Bernoulli distribution

Bernoulli

Parameters	$0<p<1, p\in\R$
Support	$k=\{0,1\}\,$
PMF	$\begin{cases} q=(1-p) & \text{for }k=0 \\ p & \text{for }k=1 \end{cases}$
CDF	$\begin{cases} 0 & \text{for }k<0 \\ q & \text{for }0\leq k<1 \\ 1 & \text{for }k\geq 1 \end{cases}$
Mean	$p\,$
Median	N/A
Mode	$\begin{cases} 0 & \text{if } q > p\\ 0, 1 & \text{if } q=p\\ 1 & \text{if } q < p \end{cases}$
Variance	$p(1-p)\,$
Skewness	$\frac{q-p}{\sqrt{pq}}$
Ex. kurtosis	$\frac{1-6pq}{pq}$
Entropy	$-q\ln(q)-p\ln(p)\,$
MGF	$q+pe^t\,$
CF	$q+pe^{it}\,$
PGF	$q+pz\,$

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:

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

A classical example of a Bernoulli experiment is a single toss of a coin. The coin might come up heads with probability p and tails with probability 1-p. The experiment is called fair if p=0.5, indicating the origin of the terminology in betting (the bet is fair if both possible outcomes have the same probability).

The probability mass function f of this distribution is

$f(k;p) = \begin{cases} p & \text{if }k=1, \\[6pt] 1-p & \text {if }k=0.\end{cases}$

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 above can be derived from the Bernoulli distribution as a special case of the Binomial distribution .

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.

The maximum likelihood estimator of p based on a random sample is the sample mean.

Related distributions

• If $X_1,\dots,X_n$ are independent, identically distributed (i.i.d.) random variables, all Bernoulli distributed with success probability p, then $Y = \sum_{k=1}^n X_k \sim \mathrm{Binomial}(n,p)$ (binomial distribution). The Bernoulli distribution is simply $\mathrm{Binomial}(1,p)$.
• The categorical distribution is the generalization of the Bernoulli distribution for variables with any constant number of discrete values.
• The Beta distribution is the conjugate prior of the Bernoulli distribution.
• The geometric distribution is the number of Bernoulli trials needed to get one success.

See also

• Bernoulli trial
• Bernoulli process
• Bernoulli sampling
• Binary entropy function

References

• McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5. 

• Johnson, N.L., Kotz, S., Kemp A. (1993) Univariate Discrete Distributions (2nd Edition). Wiley. ISBN 0-471-54897-9

External links

• Weisstein, Eric W., "Bernoulli Distribution" from MathWorld.