If you had to describe a Na$$ve Bayes theorem, which of the following would apply?

a$.$

Models the linear relationship between a scalar$-$dependent variable $\text{'}$y$\text{'}$ and one or more explanatory variables $($or independent variables$)\text{'}$x$\text{'}$

b$.$

It is particularly suited when the dimensionality of the inputs is high

c$.$

The Classifiers features assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature

d$.$

Prior probabilities are based on previous experience