Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predcitors) in a learning problem. Maxumum-likelihood training can be done by evaluting a closed-form exporession, which takes linear time, rather tahn by expensive iterative approximation as used for many other typs of classifier. Wikipedia

Random variable can be generated from a good random number generator. If real variables has moved the reality, we could design a future with a good random variables.

A simulation is not real but it can represent the real. It’s because a simulation is an imitation of real situation - it can’t be exact but it can be approximate.