Gaussian distribution¶
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class
pints.toy.
GaussianLogPDF
(mean=[0, 0], sigma=[1, 1])[source]¶ Toy distribution based on a multivariate (unimodal) Normal/Gaussian distribution.
Extends
pints.toy.ToyLogPDF
.Parameters: - mean – The distribution mean (specified as a vector).
- sigma – The distribution’s covariance matrix. Can be given as either a matrix
or a vector (in which case
diag(sigma)
will be used. Should be symmetric and positive-semidefinite.
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distance
(samples)[source]¶ Returns the
Kullback-Leibler divergence
.
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kl_divergence
(samples)[source]¶ Calculates the Kullback-Leibler divergence between a given list of samples and the distribution underlying this LogPDF.
The returned value is (near) zero for perfect sampling, and then increases as the error gets larger.
See: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence
-
suggested_bounds
()¶ Returns suggested boundaries for prior.