

 
Math::GSL::Randist(3) 
User Contributed Perl Documentation 
Math::GSL::Randist(3) 
Math::GSL::Randist  Probability Distributions
use Math::GSL::RNG;
use Math::GSL::Randist qw/:all/;
my $rng = Math::GSL::RNG>new();
my $coinflip = gsl_ran_bernoulli($rng>raw(), .5);
Here is a list of all the functions included in this module. For all sampling
methods, the first argument $r is a raw gsl_rnd structure retrieve by calling
raw() on an Math::GSL::RNG object.
 gsl_ran_bernoulli($r, $p)
 This function returns either 0 or 1, the result of a Bernoulli trial with
probability $p. The probability distribution for a Bernoulli trial is,
p(0) = 1  $p and p(1) = $p. $r is a gsl_rng structure.
 gsl_ran_bernoulli_pdf($k, $p)
 This function computes the probability p($k) of obtaining $k from a
Bernoulli distribution with probability parameter $p, using the formula
given above.
 gsl_ran_beta($r, $a, $b)
 This function returns a random variate from the beta distribution. The
distribution function is, p($x) dx = {Gamma($a+$b) \ Gamma($a) Gamma($b)}
$x**{$a1} (1$x)**{$b1} dx for 0 <= $x <= 1.$r is a gsl_rng
structure.
 gsl_ran_beta_pdf($x, $a, $b)
 This function computes the probability density p($x) at $x for a beta
distribution with parameters $a and $b, using the formula given
above.
 gsl_ran_binomial($k, $p, $n)
 This function returns a random integer from the binomial distribution, the
number of successes in n independent trials with probability $p. The
probability distribution for binomial variates is p($k) = {$n! \ $k!
($n$k)! } $p**$k (1$p)^{$n$k} for 0 <= $k <= $n. Uses Binomial
Triangle Parallelogram Exponential algorithm.
 gsl_ran_binomial_knuth($k, $p, $n)
 Alternative and original implementation for gsl_ran_binomial using Knuth's
algorithm.
 gsl_ran_binomial_tpe($k, $p, $n)
 Same as gsl_ran_binomial.
 gsl_ran_binomial_pdf($k, $p, $n)
 This function computes the probability p($k) of obtaining $k from a
binomial distribution with parameters $p and $n, using the formula given
above.
 gsl_ran_exponential($r, $mu)
 This function returns a random variate from the exponential distribution
with mean $mu. The distribution is, p($x) dx = {1 \ $mu} exp($x/$mu) dx
for $x >= 0. $r is a gsl_rng structure.
 gsl_ran_exponential_pdf($x, $mu)
 This function computes the probability density p($x) at $x for an
exponential distribution with mean $mu, using the formula given
above.
 gsl_ran_exppow($r, $a, $b)
 This function returns a random variate from the exponential power
distribution with scale parameter $a and exponent $b. The distribution is,
p(x) dx = {1 / 2 $a Gamma(1+1/$b)} exp($x/$a**$b) dx for $x >= 0.
For $b = 1 this reduces to the Laplace distribution. For $b = 2 it has the
same form as a gaussian distribution, but with $a = sqrt(2) sigma.
$r is a gsl_rng structure.
 gsl_ran_exppow_pdf($x, $a, $b)
 This function computes the probability density p($x) at $x for an
exponential power distribution with scale parameter $a and exponent $b,
using the formula given above.
 gsl_ran_cauchy($r, $scale)
 This function returns a random variate from the Cauchy distribution with
$scale. The probability distribution for Cauchy random variates is,
p(x) dx = {1 / $scale pi (1 + (x/$$scale)**2) } dx
for x in the range infinity to +infinity. The Cauchy distribution is also
known as the Lorentz distribution. $r is a gsl_rng structure.
 gsl_ran_cauchy_pdf($x, $scale)
 This function computes the probability density p($x) at $x for a Cauchy
distribution with $scale, using the formula given above.
 gsl_ran_chisq($r, $nu)
 This function returns a random variate from the chisquared distribution
with $nu degrees of freedom. The distribution function is, p(x) dx = {1 /
2 Gamma($nu/2) } (x/2)**{$nu/2  1} exp(x/2) dx for $x >= 0. $r is a
gsl_rng structure.
