# Binomial distribution

Bernoulli distribution

2010 Mathematics Subject Classification: Primary: 60E99 [MSN][ZBL]

The probability distribution of a random variable $X$ which assumes integral values $x = 0 \dots n$ with the probabilities

$${\mathsf P} \{ X=x \} = b _ {x} (n, p) = \ \left ( \begin{array}{c} n \\ x \end{array} \right ) p ^ {x} (1-p) ^ {n-x} ,$$

where $( {} _ {x} ^ {n} )$ is the binomial coefficient, and $p$ is a parameter of the binomial distribution, called the probability of a positive outcome, which can take values in the interval $0 \leq p \leq 1$. The binomial distribution is one of the fundamental probability distributions connected with a sequence of independent trials. Let $Y _ {1} , Y _ {2} \dots$ be a sequence of independent random variables, each one of which may assume only one of the values 1 and 0 with respective probabilities $p$ and $1 - p$( i.e. all $Y _ {i}$ are binomially distributed with $n = 1$). The values of $Y _ {i}$ may be treated as the results of independent trials, with $Y _ {i} = 1$ if the result of the $i$- th trial is "positive" and $Y _ {i} = 0$ if it is "negative" . If the total number of independent trials $n$ is fixed, such a scheme is known as Bernoulli trials, and the total number of positive results,

$$X=Y _ {1} + \dots + Y _ {n} ,\ \ n \geq 1 ,$$

is then binomially distributed with parameter $p$.

The mathematical expectation ${\mathsf E} z ^ {X}$( the generating function of the binomial distribution) for any value of $z$ is the polynomial $[pz + (1 - p)] ^ {n}$, the representation of which by Newton's binomial series has the form

$$b _ {0} + b _ {1} z + \dots + b _ {n} z ^ {n} .$$

(Hence the very name "binomial distribution" .) The moments (cf. Moment) of a binomial distribution are given by the formulas

$${\mathsf E} X = np,$$

$${\mathsf D} X = {\mathsf E} (X-np) ^ {2} = np (1-p),$$

$${\mathsf E}(X-np) ^ {3} = np (1-p) (1 - 2p).$$

The binomial distribution function is defined, for any real $y$, $0 < y < n$, by the formula

$$F (y) = \ {\mathsf P} \{ X \leq u \} = \ \sum _ {x = 0 } ^ { [y] } \left ( \begin{array}{c} n \\ x \end{array} \right ) p ^ {x} (1 - p) ^ {n - x } ,$$

where $[y]$ is the integer part of $y$, and

$$F (y) \equiv \ \frac{1}{B([y] + 1, n - [y]) } \int\limits _ { p } ^ { 1 } t ^ {[y]} (1 - t) ^ {n - [y] - 1 } dt,$$

$B(a, b)$ is Euler's beta-function, and the integral on the right-hand side is known as the incomplete beta-function.

As $n \rightarrow \infty$, the binomial distribution function is expressed in terms of the standard normal distribution function $\Phi$ by the asymptotic formula (the de Moivre–Laplace theorem):

$$F (y) = \Phi \left [ \frac{y - np + 0.5 }{\sqrt {np (1 - p) } } \right ] + R _ {n} (y, p),$$

where

$$R _ {n} (y, p) = O (n ^ {-1/2 } )$$

uniformly for all real $y$. There also exist other, higher order, normal approximations of the binomial distribution.

If the number of independent trials $n$ is large, while the probability $p$ is small, the individual probabilities $b _ {x} (n, p)$ can be approximately expressed in terms of the Poisson distribution:

$$b _ {x} (n, p) = \ \left ( \begin{array}{c} n \\ x \end{array} \right ) p ^ {x} (1 - p) ^ {n - x } \approx \ \frac{(np) ^ {x} }{x!} e ^ {-np } .$$

If $n \rightarrow \infty$ and $0 < c \leq y \leq C$( where $c$ and $C$ are constants), the asymptotic formula

$$F (y) = \ \sum _ {x = 0 } ^ { [y] } \frac{\lambda ^ {x} }{x!} e ^ {- \lambda } + O (n ^ {-2} ),$$

where $\lambda = (2n - [y])p / (2 - p)$, is uniformly valid with respect to all $p$ in the interval $0 < p < 1$.

The multinomial distribution is the multi-dimensional generalization of the binomial distribution.

How to Cite This Entry:
Binomial distribution. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Binomial_distribution&oldid=46067
This article was adapted from an original article by L.N. Bol'shev (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article