Bernoulli distribution

In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, is the discrete probability distribution of a random variable which takes the value 1 with probability $p$ and the value 0 with probability $q=1-p$. Less formally, it can be thought of as a model for the set of possible outcomes of any single experiment that asks a yes–no question. Such questions lead to outcomes that are Boolean-valued: a single bit whose value is success/yes/true/one with probability p and failure/no/false/zero with probability q. It can be used to represent a (possibly biased) coin toss where 1 and 0 would represent "heads" and "tails", respectively, and p would be the probability of the coin landing on heads (or vice versa where 1 would represent tails and p would be the probability of tails). In particular, unfair coins would have $p\ne 1/2.$

The Bernoulli distribution is a special case of the binomial distribution where a single trial is conducted (so n would be 1 for such a binomial distribution). It is also a special case of the two-point distribution, for which the possible outcomes need not be 0 and 1.

Properties

If $X$ is a random variable with a Bernoulli distribution, then:

$\mathrm{Pr}(X=1)=p=1-\mathrm{Pr}(X=0)=1-q.$

The probability mass function $f$ of this distribution, over possible outcomes k, is

$f(k;p)=\{\begin{array}{cc}p& \text{if }k=1,\\ q=1-p& \text{if }k=0.\end{array}$

This can also be expressed as

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

or as

$f(k;p)=pk+(1-p)(1-k)\text{for }k\in \{0,1\}.$

The Bernoulli distribution is a special case of the binomial distribution with $n=1.$

The kurtosis goes to infinity for high and low values of $p,$ but for $p=1/2$ the two-point distributions including the Bernoulli distribution have a lower excess kurtosis, namely −2, than any other probability distribution.

The Bernoulli distributions for $0\le p\le 1$ form an exponential family.

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

The probability mass distribution function of a Bernoulli experiment along with its corresponding cumulative distribution function.

Mean

The expected value of a Bernoulli random variable $X$ is

$\mathrm{E}[X]=p$

This is because for a Bernoulli distributed random variable $X$ with $\mathrm{Pr}(X=1)=p$ and $\mathrm{Pr}(X=0)=q$ we find

$\mathrm{E}[X]=\mathrm{Pr}(X=1)\cdot 1+\mathrm{Pr}(X=0)\cdot 0=p\cdot 1+q\cdot 0=p.$

Variance

The variance of a Bernoulli distributed $X$ is

$\mathrm{Var}[X]=pq=p(1-p)$

We first find

$\mathrm{E}[X^2]=\mathrm{Pr}(X=1)\cdot 1^2+\mathrm{Pr}(X=0)\cdot 0^2$

$=p\cdot 1^2+q\cdot 0^2=p=\mathrm{E}[X]$

From this follows

$\mathrm{Var}[X]=\mathrm{E}[X^2]-\mathrm{E}[X{]}^2=\mathrm{E}[X]-\mathrm{E}[X{]}^2$

$=p-p^2=p(1-p)=pq$

With this result it is easy to prove that, for any Bernoulli distribution, its variance will have a value inside $[0,1/4]$.

Skewness

The skewness is $\frac{q-p}{\sqrt{pq}}=\frac{1-2p}{\sqrt{pq}}$. When we take the standardized Bernoulli distributed random variable $\frac{X-\mathrm{E}[X]}{\sqrt{\mathrm{Var}[X]}}$ we find that this random variable attains $\frac{q}{\sqrt{pq}}$ with probability $p$ and attains $-\frac{p}{\sqrt{pq}}$ with probability $q$. Thus we get

$\begin{array}{cc}{\gamma }_1& =\mathrm{E}\left[{\left(\frac{X-\mathrm{E}[X]}{\sqrt{\mathrm{Var}[X]}}\right)}^3\right]\\ & =p\cdot {\left(\frac{q}{\sqrt{pq}}\right)}^3+q\cdot {(-\frac{p}{\sqrt{pq}})}^3\\ & =\frac{1}{{\sqrt{pq}}^3}(p{q}^3-q{p}^3)\\ & =\frac{pq}{{\sqrt{pq}}^3}(q^2-p^2)\\ & =\frac{(1-p{)}^2-p^2}{\sqrt{pq}}\\ & =\frac{1-2p}{\sqrt{pq}}=\frac{q-p}{\sqrt{pq}}.\end{array}$

Higher moments and cumulants

The raw moments are all equal because $1^k=1$ and $0^k=0$.

