Normalizing constant
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics.
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2 Bayes' theorem 3 Non-probabilitistic uses |
Definition and examples
In probability theory, a normalizing constant is a constant by which an everywhere nonnegative function must be multiplied in order to get a probability density function or a probability mass function. For example, we have
Similarly,
Bayes' theorem
In Bayes' theorem says that the product of the prior probability measure and the likelihood function is proportional to the posterior probability measure. Proportional to implies that one must multiply by a normalizing constant in order to assign measure 1 to the whole space, i.e., to get a probability measure. In a simple discrete case we have
- .
Non-probabilitistic uses
The Legendre polynomials are characterized by orthogonality with respect to the uniform measure on the interval [− 1, 1] and the fact that they are normalized so that their value at 1 is 1. The constant by which one multiplies a polynomial in order that its value at 1 will be 1 is a normalizing constant.
