SearchOnMath

Shannon's entropy, \(H(X) = -\sum_{x \in \mathbf{X}} p(x) \log p(x)\), measures how informative the mass function \(p\) is about the occurrence of the possible values of variable \(X\).

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

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.