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The normal density, \(p(x) = \frac{1}{\sigma \sqrt{2\pi}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\), has become important in many applications because of its close relationship to the central limit theorem.

Bayes's theorem, \(P(A|B) = \frac{P(B|A)P(A)}{P(B)}\), relates the two conditional probabilities involving events \(A\) and \(B\).

The conditional probability that an event \(A\) occurs given the occurrence of another event \(B\) is \(P(A|B) = \frac{P(A \cap B)}{P(B)}\).