estimator.nd.DiscreteGaussian

estimator.nd.DiscreteGaussian#

class estimator.nd.DiscreteGaussian(stddev, mean=0, n=None)[source]#

A discrete Gaussian distribution with standard deviation stddev per component.

EXAMPLE:

>>> from estimator import *
>>> ND.DiscreteGaussian(3.0, 1.0)
D(σ=3.00, μ=1.00)
__call__(**kwargs)#

Call self as a function.

Methods

__init__(stddev[, mean, n])

resize(new_n)

Return an altered distribution having a dimension new_n.

support_size([fraction])

Compute the size of the support covering the probability given as fraction.

Attributes

bounds

gaussian_tail_bound

gaussian_tail_prob

hamming_weight

The number of non-zero coefficients in this distribution

is_Gaussian_like

is_bounded

Whether the value of coefficients are bounded

is_sparse

Whether the density of the distribution is < 1/2.

mean

n

stddev