Editing Binomial distribution (section)

=== Estimation of parameters ===
{{see also|Beta distribution#Bayesian inference}}

When {{math|''n''}} is known, the parameter {{math|''p''}} can be estimated using the proportion of successes:
: <math> \widehat{p} = \frac{x}{n}.</math>
This estimator is found using [[maximum likelihood estimator]] and also the [[method of moments (statistics)|method of moments]]. This estimator is [[Bias of an estimator|unbiased]] and uniformly with [[Minimum-variance unbiased estimator|minimum variance]], proven using [[Lehmann–Scheffé theorem]], since it is based on a [[minimal sufficient]] and [[Completeness (statistics)|complete]] statistic (i.e.: {{math|''x''}}). It is also [[Consistent estimator|consistent]] both in probability and in [[Mean squared error|MSE]]. This statistic is [[Asymptotic distribution|asymptotically]] [[normal distribution|normal]] thanks to the [[central limit theorem]], because it is the same as taking the [[arithmetic mean|mean]] over Bernoulli samples.  It has a variance of <math> var(\widehat{p}) = \frac{p(1-p)}{n}</math>, a property which is used in various ways, such as in [[Binomial_proportion_confidence_interval#Wald_interval|Wald's confidence intervals]].

A closed form [[Bayes estimator]] for {{math|''p''}} also exists when using the [[Beta distribution]] as a [[Conjugate prior|conjugate]] [[prior distribution]]. When using a general <math>\operatorname{Beta}(\alpha, \beta)</math> as a prior, the [[Bayes estimator#Posterior mean|posterior mean]] estimator is:
: <math> \widehat{p}_b = \frac{x+\alpha}{n+\alpha+\beta}.</math>
The Bayes estimator is [[Asymptotic efficiency (Bayes)|asymptotically efficient]] and as the sample size approaches infinity ({{math|''n'' → ∞}}), it approaches the [[Maximum likelihood estimation|MLE]] solution.<ref>{{Cite journal |last=Wilcox |first=Rand R. |date=1979 |title=Estimating the Parameters of the Beta-Binomial Distribution |url=http://journals.sagepub.com/doi/10.1177/001316447903900302 |journal=Educational and Psychological Measurement |language=en |volume=39 |issue=3 |pages=527–535 |doi=10.1177/001316447903900302 |s2cid=121331083 |issn=0013-1644}}</ref> The Bayes estimator is [[Bias of an estimator|biased]] (how much depends on the priors),  [[Bayes estimator#Admissibility|admissible]] and [[Consistent estimator|consistent]] in probability. Using the Bayesian estimator with the Beta distribution can be used with [[Thompson sampling]].

For the special case of using the [[standard uniform distribution]] as a [[non-informative prior]], <math>\operatorname{Beta}(\alpha=1, \beta=1) = U(0,1)</math>, the posterior mean estimator becomes:
:<math> \widehat{p}_b = \frac{x+1}{n+2}.</math>
(A [[Bayes estimator#Posterior mode|posterior mode]] should just lead to the standard estimator.) This method is called the [[rule of succession]], which was introduced in the 18th century by [[Pierre-Simon Laplace]].

When relying on [[Jeffreys prior]], the prior is <math>\operatorname{Beta}(\alpha=\frac{1}{2}, \beta=\frac{1}{2})</math>,<ref>Marko Lalovic (https://stats.stackexchange.com/users/105848/marko-lalovic), Jeffreys prior for binomial likelihood, URL (version: 2019-03-04): https://stats.stackexchange.com/q/275608</ref> which leads to the estimator:
: <math> \widehat{p}_{Jeffreys} = \frac{x+\frac{1}{2}}{n+1}.</math>

When estimating {{math|''p''}} with very rare events and a small {{math|''n''}} (e.g.: if {{math|1=''x'' = 0}}), then using the standard estimator leads to <math> \widehat{p} = 0,</math> which sometimes is unrealistic and undesirable. In such cases there are various alternative estimators.<ref>{{cite journal |last=Razzaghi |first=Mehdi |title=On the estimation of binomial success probability with zero occurrence in sample |journal=Journal of Modern Applied Statistical Methods |volume=1 |issue=2 |year=2002 |pages=326–332 |doi=10.22237/jmasm/1036110000 |doi-access=free }}</ref> One way is to use the Bayes estimator <math> \widehat{p}_b</math>, leading to:
: <math> \widehat{p}_b = \frac{1}{n+2}.</math>
Another method is to use the upper bound of the [[confidence interval]] obtained using the [[Rule of three (statistics)|rule of three]]:
: <math> \widehat{p}_{\text{rule of 3}} = \frac{3}{n}.</math>