gsl-random-0.2: Bindings the the GSL random number generation facilities.

Stability experimental Patrick Perry

GSL.Random.Dist

Description

Random number distributions. Functions for generating random variates and computing their probability distributions.

Synopsis

# The Gaussian Distribution

## General

`gaussianPdf x sigma` computes the probabililty density p(x) for a Gaussian distribution with mean `0` and standard deviation `sigma`.

`gaussianP x sigma` computes the cumulative distribution function P(x) for a Gaussian distribution with mean `0` and standard deviation `sigma`.

`gaussianQ x sigma` computes the cumulative distribution function Q(x) for a Gaussian distribution with mean `0` and standard deviation `sigma`.

`gaussianPInv p sigma` computes the inverse of the cumulative distribution function of a Gaussian distribution with mean `0` and standard deviation `sigma`. It returns `x` such that `P(x) = p`.

`gaussianPInv q sigma` computes the inverse of the cumulative distribution function of a Gaussian distribution with mean `0` and standard deviation `sigma`. It returns `x` such that `Q(x) = q`.

`getGaussian r sigma` gets a normal random variable with mean `0` and standard deviation `sigma`. This uses the Box-Mueller algorithm.

`getGaussianZiggurat r sigma` gets a normal random variable with mean `0` and standard deviation `sigma`. This uses the Marsaglia-Tsang ziggurat algorithm.

`getGaussianRatioMethod r sigma` gets a normal random variable with mean `0` and standard deviation `sigma`. This uses the Kinderman-Monahan-Leva ratio method.

## Unit Variance

`ugaussianPdf x` computes the probabililty density p(x) for a Gaussian distribution with mean `0` and standard deviation `1`.

`ugaussianP x` computes the cumulative distribution function P(x) for a Gaussian distribution with mean `0` and standard deviation `1`.

`ugaussianQ x` computes the cumulative distribution function Q(x) for a Gaussian distribution with mean `0` and standard deviation `1`.

`ugaussianPInv p` computes the inverse of the cumulative distribution function of a Gaussian distribution with mean `0` and standard deviation `1`. It returns `x` such that `P(x) = p`.

`ugaussianPInv q` computes the inverse of the cumulative distribution function of a Gaussian distribution with mean `0` and standard deviation `1`. It returns `x` such that `Q(x) = q`.

`getUGaussian r` gets a normal random variable with mean `0` and standard deviation `1`. This uses the Box-Mueller algorithm.

`getUGaussianZiggurat r` gets a normal random variable with mean `0` and standard deviation `1`. This uses the Marsaglia-Tsang ziggurat algorithm.

`getUGaussianRatioMethod r` gets a normal random variable with mean `0` and standard deviation `1`. This uses the Kinderman-Monahan-Leva ratio method.

# The Flat (Uniform) Distribution

`flatPdf x a b` computes the probability density `p(x)` at `x` for a uniform distribution from `a` to `b`.

`flatP x a b` computes the cumulative distribution function `P(x)`.

`flatQ x a b` computes the cumulative distribution function `Q(x)`.

`flatPInv p a b` computes the inverse of the cumulative distribution and returns `x` so that function `P(x) = p`.

`flatQInv q a b` computes the inverse of the cumulative distribution and returns `x` so that function `Q(x) = q`.

`getFlat r a b` gets a value uniformly chosen in `[a,b)`.

# The Poisson Distribution

`poissonPdf k mu` evaluates the probability density `p(k)` at `k` for a Poisson distribution with mean `mu`.

`poissonP k mu` evaluates the cumulative distribution function `P(k)` at `k` for a Poisson distribution with mean `mu`.

`poissonQ k mu` evaluates the cumulative distribution function `Q(k)` at `k` for a Poisson distribution with mean `mu`.

`getPoisson r mu` gets a poisson random variable with mean `mu`.