SearchOnMath

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 heat equation, \(\frac{\partial \varnothing}{\partial t} = D \nabla^2 \varnothing\), uses the Laplacian of \(\varnothing\) to express diffusion through a medium.