Intuition for why LDA is a special case of naive Bayes The naive Bayes classifier assumes the regressors to be mutually independent, while linear discriminant analysis (LDA) allows them to be correlated. James et al. "An Introduction to Statistical Learning" (2nd edition, 2021) section 4.5 (bottom of p. 159) claim that LDA is in fact a special case of the naive Bayes classifier (admitting that the fact is not at all obvious -- with which I agree, and hence my question). What is the intuition?

LDA is a special case of a n̶a̶i̶v̶e̶ Bayes classifier.

*

*It is assuming Gaussian distributions

*For different classes, the distributions have the same variance (the same covariance matrix for their distribution with respect to the variables $X$).

Gaussian Naive Bayes classifier is a special case of LDA
If you consider the naive Bayes classifier with the assumption of Gaussian distributions and the same variance for different groups/classes, then you could see this as a special case of LDA. It is like LDA with the restriction that the covariance matrix $\Sigma$ is diagonal.
Other point of view
You might also see the LDA as a pre-treatment step giving you as result one or more components. Then afterward you apply naive Bayes on the components. So LDA can be seen as a special case of naive Bayes in the sense that it is naive Bayes with a pretreatment extracting first LDA components.
