bayespy.utils.randomΒΆ

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.