Bernoulli distribution

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**Bernoulli**
Probability mass function
Cumulative distribution function
Parameters	$p>0\,$ (real) $q\equiv 1-p\,$
Support	$k=\{0,1\}\,$
Template:Probability distribution/link mass	${\displaystyle \begin{matrix} q & \mbox{for }k=0 \\p~~ & \mbox{for }k=1 \end{matrix} }$
cdf	${\displaystyle \begin{matrix} 0 & \mbox{for }k<0 \\q & \mbox{for }0<k<1\\1 & \mbox{for }k>1 \end{matrix} }$
Mean	$p\,$
Median	N/A
Mode	$\textrm{max}(p,q)\,$
Variance	$pq\,$
Skewness	$\frac{q-p}{\sqrt{pq}}$
Kurtosis	$\frac{6p^2-6p+1}{p(1-p)}$
Entropy	$-q\ln(q)-p\ln(p)\,$
mgf	$q+pe^t\,$
Char. func.	$q+pe^{it}\,$

In probability theory and statistics, the Bernoulli distribution, named after Swiss scientist Jakob Bernoulli, is a discrete probability distribution, which takes value 1 with success probability $p$ and value 0 with failure probability $q=1-p$ . So if X is a random variable with this distribution, we have:

\Pr(X=1) = 1- \Pr(X=0) = p.\!

The probability mass function f of this distribution is

{\displaystyle f(k;p) = \left\{\begin{matrix} p & \mbox {if }k=1, \\ 1-p & \mbox {if }k=0, \\ 0 & \mbox {otherwise.}\end{matrix}\right.}

The expected value of a Bernoulli random variable X is $E\left(X\right)=p$ , and its variance is

\textrm{var}\left(X\right)=p\left(1-p\right).\,

The Bernoulli distribution is a member of the exponential family.

Related distributions[]

If $X_1, \dots, X_n$ are independent, identically distributed random variables, all Bernoulli distributed with success probability p, then $Y = \sum_{k=1}^n X_k \sim \mathrm{Binomial}(n,p)$ (binomial distribution).

Bernoulli distribution

Related distributions[]

See also[]

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