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]) |
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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) |
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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]) |
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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) |
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wishart (nu, V) |
Draw a random sample from the Wishart distribution. |
wishart_rand (nu, V) |
Draw a random sample from the Wishart distribution. |