estimator.nd.CenteredBinomial#
- class estimator.nd.CenteredBinomial(eta, n=None)[source]#
Sample a_1, …, a_η, b_1, …, b_η uniformly from {0, 1}, and return Σ(a_i - b_i).
EXAMPLE:
>>> from estimator import * >>> ND.CenteredBinomial(8) D(σ=2.00)
- __call__(**kwargs)#
Call self as a function.
Methods
__init__
(eta[, 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
The number of non-zero coefficients in this distribution
Whether the value of coefficients are bounded
Whether the density of the distribution is < 1/2.