<ul data-eligibleForWebStory="true"><li>This paper introduces a new type of probabilistic semiparametric model that utilizes data binning to improve computational efficiency in nonparametric distributions.</li><li>Two new conditional probability distributions, sparse binned kernel density estimation and Fourier kernel density estimation, are developed for the binned semiparametric Bayesian networks.</li><li>The models address the curse of dimensionality by employing sparse tensors and limiting the number of parent nodes in conditional probability calculations.</li><li>Experiments with synthetic data and datasets from the UCI Machine Learning repository show that the binned semiparametric Bayesian networks achieve similar performance to non-binned models in terms of structural learning and log-likelihood estimations, but with significantly higher speed.</li></ul>

Binned semiparametric Bayesian networks

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