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 \mathcal{G}(a,b).

gamma_logpdf(bx, logx, a_logx, a_logb, gammaln_a)

Log-density of \mathcal{G}(x|a,b).

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.