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smoothDE: a smooth density estimator with good performance

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  • Adams, Rhys M.

Abstract

Probability density estimation is the problem of inferring an underlying probability from a sampling of points. This study introduces smoothDE, an algorithm that uses Bayesian Field Theory to optimize non-parametric density estimation. smoothDE deterministically finds an optimal density function based on the probability of observed data, subject to a smoothing constraint and its associated prior probability. smoothDE's predicted densities have almost universally lower Kullback-Leibler divergences from simulated Gaussian Mixtures densities when compared to similar Bayesian Field Theory methods and Kernel Density Estimators. smoothDE was even able to outperform a specialized Bayesian Gaussian Mixture density estimator at lower samplings. smoothDE's ability to quickly fit arbitrary densities allowed it to be used as a preprocessing step for classification algorithm, in certain cases boosting classifier performance.

Suggested Citation

  • Adams, Rhys M., 2025. "smoothDE: a smooth density estimator with good performance," OSF Preprints nwyur_v2, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:nwyur_v2
    DOI: 10.31219/osf.io/nwyur_v2
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    References listed on IDEAS

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    1. Jingjing He & Wei Wang & Min Huang & Shaohua Wang & Xuefei Guan, 2021. "Bayesian Inference under Small Sample Sizes Using General Noninformative Priors," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
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    2. Adams, Rhys M., 2025. "smoothDE: a smooth density estimator with good performance," OSF Preprints nwyur_v1, Center for Open Science.

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