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

Euler's formula for convex polyhedra is a function of their number of faces (\(F\)), edges (\(E\)), and vertices (\(V\)), stating that \(F-E+V=2\).

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.