What is decision boundary geometry of Naïve Bayes in log-space?

In log-space, Naïve Bayes produces linear decision boundaries for many feature distributions.

Taking logarithm of posterior transforms multiplicative likelihoods into additive terms. This results in linear decision boundaries when likelihoods are exponential family distributions. For Gaussian NB, boundaries become quadratic in original space but linear in transformed feature space.