Beta - Maple Help

Statistics[Distributions]

 Beta
 beta distribution

 Calling Sequence 'Beta'(nu, omega) BetaDistribution(nu, omega)

Parameters

 nu - first shape parameter omega - second shape parameter

Description

 • The beta distribution is a continuous probability distribution with probability density function given by:

$f\left(t\right)=\left\{\begin{array}{cc}0& t<0\\ \frac{{t}^{-1+\mathrm{\nu }}{\left(1-t\right)}^{-1+\mathrm{\omega }}}{\mathrm{Β}\left(\mathrm{\nu },\mathrm{\omega }\right)}& t<1\\ 0& \mathrm{otherwise}\end{array}\right\$

 subject to the following conditions:

$0<\mathrm{\nu },0<\mathrm{\omega }$

 • The beta distribution is related to the independent Gamma variates Gamma(1,nu) and Gamma(1,omega) by the formula Beta(nu,omega) ~ Gamma(1,nu)/(Gamma(1,nu)+Gamma(1,omega)).
 • Note that the Beta(a, b) returns the value of the Beta function with parameters a and b, so in order to define a Beta random variable one should use the unevaluated name 'Beta'. In 2D math notation, the capital letter $\mathrm{Β}$ looks like a capital letter $B$, but the two are different in Maple.

Examples

 > $\mathrm{with}\left(\mathrm{Statistics}\right):$

The following is invalid.

 > $\mathrm{RandomVariable}\left(\mathrm{Β}\left(1,2\right)\right)$

Alternatives are:

 > $\mathrm{RandomVariable}\left('\mathrm{Β}'\left(1,2\right)\right)$
 ${\mathrm{_R}}$ (1)

and

 > $\mathrm{RandomVariable}\left(\mathrm{BetaDistribution}\left(1,2\right)\right)$
 ${\mathrm{_R0}}$ (2)
 > $X≔\mathrm{RandomVariable}\left('\mathrm{Β}'\left(\mathrm{ν},\mathrm{ω}\right)\right):$
 > $\mathrm{PDF}\left(X,u\right)$
 $\left\{\begin{array}{cc}{0}& {u}{<}{0}\\ \frac{{{u}}^{{-}{1}{+}{\mathrm{\nu }}}{}{\left({1}{-}{u}\right)}^{{-}{1}{+}{\mathrm{\omega }}}}{{\mathrm{Β}}{}\left({\mathrm{\nu }}{,}{\mathrm{\omega }}\right)}& {u}{<}{1}\\ {0}& {\mathrm{otherwise}}\end{array}\right\$ (3)
 > $\mathrm{PDF}\left(X,0.5\right)$
 $\frac{{{0.5}}^{{-}{1.}{+}{\mathrm{\nu }}}{}{{0.5}}^{{-}{1.}{+}{\mathrm{\omega }}}}{{\mathrm{Β}}{}\left({\mathrm{\nu }}{,}{\mathrm{\omega }}\right)}$ (4)
 > $\mathrm{Mean}\left(X\right)$
 $\frac{{\mathrm{\nu }}}{{\mathrm{\nu }}{+}{\mathrm{\omega }}}$ (5)
 > $\mathrm{Variance}\left(X\right)$
 $\frac{{\mathrm{\nu }}{}{\mathrm{\omega }}}{{\left({\mathrm{\nu }}{+}{\mathrm{\omega }}\right)}^{{2}}{}\left({\mathrm{\nu }}{+}{\mathrm{\omega }}{+}{1}\right)}$ (6)

References

 Evans, Merran; Hastings, Nicholas; and Peacock, Brian. Statistical Distributions. 3rd ed. Hoboken: Wiley, 2000.
 Johnson, Norman L.; Kotz, Samuel; and Balakrishnan, N. Continuous Univariate Distributions. 2nd ed. 2 vols. Hoboken: Wiley, 1995.
 Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998.  Vol. 1: Distribution Theory.