$\mathrm{E}[X^k]=\mathrm{Pr}(X=1)\cdot 1^k+\mathrm{Pr}(X=0)\cdot 0^k=p\cdot 1+q\cdot 0=p=\mathrm{E}[X].$

The central moment of order $k$ is given by

${\mu }_k=(1-p)(-p{)}^k+p(1-p{)}^k.$

The first six central moments are

$\begin{array}{cc}{\mu }_1& =0,\\ {\mu }_2& =p(1-p),\\ {\mu }_3& =p(1-p)(1-2p),\\ {\mu }_4& =p(1-p)(1-3p(1-p)),\\ {\mu }_5& =p(1-p)(1-2p)(1-2p(1-p)),\\ {\mu }_6& =p(1-p)(1-5p(1-p)(1-p(1-p))).\end{array}$

The higher central moments can be expressed more compactly in terms of ${\mu }_2$ and ${\mu }_3$

$\begin{array}{cc}{\mu }_4& ={\mu }_2(1-3{\mu }_2),\\ {\mu }_5& ={\mu }_3(1-2{\mu }_2),\\ {\mu }_6& ={\mu }_2(1-5{\mu }_2(1-{\mu }_2)).\end{array}$

The first six cumulants are

$\begin{array}{cc}{\kappa }_1& =p,\\ {\kappa }_2& ={\mu }_2,\\ {\kappa }_3& ={\mu }_3,\\ {\kappa }_4& ={\mu }_2(1-6{\mu }_2),\\ {\kappa }_5& ={\mu }_3(1-12{\mu }_2),\\ {\kappa }_6& ={\mu }_2(1-30{\mu }_2(1-4{\mu }_2)).\end{array}$

Entropy and Fisher's Information

Entropy

Entropy is a measure of uncertainty or randomness in a probability distribution. For a Bernoulli random variable $X$ with success probability $p$ and failure probability $q=1-p$, the entropy $H(X)$ is defined as:

$\begin{array}{cc}H(X)& ={𝔼}_p\ln (\frac{1}{P(X)})=-[P(X=0)\ln P(X=0)+P(X=1)\ln P(X=1)]\\ H(X)& =-(q\ln q+p\ln p),q=P(X=0),p=P(X=1)\end{array}$

The entropy is maximized when $p=0.5$, indicating the highest level of uncertainty when both outcomes are equally likely. The entropy is zero when $p=0$ or $p=1$, where one outcome is certain.

Fisher's Information

Fisher information measures the amount of information that an observable random variable $X$ carries about an unknown parameter $p$ upon which the probability of $X$ depends. For the Bernoulli distribution, the Fisher information with respect to the parameter $p$ is given by:

$\begin{array}{c}I(p)=\frac{1}{pq}\end{array}$

Proof:

• The Likelihood Function for a Bernoulli random variable$X$ is:

$\begin{array}{c}L(p;X)=p^X(1-p{)}^{1-X}\end{array}$

This represents the probability of observing $X$ given the parameter $p$.

• The Log-Likelihood Function is:

$\begin{array}{c}\ln L(p;X)=X\ln p+(1-X)\ln (1-p)\end{array}$

• The Score Function (the first derivative of the log-likelihood w.r.t. $p$ is:

$\begin{array}{c}\frac{\partial }{\partial p}\ln L(p;X)=\frac{X}{p}-\frac{1-X}{1-p}\end{array}$

• The second derivative of the log-likelihood function is:

$\begin{array}{c}\frac{{\partial }^2}{\partial p^2}\ln L(p;X)=-\frac{X}{p^2}-\frac{1-X}{(1-p{)}^2}\end{array}$

• Fisher information is calculated as the negative expected value of the second derivative of the log-likelihood:

$\begin{array}{c}I(p)=-E[\frac{{\partial }^2}{\partial p^2}\ln L(p;X)]=-(-\frac{p}{p^2}-\frac{1-p}{(1-p{)}^2})=\frac{1}{p(1-p)}=\frac{1}{pq}\end{array}$

It is maximized when $p=0.5$, reflecting maximum uncertainty and thus maximum information about the parameter $p$.

Related distributions

• If $X_1,\dots ,X_n$ are independent, identically distributed (i.i.d.) random variables, all Bernoulli trials with success probability p, then their sum is distributed according to a binomial distribution with parameters n and p:

  $\sum _{k=1}^n{X}_k\sim \mathrm{B}(n,p)$ (binomial distribution).

The Bernoulli distribution is simply $\mathrm{B}(1,p)$, also written as $\mathrm{B}\mathrm{e}\mathrm{r}\mathrm{n}\mathrm{o}\mathrm{u}\mathrm{l}\mathrm{l}\mathrm{i}(p).$

• 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 models the number of independent and identical Bernoulli trials needed to get one success.
• If $Y\sim \mathrm{B}\mathrm{e}\mathrm{r}\mathrm{n}\mathrm{o}\mathrm{u}\mathrm{l}\mathrm{l}\mathrm{i}\left(\frac{1}{2}\right)$, then $2Y-1$ has a Rademacher distribution.

See also

• Bernoulli process, a random process consisting of a sequence of independent Bernoulli trials
• Bernoulli sampling
• Binary entropy function
• Binary decision diagram

Further reading

• Template:Cite book
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External links

• Template:Springer.
• Template:MathWorld
• Interactive graphic: Univariate Distribution Relationships.