 gsl_ran_chisq_pdf($x, $nu)
 This function computes the probability density p($x) at $x for a
chisquared distribution with $nu degrees of freedom, using the formula
given above.
 gsl_ran_dirichlet($r, $alpha)
 This function returns an array of K (where K = length of $alpha array)
random variates from a Dirichlet distribution of order K1. The
distribution function is
p(\theta_1, ..., \theta_K) d\theta_1 ... d\theta_K =
(1/Z) \prod_{i=1}^K \theta_i^{\alpha_i  1} \delta(1 \sum_{i=1}^K \theta_i) d\theta_1 ... d\theta_K
for theta_i >= 0 and alpha_i > 0. The delta function ensures that \sum
\theta_i = 1. The normalization factor Z is
Z = {\prod_{i=1}^K \Gamma(\alpha_i)} / {\Gamma( \sum_{i=1}^K \alpha_i)}
The random variates are generated by sampling K values from gamma
distributions with parameters a=alpha_i, b=1, and renormalizing. See A.M.
Law, W.D. Kelton, Simulation Modeling and Analysis (1991).
 gsl_ran_dirichlet_pdf($theta, $alpha)
 This function computes the probability density p(\theta_1, ... , \theta_K)
at theta[K] for a Dirichlet distribution with parameters alpha[K], using
the formula given above. $alpha and $theta should be array references of
the same size. Theta should be normalized to sum to 1.
 gsl_ran_dirichlet_lnpdf($theta, $alpha)
 This function computes the logarithm of the probability density
p(\theta_1, ... , \theta_K) for a Dirichlet distribution with parameters
alpha[K]. $alpha and $theta should be array references of the same size.
Theta should be normalized to sum to 1.
 gsl_ran_erlang($r, $scale, $shape)
 Equivalent to gsl_ran_gamma($r, $shape, $scale) where $shape is an
integer.
 gsl_ran_erlang_pdf
 Equivalent to gsl_ran_gamma_pdf($r, $shape, $scale) where $shape is an
integer.
 gsl_ran_fdist($r, $nu1, $nu2)
 This function returns a random variate from the Fdistribution with
degrees of freedom nu1 and nu2. The distribution function is, p(x) dx = {
Gamma(($nu_1 + $nu_2)/2) / Gamma($nu_1/2) Gamma($nu_2/2) }
$nu_1**{$nu_1/2} $nu_2**{$nu_2/2} x**{$nu_1/2  1} ($nu_2 + $nu_1
x)**{$nu_1/2 $nu_2/2} for $x >= 0. $r is a gsl_rng structure.
 gsl_ran_fdist_pdf($x, $nu1, $nu2)
 This function computes the probability density p(x) at x for an
Fdistribution with nu1 and nu2 degrees of freedom, using the formula
given above.
 gsl_ran_flat($r, $a, $b)
 This function returns a random variate from the flat (uniform)
distribution from a to b. The distribution is, p(x) dx = {1 / ($b$a)} dx
if $a <= x < $b and 0 otherwise. $r is a gsl_rng structure.
 gsl_ran_flat_pdf($x, $a, $b)
 This function computes the probability density p($x) at $x for a uniform
distribution from $a to $b, using the formula given above.
 gsl_ran_gamma($r, $shape, $scale)
 This function returns a random variate from the gamma distribution. The
distribution function is,
p(x) dx = {1 \over \Gamma($shape) $scale^$shape} x^{$shape1} e^{x/$scale}
dx for x > 0. Uses MarsagliaTsang method. Can also be called as
gsl_ran_gamma_mt.
 gsl_ran_gamma_pdf($x, $shape, $scale)
 This function computes the probability density p($x) at $x for a gamma
distribution with parameters $shape and $scale, using the formula given
above.
 gsl_ran_gamma($r, $shape, $scale)
 Same as gsl_ran_gamma.
 gsl_ran_gamma_knuth($r, $shape, $scale)
 Alternative implementation for gsl_ran_gamma, using algorithm in Knuth
volume 2.
