mlsanityData to 7 Oct 2026

Estimating Continuous Distributions in Bayesian Classifiers

arXiv 1302.4964 ↗First posted 20 Feb 2013cs.LG cs.AI stat.MLcited by 53

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Abstract (arXiv)

When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

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Citing papers per year: 2021: 18; 2022: 7; 2023: 6; 2024: 7; 2025: 9; 2026: 6.

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