TransportMaps.Distributions.Deprecated

Module Contents

Classes

GaussianDistribution

Multivariate Gaussian distribution \(\pi\)

class TransportMaps.Distributions.Deprecated.GaussianDistribution(mu, sigma=None, precision=None, square_root=None)

Bases: TransportMaps.Distributions.DistributionBase.Distribution

Multivariate Gaussian distribution \(\pi\)

Parameters:

• mu (ndarray [\(d\)]) – mean vector

• sigma (ndarray [\(d,d\)]) – covariance matrix

• precision (ndarray [\(d,d\)]) – precision matrix

property mu

property square_root

property sigma

property precision

rvs(m, *args, **kwargs)

Generate \(m\) samples from the distribution.

Parameters:

m (int) – number of samples

Returns:

(ndarray [\(m,d\)]) – samples

See also

Distribution.rvs()

quadrature(qtype, qparams: int, mass=1.0, batch_size=np.inf, **kwargs)

Generate quadrature points and weights.

Types of quadratures:

Monte-Carlo (qtype==0)

qparams: (int) – number of samples

Quasi-Monte-Carlo (qtype==1)

qparams: (int) – number of samples

Latin-Hypercube-Sampling (qtype==2)

qparams: (int) – number of samples

Gauss-quadrature (qtype==3)

qparams: (list [\(d\)]) – orders for each dimension

pdf(x, *args, **kwargs)

Evaluate \(\pi(x)\)

See also

Distribution.pdf()

log_pdf(x, *args, **kwargs)

Evaluate \(\log\pi(x)\)

See also

Distribution.log_pdf()

grad_x_log_pdf(x, *args, **kwargs)

Evaluate \(\nabla_{\bf x}\log\pi(x)\)

See also

Distribution.grad_x_log_pdf()

hess_x_log_pdf(x, *args, **kwargs)

Evaluate \(\nabla^2_{\bf x}\log\pi(x)\)

See also

Distribution.hess_x_log_pdf()

action_hess_x_log_pdf(x, dx, *args, **kwargs)

Evaluate \(\langle \nabla^2_{\bf x} \log \pi({\bf x}), \delta{\bf x}\rangle\)

See also

Distribution.action_hess_x_log_pdf()

mean_log_pdf()

Evaluate \(\mathbb{E}_{\pi}[\log \pi]\).

See also

Distribution.mean_log_pdf()