Bernoulli distribution

Bernoulli
parameters: 0<p<1, p\in\R
support: k=\{0,1\}\,
pmf: 
    \begin{matrix}
    q=(1-p) & \mbox{for }k=0 \\p~~ & \mbox{for }k=1
    \end{matrix}
cdf: 
    \begin{matrix}
    0 & \mbox{for }k<0 \\q & \mbox{for }0\leq k<1\\1 & \mbox{for }k\geq 1
    \end{matrix}
mean: p\,
median: N/A
mode: \begin{matrix}
0 & \mbox{if } q > p\\
0, 1 & \mbox{if } q=p\\
1 & \mbox{if } q < p
\end{matrix}
variance: pq\,
skewness: \frac{q-p}{\sqrt{pq}}
ex.kurtosis: \frac{6p^2-6p+1}{p(1-p)}
entropy: -q\ln(q)-p\ln(p)\,
mgf: q+pe^t\,
cf: q+pe^{it}\,

In probability theory and statistics, the Bernoulli distribution, named after Swiss scientist Jacob 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) =\!  1 - q = p.\!

The probability mass function f of this distribution is

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

This can also be expressed as

f(k;p) = p^k (1-p)^{1-k}\!\quad \text{for }k\in\{0,1\}.

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 kurtosis goes to infinity for high and low values of p, but for p=1/2 the Bernoulli distribution has a lower kurtosis than any other probability distribution, namely -2.

The Bernoulli distribution is a member of the exponential family.

Related distributions

See also