Bernoulli Probability Distribution Family

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A Bernoulli Probability Distribution Family is binomial probability distribution family where n=1.



  • (Wikipedia, 2015) ⇒ Retrieved:2015-6-21.
    • In probability theory and statistics, the Bernoulli distribution, named after Swiss scientist Jacob Bernoulli, is the probability distribution of a random variable which takes value 1 with success probability [math] p [/math] and value 0 with failure probability [math] q=1-p [/math] . It can be used, for example, to represent the toss of a (not necessarily fair) coin, where "1" is defined to mean "heads" and "0" is defined to mean "tails" (or vice versa).

      The Bernoulli distribution is a special case of the two-point distribution, for which the two possible outcomes need not be 0 and 1.


  • (Wikipedia, 2015) ⇒ Retrieved:2015-6-21.
    • If [math] X [/math] is a random variable with this distribution, we have: : [math] Pr(X=1) = 1 - Pr(X=0) = 1 - q = p.\! [/math] A classic example of a Bernoulli experiment is a single toss of a coin. The coin might come up heads with probability [math] p [/math] and tails with probability [math] 1-p [/math] . The experiment is called fair if [math] p=0.5 [/math] , indicating the origin of the terminology in betting (the bet is fair if both possible outcomes have the same probability).

      The probability mass function [math] f [/math] of this distribution, over possible outcomes k, is : [math] f(k;p) = \begin{cases} p & \text{if }k=1, \\[6pt] 1-p & \text {if }k=0.\end{cases} [/math] This can also be expressed as : [math] f(k;p) = p^k (1-p)^{1-k}\!\quad \text{for }k\in\{0,1\}. [/math] The expected value of a Bernoulli random variable [math] X [/math] is : [math] E\left(X\right)=p [/math] and its variance is : [math] \textrm{Var}\left(X\right)=p\left(1-p\right). [/math] The Bernoulli distribution is a special case of the binomial distribution with [math] n = 1 [/math] .[1]

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

      The Bernoulli distributions for [math] 0 \le p \le 1 [/math] form an exponential family.

      The maximum likelihood estimator of [math] p [/math] based on a random sample is the sample mean.