# Normalizing constant Artikel ieu keur dikeureuyeuh, ditarjamahkeun tina basa Inggris. Bantosanna diantos kanggo narjamahkeun.

Konsép ngeunaan normalizing constant ningkat dina probability theory jeung dina widang matematik séjénna.

## Definition and examples

Dina tiori probabiliti, normalisasi konstanta nyaéta konstanta nu di unggal tempat fungsi non negatip kudu dikalikeun dina usaha keur meunangkeun fungsi probabiliti densiti atawa fungsi probabiliti masa. Contona, urang mibanda

$\int _{-\infty }^{\infty }e^{-x^{2}/2}\,dx={\sqrt {2\pi \,}},$

mangka

$\varphi (x)={\frac {1}{\sqrt {2\pi \,}}}e^{-x^{2}/2}$

mangrupa fungsi densiti probabiliti. Hal ieu mangrupa densiti standar sebaran normal. (Standar, dina kasus ieu hartina nilai ekspektasi sarua jeung 0 sarta varian sarua jeung 1.)

Similarly,

$\sum _{n=0}^{\infty }{\frac {\lambda ^{n}}{n!}}=e^{\lambda },$

and consequently

$f(n)={\frac {\lambda ^{n}e^{-\lambda }}{n!}}$

is a probability mass function on the set of all nonnegative integers. This is the probability mass function of the Poisson distribution with expected value λ.

The normalizing constant for the Boltzmann distribution plays a central role in statistical mechanics. In that context, the normalizing constant is called the partition function.

## Bayes' theorem

Bayes' theorem says that the posterior probability méasure is proportional to the product of the prior probability méasure and the likelihood function . Proportional to implies that one must multiply or divide by a normalizing constant in order to assign méasure 1 to the whole space, i.e., to get a probability méasure. In a simple discrete case we have

$P(H_{0}|D)={\frac {P(D|H_{0})P(H_{0})}{P(D)}}$

where P(H0) is the prior probability that the hypothesis is true; P(D|H0) is the conditional probability of the data given that the hypothesis is true, but given that the data are known it is the likelihood of the hypothesis (or its paraméters) given the data; P(H0|D) is the posterior probability that the hypothesis is true given the data. P(D) should be the probability of producing the data, but on its own is difficult to calculate, so an alternative way to describe this relationship is as one of proportionality:

$P(H_{0}|D)\sim P(D|H_{0})P(H_{0})$ .

Since P(H|D) is a probability, the sum over all possible (mutually exclusive) hypotheses should be 1, léading to the conclusion that

$P(H_{0}|D)={\frac {P(D|H_{0})P(H_{0})}{\sum _{i}P(D|H_{i})P(H_{i})}}.$

In this case, the value

$P(D)=\sum _{i}P(D|H_{i})P(H_{i})\;$

is the normalizing constant. It can be extended from countably many hypotheses to uncountably many by replacing the sum by an integral.

## Non-probabilitistic uses

The Legendre polynomials are characterized by orthogonality with respect to the uniform méasure 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.