 gsl_ran_gaussian($r, $sigma)
 This function returns a Gaussian random variate, with mean zero and
standard deviation $sigma. The probability distribution for Gaussian
random variates is, p(x) dx = {1 / sqrt{2 pi $sigma**2}} exp(x**2 / 2
$sigma**2) dx for x in the range infinity to +infinity. $r is a gsl_rng
structure. Uses BoxMueller (polar) method.
 gsl_ran_gaussian_ratio_method($r, $sigma)
 This function computes a Gaussian random variate using the alternative
KindermanMonahanLeva ratio method.
 gsl_ran_gaussian_ziggurat($r, $sigma)
 This function computes a Gaussian random variate using the alternative
MarsagliaTsang ziggurat ratio method. The Ziggurat algorithm is the
fastest available algorithm in most cases. $r is a gsl_rng structure.
 gsl_ran_gaussian_pdf($x, $sigma)
 This function computes the probability density p($x) at $x for a Gaussian
distribution with standard deviation sigma, using the formula given
above.
 gsl_ran_ugaussian($r)
 gsl_ran_ugaussian_ratio_method($r)
 gsl_ran_ugaussian_pdf($x)
 This function computes results for the unit Gaussian distribution. It is
equivalent to the gaussian functions above with a standard deviation of
one, sigma = 1.
 gsl_ran_bivariate_gaussian($r, $sigma_x, $sigma_y, $rho)
 This function generates a pair of correlated Gaussian variates, with mean
zero, correlation coefficient rho and standard deviations $sigma_x and
$sigma_y in the x and y directions. The first value returned is x and the
second y. The probability distribution for bivariate Gaussian random
variates is, p(x,y) dx dy = {1 / 2 pi $sigma_x $sigma_y sqrt{1$rho**2}}
exp ((x**2/$sigma_x**2 + y**2/$sigma_y**2  2 $rho x y/($sigma_x
$sigma_y))/2(1 $rho**2)) dx dy for x,y in the range infinity to
+infinity. The correlation coefficient $rho should lie between 1 and 1.
$r is a gsl_rng structure.
 gsl_ran_bivariate_gaussian_pdf($x, $y, $sigma_x, $sigma_y, $rho)
 This function computes the probability density p($x,$y) at ($x,$y) for a
bivariate Gaussian distribution with standard deviations $sigma_x,
$sigma_y and correlation coefficient $rho, using the formula given
above.
 gsl_ran_gaussian_tail($r, $a, $sigma)
 This function provides random variates from the upper tail of a Gaussian
distribution with standard deviation sigma. The values returned are larger
than the lower limit a, which must be positive. The probability
distribution for Gaussian tail random variates is, p(x) dx = {1 / N($a;
$sigma) sqrt{2 pi sigma**2}} exp( x**2/(2 sigma**2)) dx for x > $a
where N($a; $sigma) is the normalization constant, N($a; $sigma) = (1/2)
erfc($a / sqrt(2 $sigma**2)). $r is a gsl_rng structure.
 gsl_ran_gaussian_tail_pdf($x, $a, $gaussian)
 This function computes the probability density p($x) at $x for a Gaussian
tail distribution with standard deviation sigma and lower limit $a, using
the formula given above.
 gsl_ran_ugaussian_tail($r, $a)
 This functions compute results for the tail of a unit Gaussian
distribution. It is equivalent to the functions above with a standard
deviation of one, $sigma = 1. $r is a gsl_rng structure.
 gsl_ran_ugaussian_tail_pdf($x, $a)
 This functions compute results for the tail of a unit Gaussian
distribution. It is equivalent to the functions above with a standard
deviation of one, $sigma = 1.
 gsl_ran_landau($r)
 This function returns a random variate from the Landau distribution. The
probability distribution for Landau random variates is defined
analytically by the complex integral, p(x) = (1/(2 \pi i))
\int_{ci\infty}^{c+i\infty} ds exp(s log(s) + x s) For numerical purposes
it is more convenient to use the following equivalent form of the
integral, p(x) = (1/\pi) \int_0^\infty dt \exp(t \log(t)  x t) \sin(\pi
t). $r is a gsl_rng structure.
