NAG Library Routine Document
G05SBF
1 Purpose
G05SBF generates a vector of pseudorandom numbers taken from a beta distribution with parameters $a$ and $b$.
2 Specification
INTEGER 
N, STATE(*), IFAIL 
REAL (KIND=nag_wp) 
A, B, X(N) 

3 Description
The beta distribution has PDF (probability density function)
One of four algorithms is used to generate the variates depending on the values of
$a$ and
$b$. Let
$\alpha $ be the maximum and
$\beta $ be the minimum of
$a$ and
$b$. Then the algorithms are as follows:
(i) 
if $\alpha <0.5$, Johnk's algorithm is used, see for example Dagpunar (1988). This generates the beta variate as ${u}_{1}^{1/a}/\left(\begin{array}{c}{u}_{1}^{1/a}+{u}_{2}^{1/b}\end{array}\right)$, where ${u}_{1}$ and ${u}_{2}$ are uniformly distributed random variates; 
(ii) 
if $\beta >1$, the algorithm BB given by Cheng (1978) is used. This involves the generation of an observation from a beta distribution of the second kind by the envelope rejection method using a loglogistic target distribution and then transforming it to a beta variate; 
(iii) 
if $\alpha >1$ and $\beta <1$, the switching algorithm given by Atkinson (1979) is used. The two target distributions used are ${f}_{1}\left(x\right)=\beta {x}^{\beta}$ and ${f}_{2}\left(x\right)=\alpha {\left(1x\right)}^{\beta 1}$, along with the approximation to the switching argument of $t=\left(1\beta \right)/\left(\alpha +1\beta \right)$; 
(iv) 
in all other cases, Cheng's BC algorithm (see Cheng (1978)) is used with modifications suggested by Dagpunar (1988). This algorithm is similar to BB, used when $\beta >1$, but is tuned for small values of $a$ and $b$. 
One of the initialization routines
G05KFF (for a repeatable sequence if computed sequentially) or
G05KGF (for a nonrepeatable sequence) must be called prior to the first call to G05SBF.
4 References
Atkinson A C (1979) A family of switching algorithms for the computer generation of beta random variates Biometrika 66 141–5
Cheng R C H (1978) Generating beta variates with nonintegral shape parameters Comm. ACM 21 317–322
Dagpunar J (1988) Principles of Random Variate Generation Oxford University Press
Hastings N A J and Peacock J B (1975) Statistical Distributions Butterworth
5 Arguments
 1: $\mathrm{N}$ – INTEGERInput

On entry: $n$, the number of pseudorandom numbers to be generated.
Constraint:
${\mathbf{N}}\ge 0$.
 2: $\mathrm{A}$ – REAL (KIND=nag_wp)Input

On entry: $a$, the parameter of the beta distribution.
Constraint:
${\mathbf{A}}>0.0$.
 3: $\mathrm{B}$ – REAL (KIND=nag_wp)Input

On entry: $b$, the parameter of the beta distribution.
Constraint:
${\mathbf{B}}>0.0$.
 4: $\mathrm{STATE}\left(*\right)$ – INTEGER arrayCommunication Array

Note: the actual argument supplied
must be the array
STATE supplied to the initialization routines
G05KFF or
G05KGF.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
 5: $\mathrm{X}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayOutput

On exit: the $n$ pseudorandom numbers from the specified beta distribution.
 6: $\mathrm{IFAIL}$ – INTEGERInput/Output

On entry:
IFAIL must be set to
$0$,
$1\text{ or}1$. If you are unfamiliar with this argument you should refer to
Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{ or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{ or}\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit:
${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
${\mathbf{IFAIL}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
 ${\mathbf{IFAIL}}=1$

On entry, ${\mathbf{N}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{N}}\ge 0$.
 ${\mathbf{IFAIL}}=2$

On entry, ${\mathbf{A}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{A}}>0.0$.
 ${\mathbf{IFAIL}}=3$

On entry, ${\mathbf{B}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{B}}>0.0$.
 ${\mathbf{IFAIL}}=4$

On entry,
STATE vector has been corrupted or not initialized.
 ${\mathbf{IFAIL}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 3.9 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{IFAIL}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 3.8 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{IFAIL}}=999$
Dynamic memory allocation failed.
See
Section 3.7 in How to Use the NAG Library and its Documentation for further information.
7 Accuracy
Not applicable.
8 Parallelism and Performance
G05SBF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementationspecific information.
To generate an observation, $y$, from the beta distribution of the second kind from an observation, $x$, generated by G05SBF the transformation, $y=x/\left(1x\right)$, may be used.
10 Example
This example prints a set of five pseudorandom numbers from a beta distribution with parameters
$a=2.0$ and
$b=2.0$, generated by a single call to G05SBF, after initialization by
G05KFF.
10.1 Program Text
Program Text (g05sbfe.f90)
10.2 Program Data
Program Data (g05sbfe.d)
10.3 Program Results
Program Results (g05sbfe.r)