Fungsi dénsitas probabilitas

Dina matematika, probability density function dipaké keur ngagambarkeun probability distribution di watesan integrals. Lamun probability distribution mibanda densiti f(x), saterusna interval tak terhingga [x, x + dx] mibanda probabiliti f(x) dx. Probability density function bisa ogé ditempo tina versi "smoothed out" histogram: if one empirically méasures values of a variabel acak repéatedly and produces a histogram depicting relative frequencies of output ranges, then this histogram will resemble the random variable's probability density (assuming that the variable is sampled sufficiently often and the output ranges are sufficiently narrow).

Formally, a probability distribution has density f(x) if f(x) is a non-negative Lebesgue-integrable function R → R such that the probability of the interval [a, b] is given by

for any two numbers a and b. This implies that the total integral of f must be 1. Conversely, any non-negative Lebesgue-integrable function with total integral 1 is the probability density of a suitably defined probability distribution.

Contona, sebaran seragam dina interval [0,1] mibanda probabiliti densiti f(x) = 1 keur 0 ≤ x ≤ 1 jeung nol dimamana. Standar sebaran normal mibanda probabiliti densiti

.

Lamun variabel acak X dibérékeun sarta distribusina kaasup kana fungsi probabiliti densiti f(x), mangka nilai ekspektasi X (lamun éta aya) bisa diitung ku

Not every probability distribution has a density function: the distributions of discrete random variables do not; nor does the Cantor distribution, even though it has no discrete component, i.e., does not assign positive probability to any individual point.

A distribution has a density function if and only if its cumulative distribution function F(x) is absolutely continuous. In this case, F is almost everywhere differentiable, and its derivative can be used as probability density. If a probability distribution admits a density, then the probability of every one-point set {a} is zero.

It is a common mistake to think of f(a) as the probability of {a}, but this is incorrect; in fact, f(a) will often be bigger than 1 - consider a random variable with a uniform distribution between 0 and 1/2.

Dua densiti f jeung g for the same distribution can only differ on a set of Lebesgue measure zero.

Tempo oge:
 likelihood
 probability mass function
 exponential family

Rujukan
 Wikipédia basa Inggris, disalin ping 17 Agustus 2004.