 gsl_ran_landau_pdf($x)
 This function computes the probability density p($x) at $x for the Landau
distribution using an approximation to the formula given above.
 gsl_ran_geometric($r, $p)
 This function returns a random integer from the geometric distribution,
the number of independent trials with probability $p until the first
success. The probability distribution for geometric variates is, p(k) = p
(1$p)^(k1) for k >= 1. Note that the distribution begins with k=1
with this definition. There is another convention in which the exponent
k1 is replaced by k. $r is a gsl_rng structure.
 gsl_ran_geometric_pdf($k, $p)
 This function computes the probability p($k) of obtaining $k from a
geometric distribution with probability parameter p, using the formula
given above.
 gsl_ran_hypergeometric($r, $n1, $n2, $t)
 This function returns a random integer from the hypergeometric
distribution. The probability distribution for hypergeometric random
variates is, p(k) = C(n_1, k) C(n_2, t  k) / C(n_1 + n_2, t) where C(a,b)
= a!/(b!(ab)!) and t <= n_1 + n_2. The domain of k is max(0,tn_2),
..., min(t,n_1). If a population contains n_1 elements of "type
1" and n_2 elements of "type 2" then the hypergeometric
distribution gives the probability of obtaining k elements of "type
1" in t samples from the population without replacement. $r is a
gsl_rng structure.
 gsl_ran_hypergeometric_pdf($k, $n1, $n2, $t)
 This function computes the probability p(k) of obtaining k from a
hypergeometric distribution with parameters $n1, $n2 $t, using the formula
given above.
 gsl_ran_gumbel1($r, $a, $b)
 This function returns a random variate from the Type1 Gumbel
distribution. The Type1 Gumbel distribution function is, p(x) dx = a b
\exp((b \exp(ax) + ax)) dx for \infty < x < \infty. $r is a
gsl_rng structure.
 gsl_ran_gumbel1_pdf($x, $a, $b)
 This function computes the probability density p($x) at $x for a Type1
Gumbel distribution with parameters $a and $b, using the formula given
above.
 gsl_ran_gumbel2($r, $a, $b)
 This function returns a random variate from the Type2 Gumbel
distribution. The Type2 Gumbel distribution function is, p(x) dx = a b
x^{a1} \exp(b x^{a}) dx for 0 < x < \infty. $r is a gsl_rng
structure.
 gsl_ran_gumbel2_pdf($x, $a, $b)
 This function computes the probability density p($x) at $x for a Type2
Gumbel distribution with parameters $a and $b, using the formula given
above.
 gsl_ran_logistic($r, $a)
 This function returns a random variate from the logistic distribution. The
distribution function is, p(x) dx = { \exp(x/a) \over a (1 +
\exp(x/a))^2 } dx for \infty < x < +\infty. $r is a gsl_rng
structure.
 gsl_ran_logistic_pdf($x, $a)
 This function computes the probability density p($x) at $x for a logistic
distribution with scale parameter $a, using the formula given above.
 gsl_ran_lognormal($r, $zeta, $sigma)
 This function returns a random variate from the lognormal distribution.
The distribution function is, p(x) dx = {1 \over x \sqrt{2 \pi \sigma^2} }
\exp((\ln(x)  \zeta)^2/2 \sigma^2) dx for x > 0. $r is a gsl_rng
structure.
 gsl_ran_lognormal_pdf($x, $zeta, $sigma)
 This function computes the probability density p($x) at $x for a lognormal
distribution with parameters $zeta and $sigma, using the formula given
above.
 gsl_ran_logarithmic($r, $p)
 This function returns a random integer from the logarithmic distribution.
