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Beta-binomial distribution

In probability theory and statistics, the beta-binomial distribution is a family of discrete probability distributions on a finite support of non-negative integers arising when the probability of success in each of a fixed or known number of Bernoulli trials is either unknown or random. The beta-binomial distribution is the binomial distribution in which…

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CDF
{ 0 , x < 0 ( n x ) B ( x + α , n − x + β ) B ( α , β ) 3 F 2 ( a ; b ; x ) , 0 ≤ x < n 1 , x ≥ n {\displaystyle {\begin{cases}0,&x<0\\{\binom {n}{x}}{\tfrac {\mathrm {B} (x+\al…
CF
2 F 1 ( − n , α ; α + β ; 1 − e i t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{it})\!}
Excess kurtosis
See text
Mean
n α α + β {\displaystyle {\frac {n\alpha }{\alpha +\beta }}\!}
MGF
2 F 1 ( − n , α ; α + β ; 1 − e t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{t})\!} where 2 F 1 {\displaystyle _{2}F_{1}} is the hypergeometric function
Notation
B e t a B i n ( n , α , β ) {\displaystyle \mathrm {BetaBin} (n,\alpha ,\beta )}

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Beta-binomial distribution

Nodes52
Edges51
Triples47
Avg. degree1.96
Density0.038462
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Beta-binomial distribution

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related to External links · 11
Beta-binomial distribution → Archived, Beta-binomial, Foundry Java, Interactive, Matlab, Pólya, Sandia National Labs Cognitive, Univariate Distribution RelationshipsBeta-binomial, Using, VGAM, Wayback Machine
related to Maximum likelihood estimation · 8
Beta-binomial distribution → Beta, Maximum, Minka, Pólya, The, There, VGAM, While
related to Role in Bayesian statistics · 8
Beta-binomial distribution → After, Bayesian, Bernoulli, Beta, If, Let, Suppose, The
related to As an urn model · 6
Beta-binomial distribution → By, If, Likewise, Pólya, Specifically, The
is a · 2
Beta-binomial distribution → binomial distribution in which the probability of success at each of n trials is not fixed but randomly drawn from a beta distribution, family of discrete probability distributions on a finite support of non-negative integers arising when the probability of success in each of a fixed or known number of Bernoulli…
CDF · 1
Beta-binomial distribution → { 0 , x 0 ( n x ) B ( x + α , n − x + β ) B ( α , β ) 3 F 2 ( a ; b ; x ) , 0 ≤ x n 1 , x ≥ n {\displaystyle {\begin{cases}0,&x0\\{\binom {n}{x}}{\tfrac {\mathrm {B} (x+\al…
CF · 1
Beta-binomial distribution → 2 F 1 ( − n , α ; α + β ; 1 − e i t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{it})\!}
Excess kurtosis · 1
Beta-binomial distribution → See text
Mean · 1
Beta-binomial distribution → n α α + β {\displaystyle {\frac {n\alpha }{\alpha +\beta }}\!}
MGF · 1
Beta-binomial distribution → 2 F 1 ( − n , α ; α + β ; 1 − e t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{t})\!} where 2 F 1 {\displaystyle _{2}F_{1}} is the hypergeometric function

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distribution displaystyle beta beta-binomial binomial mathrm sim random betabin probability data distributions alpha urn estimates frac trials statistics bernoulli known

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SubjectPredicateObjectConfidenceSrc
Beta-binomial distributionCDF{ 0 , x < 0 ( n x ) B ( x + α , n − x + β ) B ( α , β ) 3 F 2 ( a ; b ; x ) , 0 ≤ x < n 1 , x ≥ n {\displaystyle {\begin{cases}0,&x<0\\{\binom {n}{x}}{\tfrac {\mathrm {B} (x+\al…1.00infobox
Beta-binomial distributionCF2 F 1 ( − n , α ; α + β ; 1 − e i t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{it})\!}1.00infobox
Beta-binomial distributionExcess kurtosisSee text1.00infobox
Beta-binomial distributionMeann α α + β {\displaystyle {\frac {n\alpha }{\alpha +\beta }}\!}1.00infobox
Beta-binomial distributionMGF2 F 1 ( − n , α ; α + β ; 1 − e t ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-e^{t})\!} where 2 F 1 {\displaystyle _{2}F_{1}} is the hypergeometric function1.00infobox
Beta-binomial distributionNotationB e t a B i n ( n , α , β ) {\displaystyle \mathrm {BetaBin} (n,\alpha ,\beta )}1.00infobox
Beta-binomial distributionParametersn ∈ N0 — number of trials α > 0 {\displaystyle \alpha >0} (real) β > 0 {\displaystyle \beta >0} (real)1.00infobox
Beta-binomial distributionPGF2 F 1 ( − n , α ; α + β ; 1 − z ) {\displaystyle _{2}F_{1}(-n,\alpha ;\alpha +\beta ;1-z)\!}1.00infobox
Beta-binomial distributionPMF( n x ) B ( x + α , n − x + β ) B ( α , β ) {\displaystyle {\binom {n}{x}}{\frac {\mathrm {B} (x+\alpha ,n-x+\beta )}{\mathrm {B} (\alpha ,\beta )}}\!} where B ( x , y ) = Γ ( x…1.00infobox
Beta-binomial distributionSkewness( α + β + 2 n ) ( β − α ) ( α + β + 2 ) 1 + α + β n α β ( n + α + β ) {\displaystyle {\tfrac {(\alpha +\beta +2n)(\beta -\alpha )}{(\alpha +\beta +2)}}{\sqrt {\tfrac {1+\alpha +…1.00infobox
Beta-binomial distributionSupportx ∈ { 0, …, n }1.00infobox
Beta-binomial distributionVariancen α β ( α + β + n ) ( α + β ) 2 ( α + β + 1 ) {\displaystyle {\frac {n\alpha \beta (\alpha +\beta +n)}{(\alpha +\beta )^{2}(\alpha +\beta +1)}}\!}1.00infobox
Beta-binomial distributionis afamily of discrete probability distributions on a finite support of non-negative integers arising when the probability of success in each of a fixed or known number of Bernoulli…0.90text
Beta-binomial distributionis abinomial distribution in which the probability of success at each of n trials is not fixed but randomly drawn from a beta distribution0.90text

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