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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)`.