The probability distribution for logarithmic random variates is, p(k) =
{1 \over \log(1p)} {(p^k \over k)} for k >= 1. $r is a gsl_rng
structure.
 gsl_ran_logarithmic_pdf($k, $p)
 This function computes the probability p($k) of obtaining $k from a
logarithmic distribution with probability parameter $p, using the formula
given above.
 gsl_ran_multinomial($r, $P, $N)
 This function computes and returns a random sample n[] from the
multinomial distribution formed by N trials from an underlying
distribution p[K]. The distribution function for n[] is,
P(n_1, n_2, ..., n_K) =
(N!/(n_1! n_2! ... n_K!)) p_1^n_1 p_2^n_2 ... p_K^n_K
where (n_1, n_2, ..., n_K) are nonnegative integers with sum_{k=1}^K n_k =
N, and (p_1, p_2, ..., p_K) is a probability distribution with \sum p_i =
1. If the array p[K] is not normalized then its entries will be treated as
weights and normalized appropriately.
Random variates are generated using the conditional binomial method (see
C.S. Davis, The computer generation of multinomial random variates, Comp.
Stat. Data Anal. 16 (1993) 205217 for details).
 gsl_ran_multinomial_pdf($counts, $P)
 This function returns the probability for the multinomial distribution
P(counts[1], counts[2], ..., counts[K]) with parameters p[K].
 gsl_ran_multinomial_lnpdf($counts, $P)
 This function returns the logarithm of the probability for the multinomial
distribution P(counts[1], counts[2], ..., counts[K]) with parameters
p[K].
 gsl_ran_negative_binomial($r, $p, $n)
 This function returns a random integer from the negative binomial
distribution, the number of failures occurring before n successes in
independent trials with probability p of success. The probability
distribution for negative binomial variates is, p(k) = {\Gamma(n + k)
\over \Gamma(k+1) \Gamma(n) } p^n (1p)^k Note that n is not required to
be an integer.
 gsl_ran_negative_binomial_pdf($k, $p, $n)
 This function computes the probability p($k) of obtaining $k from a
negative binomial distribution with parameters $p and $n, using the
formula given above.
 gsl_ran_pascal($r, $p, $n)
 This function returns a random integer from the Pascal distribution. The
Pascal distribution is simply a negative binomial distribution with an
integer value of $n. p($k) = {($n + $k  1)! \ $k! ($n  1)! } $p**$n
(1$p)**$k for $k >= 0. $r is gsl_rng structure
 gsl_ran_pascal_pdf($k, $p, $n)
 This function computes the probability p($k) of obtaining $k from a Pascal
distribution with parameters $p and $n, using the formula given
above.
 gsl_ran_pareto($r, $a, $b)
 This function returns a random variate from the Pareto distribution of
order $a. The distribution function is p($x) dx = ($a/$b) / ($x/$b)^{$a+1}
dx for $x >= $b. $r is a gsl_rng structure
 gsl_ran_pareto_pdf($x, $a, $b)
 This function computes the probability density p(x) at x for a Pareto
distribution with exponent a and scale b, using the formula given
above.
 gsl_ran_poisson($r, $lambda)
 This function returns a random integer from the Poisson distribution with
mean $lambda. $r is a gsl_rng structure. The probability distribution for
Poisson variates is,
p(k) = {$lambda**$k \ $k!} exp($lambda)
for $k >= 0. $r is a gsl_rng structure.
 gsl_ran_poisson_pdf($k, $lambda)
 This function computes the probability p($k) of obtaining $k from a
Poisson distribution with mean $lambda, using the formula given
above.
 gsl_ran_rayleigh($r, $sigma)
 This function returns a random variate from the Rayleigh distribution with
scale parameter sigma. The distribution is, p(x) dx = {x \over \sigma^2}
\exp( x^2/(2 \sigma^2)) dx for x > 0. $r is a gsl_rng structure
 gsl_ran_rayleigh_pdf($x, $sigma)
 This function computes the probability density p($x) at $x for a Rayleigh
distribution with scale parameter sigma, using the formula given
above.
 gsl_ran_rayleigh_tail($r, $a, $sigma)
 This function returns a random variate from the tail of the Rayleigh
distribution with scale parameter $sigma and a lower limit of $a. The
distribution is, p(x) dx = {x \over \sigma^2} \exp ((a^2  x^2) /(2
\sigma^2)) dx for x > a. $r is a gsl_rng structure
 gsl_ran_rayleigh_tail_pdf($x, $a, $sigma)
 This function computes the probability density p($x) at $x for a Rayleigh
tail distribution with scale parameter $sigma and lower limit $a, using
the formula given above.
