On Generalized Naive Bayes with Continuous Features
The Generalized Naive Bayes (GNB) model was introduced for discrete and categorical random variables as an extension of classic Naive Bayes. We now accommodate the GNB framework to continuous explanatory variables. A central result of the paper is that structure learning of the GNB depends only on the pair copulas of the bi-variate marginals. We proved that the GNB structure can be assigned to the basis of a matroid, therefore we give greedy algorithms for finding the optimal GNB structure on the training data, in sense of minimizing Kullback-Leibler divergence. Three cases are considered: joint Gaussian distribution, then a more flexible model where we suppose the dependence structure to...
| Authors | Ábrahám Papp, Botond Szilágyi, Edith Alice Kovács |
|---|---|
| Primary category | stat.ML |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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