Distributions

Distribution classes and associated methods.

The classes in this module describe statistical distributions. Most of them are non-normalized having implications for quantifying the evidence term of Bayes’ rule.

All of the classes inherit from _AbstractDistribution; a base class outlining required methods and their signatures (required in- and outputs).

Note

A tutorial on implementing your own distributions can be found at Creating your own inverse problem.

Available classes within Distributions:

_AbstractDistribution	Abstract base class for distributions.
StandardNormal1D	Standard normal distribution in 1 dimension.
Normal	Normal distribution in model space.
Laplace	Laplace distribution in model space.
Uniform	Uniform bounded or unbouded distribution in model space.
LinearMatrix	Likelihood model based on a linear forward model given as \(G \mathbf{m} = \mathbf{d}\) coupled with Gaussian observational errors.
CompositeDistribution	Distribution distribution combined from multiple unconditional distributions.
AdditiveDistribution	Distribution generated by summing the characteristic functions of two other distributions.
BayesRule	A class to apply (the unnormalized) Bayes' rule to two or more distributions.
Himmelblau	Himmelblau's 2-dimensional function.
SourceLocation2D	Earthquake source location in 2D using a single velocity for the subsurface.
SourceLocation3D	Earthquake source location in 3D using a single velocity for the subsurface.