# Generating Random Numbers/de

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Random numbers are important resources for scientific applications, education, game development and visualization.

The standard RTL function `random` generates random numbers that fulfill a uniform distribution. Uniformly distributed random numbers are not useful for every application. In order to create random numbers of other distributions special algorithms are necessary.

## Normal (Gaussian) Distribution

One of the more common algorithms to produce normally distributed random numbers from uniformly distributed random numbers is the Box-Müller approach. The following function calculates Gaussian-distributed random numbers:

``` function rnorm (mean, sd: real): real;
{Calculates Gaussian random numbers according to the Box-Müller approach}
var
u1, u2: real;
begin
u1 := random;
u2 := random;
rnorm := mean * abs(1 + sqrt(-2 * (ln(u1))) * cos(2 * pi * u2) * sd);
end;```

The same algorithm is used by the randg randg function from the RTL math unit:

`function randg(mean,stddev: float): float;`

## Exponential Distribution

An exponential distribution occurs frequently in real-world problems. A classical example is the distribution of waiting times between independent Poisson-random events, e.g. the radioactive decay of nuclei [Press et al. 1989].

The following function delivers a single real random number out of an exponential distribution. Rate is the inverse of the mean and the constant RESOLUTION determines the granularity of generated random numbers.

```function randomExp(a, rate: real): real;
const
RESOLUTION = 1000;
var
unif: real;
begin
if rate = 0 then
randomExp := NaN
else
begin
repeat
unif := random(RESOLUTION) / RESOLUTION;
until unif <> 0;
randomExp := a - rate * ln(unif);
end;
end;```

## Gamma Distribution

The gamma distribution is a two-parameter family of continuous random distributions. It is a generalization of both the exponential distribution and the Erlang distribution. Possible applications of the gamma distribution include modelling and simulation of waiting lines, or queues, and actuarial science.

The following function delivers a single real random number out of a gamma distribution. The shape of the distribution is defined by the parameters a, b and c. The function makes use of the function randomExp as defined above.

```function randomGamma(a, b, c: real): real;
const
RESOLUTION = 1000;
T = 4.5;
D = 1 + ln(T);
var
unif: real;
A2, B2, C2, Q, p, y: real;
p1, p2, v, w, z: real;
found: boolean;
begin
A2 := 1 / sqrt(2 * c - 1);
B2 := c - ln(4);
Q := c + 1 / A2;
C2 := 1 + c / exp(1);
found := False;
if c < 1 then
begin
repeat
repeat
unif := random(RESOLUTION) / RESOLUTION;
until unif > 0;
p := C2 * unif;
if p > 1 then
begin
repeat
unif := random(RESOLUTION) / RESOLUTION;
until unif > 0;
y := -ln((C2 - p) / c);
if unif <= power(y, c - 1) then
begin
randomGamma := a + b * y;
found := True;
end;
end
else
begin
y := power(p, 1 / c);
if unif <= exp(-y) then
begin
randomGamma := a + b * y;
found := True;
end;
end;
until found;
end
else if c = 1 then
{ Gamma distribution becomes exponential distribution, if c = 1 }
begin
randomGamma := randomExp(a, b);
end
else
begin
repeat
repeat
p1 := random(RESOLUTION) / RESOLUTION;
until p1 > 0;
repeat
p2 := random(RESOLUTION) / RESOLUTION;
until p2 > 0;
v := A2 * ln(p1 / (1 - p1));
y := c * exp(v);
z := p1 * p1 * p2;
w := B2 + Q * v - y;
if (w + D - T * z >= 0) or (w >= ln(z)) then
begin
randomGamma := a + b * y;
found := True;
end;
until found;
end;
end;```

## Erlang Distribution

The Erlang distribution is a two parameter family of continuous probability distributions. It is a generalization of the exponential distribution and a special case of the gamma distribution, where c is an integer. The Erlang distribution has been first described by Agner Krarup Erlang in order to model the time interval between telephone calls. It is used for queuing theory and for simulating waiting lines.

```  function randomErlang(mean: real; k: integer): real;
const
RESOLUTION = 1000;
var
i: integer;
unif, prod: real;
begin
if (mean <= 0) or (k < 1) then
randomErlang := NaN
else
begin
prod := 1;
for i := 1 to k do
begin
repeat
unif := random(RESOLUTION) / RESOLUTION;
until unif <> 0;
prod := prod * unif;
end;
randomErlang := -mean * ln(prod);
end;
end;```

## t Distribution

The t distribution (also referred to a Student's t distribution, since it was published by William Sealy Gosset in 1908 under the pseudonym Student) is a continuous probability distribution. Its shape is defined by one parameter, the degrees of freedom (df). In statistics, many estimators are t distributed. Therefore, Student's t-distribution plays a major role in a number of widely used statistical analyses, including Student's t-test for assessing the statistical significance of the difference between two sample means, the construction of confidence intervals for the difference between two population means, and in linear regression analysis. The t-distribution also arises in Bayesian analysis of data from a normal family.

The following algorithm depends on the [RTL]] function `random` and on the randomChisq function

```function randomT(df: integer): real;
{ Generator for Student's t distribution }
begin
if df < 1 then randomT := NaN
else
begin
randomT := randg(0, 1) / sqrt(randomChisq(df) / df);
end;
end;```

## Chi Squared Distribution

The chi squared distribution is a continuous distribution of random numbers with df degrees of freedom. It is the distribution of a sum of the squares of df independent standard normal random variables. The chi squared distribution has numerous applications in inferential statistics, e.g. in estimating variances and for chi-squared tests. It is a special gamma distribution with c = df/ 2 and b = 2. Therefore the following function depends on the function randomGamma.

```function randomChisq(df: integer): real;
begin
if df < 1 then randomChisq := NaN
else
randomChisq := randomGamma(0, 2, 0.5 * df);
end;```

## F Distribution

The F distribution, also referred to as Fisher-Snedecor distribution, is a continuous probability distribution. It is used for F Test and ANOVA. It has two degrees of freedom that serve as shape parameters v and w and that are positive integers. The following function randomF makes use of randomChisq.

```function randomF(v, w: integer): real;
begin
if (v < 1) or (w < 1) then
randomF := NaN
else
randomF := randomChisq(v) / v / (randomChisq(w) / w);
end;```