This module provides a set of predicates for sampling from various distributions. The state of the random generator is threaded through using the DCG idiom.

author

- Samer Abdallah

 bernoulli(+A:prob, -X:oneof([0,1]))// is det

Sample binary random variable.

 binomial(+P:float, +N:natural, -X:natural)// is det

Sample X from a binomial distribution, ie the number of successful trials out of N trials where the probability of success of each trial is P.

 poisson(+A:nonneg, -X:float)// is det

Sample from Poisson distribution of rate A.

 discrete(+A:list(prob), -X:natural)// is det

Sample from a discrete distribution over natural numbers.

 discrete(+O:list(T), +A:list(prob), -X:T)// is det

Sample from a discrete distribution over list of objects.

 uniform01(-X:float)// is det

Sample X from uniform distribution on [0,1).

 normal(-X:float)// is det

Sample from zero-mean unit-variance Gaussian.

 exponential(-X:float)// is det

Sample from unit-mean exponential distribution.

 stable(+A, +B, -X:float)// is det

Sample from a Levy-stable distribution.

 dirichlet(+A:list(nonneg), -X:list(prob))// is det

Sample from a Dirichlet distribution.

 uniform(+Items:list(A), -A)// is det

Uniform distribution over a finite number of items. uniform :: list(A) -> expr(A).

 uniformP(+P:dcg(-A), -A)// is det

Sample uniformly from all solutions to call(P,X).

 beta(+A:nonneg, +B:nonneg, -X:prob)// is det

Sample from beta distribution.

 zeta(+A:nonneg, -X:natural)// is det

Sample from zeta (hyperbolic or power law) distribution over natural numbers. NB: Must have A > 1.

 gamma(+A:nonneg, -X:float)// is det

Sample from gamma distribution with parameter A.

 gaussian(+Mean:float, +Var:nonneg, -X:float)// is det

gaussian :: \(float, nonneg) -> expr(float). Sample from Gaussian with given mean and variance.

 inv_gamma(+A:nonneg, -X:float)// is det

Sample from inverse gamma distribution with parameter A.

 students_t(+V:nonneg, -X:float)// is det

Sample from student's t distribution with V degrees of freedom.

 pair(+F:dist(A), +G:dist(B), -X:pair(A,B))// is det

Sample a pair from two independent distributions.

 mixture(+Sources:list(expr(A)), +Probs:list(prob), -X:A)// is det

Sample from discrete distribution over Sources with probabilities Probs and then sample from the resulting distribution.

mixture :: \(list(expr(A)), list(prob)) -> expr(A).