bayespy.utils.random

General functions random sampling and distributions.

Functions

alpha_beta_recursion(logp0, logP)	Compute alpha-beta recursion for Markov chain
bernoulli(p[, size])	Draw random samples from the Bernoulli distribution.
categorical(p[, size])	Draw random samples from a categorical distribution.
correlation(D)	Draw a random correlation matrix.
covariance(D[, size, nu])	Draw a random covariance matrix.
dirichlet(alpha[, size])	Draw random samples from the Dirichlet distribution.
gamma(a, b[, size])
gamma_entropy(a, log_b, gammaln_a, psi_a, ...)	Entropy of .
gamma_logpdf(bx, logx, a_logx, a_logb, gammaln_a)	Log-density of .
gaussian_entropy(logdet_V, D)	Compute the entropy of a Gaussian distribution.
gaussian_gamma_to_t(mu, Cov, a, b[, ndim])	Integrates gamma distribution to obtain parameters of t distribution
gaussian_logpdf(yVy, yVmu, muVmu, logdet_V, D)	Log-density of a Gaussian distribution.
intervals(N, length[, amount, gap])	Return random non-overlapping parts of a sequence.
invwishart_rand(nu, V)
logodds_to_probability(x)	Solves p from log(p/(1-p))
mask(*shape[, p])	Return a boolean array of the given shape.
multinomial(n, p[, size])
orth(D)	Draw random orthogonal matrix.
sphere([N])	Draw random points uniformly on a unit sphere.
svd(s)	Draw a random matrix given its singular values.
t_logpdf(z2, logdet_cov, nu, D)
wishart(nu, V)	Draw a random sample from the Wishart distribution.
wishart_rand(nu, V)	Draw a random sample from the Wishart distribution.