimensional probability models for local distribution functions in a...
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Learning Discrete Bayesian Networks from Continuous Data
Real data often contains a mixture of discrete and continuous variables,...
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Combinatorial Optimization by Learning and Simulation of Bayesian Networks
This paper shows how the Bayesian network paradigm can be used in order ...
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Structure Variability in Bayesian Networks
The structure of a Bayesian network encodes most of the information abou...
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Lower Bound Bayesian Networks  An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks
We present a new method to propagate lower bounds on conditional probabi...
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Interpolating Conditional Density Trees
Joint distributions over many variables are frequently modeled by decomp...
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Mixture Approximations to Bayesian Networks
Structure and parameters in a Bayesian network uniquely specify the prob...
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Generalizing Tree Probability Estimation via Bayesian Networks
Probability estimation is one of the fundamental tasks in statistics and machine learning. However, standard methods for probability estimation on discrete objects do not handle object structure in a satisfactory manner. In this paper, we derive a general Bayesian network formulation for probability estimation on leaflabeled trees that enables flexible approximations which can generalize beyond observations. We show that efficient algorithms for learning Bayesian networks can be easily extended to probability estimation on this challenging structured space. Experiments on both synthetic and real data show that our methods greatly outperform the current practice of using the empirical distribution, as well as a previous effort for probability estimation on trees.
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