 gsl_ran_tdist($r, $nu)
 This function returns a random variate from the tdistribution. The
distribution function is, p(x) dx = {\Gamma((\nu + 1)/2) \over \sqrt{\pi
\nu} \Gamma(\nu/2)} (1 + x^2/\nu)^{(\nu + 1)/2} dx for \infty < x
< +\infty.
 gsl_ran_tdist_pdf($x, $nu)
 This function computes the probability density p($x) at $x for a
tdistribution with nu degrees of freedom, using the formula given
above.
 gsl_ran_laplace($r, $a)
 This function returns a random variate from the Laplace distribution with
width $a. The distribution is, p(x) dx = {1 \over 2 a} \exp(x/a) dx for
\infty < x < \infty.
 gsl_ran_laplace_pdf($x, $a)
 This function computes the probability density p($x) at $x for a Laplace
distribution with width $a, using the formula given above.
 gsl_ran_levy($r, $c, $alpha)
 This function returns a random variate from the Levy symmetric stable
distribution with scale $c and exponent $alpha. The symmetric stable
probability distribution is defined by a fourier transform, p(x) = {1
\over 2 \pi} \int_{\infty}^{+\infty} dt \exp(it x  c t^alpha) There
is no explicit solution for the form of p(x) and the library does not
define a corresponding pdf function. For \alpha = 1 the distribution
reduces to the Cauchy distribution. For \alpha = 2 it is a Gaussian
distribution with \sigma = \sqrt{2} c. For \alpha < 1 the tails of the
distribution become extremely wide. The algorithm only works for 0 <
alpha <= 2. $r is a gsl_rng structure
 gsl_ran_levy_skew($r, $c, $alpha, $beta)
 This function returns a random variate from the Levy skew stable
distribution with scale $c, exponent $alpha and skewness parameter $beta.
The skewness parameter must lie in the range [1,1]. The Levy skew stable
probability distribution is defined by a fourier transform, p(x) = {1
\over 2 \pi} \int_{\infty}^{+\infty} dt \exp(it x  c t^alpha (1i
beta sign(t) tan(pi alpha/2))) When \alpha = 1 the term \tan(\pi \alpha/2)
is replaced by (2/\pi)\logt. There is no explicit solution for the form
of p(x) and the library does not define a corresponding pdf function. For
$alpha = 2 the distribution reduces to a Gaussian distribution with $sigma
= sqrt(2) $c and the skewness parameter has no effect. For $alpha
< 1 the tails of the distribution become extremely wide. The symmetric
distribution corresponds to $beta = 0. The algorithm only works for 0 <
$alpha <= 2. The Levy alphastable distributions have the property that
if N alphastable variates are drawn from the distribution p(c, \alpha,
\beta) then the sum Y = X_1 + X_2 + \dots + X_N will also be distributed
as an alphastable variate, p(N^(1/\alpha) c, \alpha, \beta). $r is a
gsl_rng structure
 gsl_ran_weibull($r, $scale, $exponent)
 This function returns a random variate from the Weibull distribution with
$scale and $exponent (aka scale). The distribution function is
p(x) dx = {$exponent \over $scale^$exponent} x^{$exponent1}
\exp((x/$scale)^$exponent) dx
for x >= 0. $r is a gsl_rng structure
 gsl_ran_weibull_pdf($x, $scale, $exponent)
 This function computes the probability density p($x) at $x for a Weibull
distribution with $scale and $exponent, using the formula given
above.
 gsl_ran_dir_2d($r)
 This function returns two values. The first is $x and the second is $y of
a random direction vector v = ($x,$y) in two dimensions. The vector is
normalized such that v^2 = $x^2 + $y^2 = 1. $r is a gsl_rng
structure
 gsl_ran_dir_2d_trig_method($r)
 This function returns two values. The first is $x and the second is $y of
a random direction vector v = ($x,$y) in two dimensions. The vector is
normalized such that v^2 = $x^2 + $y^2 = 1. $r is a gsl_rng
structure
 gsl_ran_dir_3d($r)
 This function returns three values. The first is $x, the second $y and the
third $z of a random direction vector v = ($x,$y,$z) in three dimensions.
