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Bernoulli distribution : ウィキペディア英語版
Bernoulli distribution

|
kurtosis =\frac|
entropy =-q\ln(q)-p\ln(p)\,|
mgf =q+pe^t\,|
char =q+pe^\,|
pgf =q+pz\,|
fisher = \frac |
}}
In probability theory and statistics, the Bernoulli distribution, named after Swiss scientist Jacob Bernoulli,〔James Victor Uspensky: ''Introduction to Mathematical Probability'', McGraw-Hill, New York 1937, page 45〕 is the probability distribution of a random variable which takes the value 1 with success probability of p and the value 0 with failure probability of q=1-p. It can be used to represent a coin toss where 1 and 0 would represent "head" and "tail" (or vice versa), respectively. In particular, unfair coins would have p \neq 0.5.
The Bernoulli distribution is a special case of the two-point distribution, for which the two possible outcomes need not be 0 and 1.
==Properties==

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, over possible outcomes ''k'', is
: f(k;p) = \begin p & \textk=1, \\()
1-p & \text k=0.\end
This can also be expressed as
:f(k;p) = p^k (1-p)^\!\quad \textk\in\.
The Bernoulli distribution is a special case of the binomial distribution with n = 1.〔McCullagh and Nelder (1989), Section 4.2.2.〕
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 than any other probability distribution, namely −2.
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.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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