The vector is normalized such that v^2 = x^2 + y^2 + z^2 = 1. The method
employed is due to Robert E. Knop (CACM 13, 326 (1970)), and explained in
Knuth, v2, 3rd ed, p136. It uses the surprising fact that the distribution
projected along any axis is actually uniform (this is only true for 3
dimensions).
 gsl_ran_dir_nd (Not yet implemented )
 This function returns a random direction vector v = (x_1,x_2,...,x_n) in n
dimensions. The vector is normalized such that
v^2 = x_1^2 + x_2^2 + ... + x_n^2 = 1.
The method uses the fact that a multivariate Gaussian distribution is
spherically symmetric. Each component is generated to have a Gaussian
distribution, and then the components are normalized. The method is
described by Knuth, v2, 3rd ed, p135136, and attributed to G. W. Brown,
Modern Mathematics for the Engineer (1956).
 gsl_ran_shuffle
 Please use the "shuffle" method in the GSL::RNG module OO
interface.
 gsl_ran_choose
 Please use the "choose" method in the GSL::RNG module OO
interface.
 gsl_ran_sample
 Please use the "sample" method in the GSL::RNG module OO
interface.
 gsl_ran_discrete_preproc
 gsl_ran_discrete($r, $g)
 After gsl_ran_discrete_preproc has been called, you use this function to
get the discrete random numbers. $r is a gsl_rng structure and $g is a
gsl_ran_discrete structure
 gsl_ran_discrete_pdf($k, $g)
 Returns the probability P[$k] of observing the variable $k. Since P[$k] is
not stored as part of the lookup table, it must be recomputed; this
computation takes O(K), so if K is large and you care about the original
array P[$k] used to create the lookup table, then you should just keep
this original array P[$k] around. $r is a gsl_rng structure and $g is a
gsl_ran_discrete structure
 gsl_ran_discrete_free($g)
 Deallocates the gsl_ran_discrete pointed to by g.
You have to add the functions you want to use inside the qw /put_funtion_here /.
You can also write use Math::GSL::Randist qw/:all/; to use all avaible functions of the module.
Other tags are also avaible, here is a complete list of all tags for this module :
 logarithmic
 choose
 exponential
 gumbel1
 exppow
 sample
 logistic
 gaussian
 poisson
 binomial
 fdist
 chisq
 gamma
 hypergeometric
 dirichlet
 negative
 flat
 geometric
 discrete
 tdist
 ugaussian
 rayleigh
 dir
 pascal
 gumbel2
 shuffle
 landau
 bernoulli
 weibull
 multinomial
 beta
 lognormal
 laplace
 erlang
 cauchy
 levy
 bivariate
 pareto
For example the beta tag contains theses functions : gsl_ran_beta, gsl_ran_beta_pdf.
For more informations on the functions, we refer you to the GSL offcial
documentation: <http://www.gnu.org/software/gsl/manual/html_node/>
You might also want to write
use Math::GSL::RNG qw/:all/;
since a lot of the functions of Math::GSL::Randist take as argument a structure
that is created by Math::GSL::RNG. Refer to Math::GSL::RNG documentation to
see how to create such a structure.
Math::GSL::CDF also contains a structure named gsl_ran_discrete_t. An example is
given in the EXAMPLES part on how to use the function related to this
structure.
use Math::GSL::Randist qw/:all/;
print gsl_ran_exponential_pdf(5,2) . "\n";
use Math::GSL::Randist qw/:all/;
my $x = Math::GSL::gsl_ran_discrete_t::new;
Jonathan "Duke" Leto <jonathan@leto.net> and Thierry Moisan
<thierry.moisan@gmail.com>
Copyright (C) 20082013 Jonathan "Duke" Leto and Thierry Moisan
This program is free software; you can redistribute it and/or modify it under
the same terms as Perl itself.
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