BayesRule#

class hmclab.Distributions.BayesRule(*args, **kwargs)[source]#

Bases: hmclab.Distributions.base.AdditiveDistribution

A class to apply (the unnormalized) Bayes’ rule to two or more distributions.

__init__(list_of_distributions: List[hmclab.Distributions.base._AbstractDistribution], lower_bounds: Optional[numpy.ndarray] = None, upper_bounds: Optional[numpy.ndarray] = None)#

Methods

`__init__`
`add_distribution`	Add a distribution to the object.
`collapse_bounds`	Method to restructure all composite bounds into top level object.
`corrector`	Override method to correct an HMC particle for additive distribution, which is called after every time integration step.
`create_default`
`dimensions`	Dimensionality of misfit space.
`generate`
`gradient`
`misfit`
`misfit_bounds`	Compute misfit of bounded distribution.
`normalize`	Normalize distribution.
`update_bounds`	Update bounded distribution.

Attributes

`lower_bounds`	Lower bounds for every parameter.
`name`	Name of the distribution.
`normalized`	Boolean describing if the distribution is normalized.
`upper_bounds`	Upper bounds for every parameter.

add_distribution(distribution: hmclab.Distributions.base._AbstractDistribution)#: Add a distribution to the object.

collapse_bounds()#: Method to restructure all composite bounds into top level object.

corrector(coordinates: numpy.ndarray, momentum: numpy.ndarray)#

Override method to correct an HMC particle for additive distribution, which is called after every time integration step. Calls all sub-correctors only if the object does not have bounds itself.

Parameters

coordinates (numpy.ndarray) – Numpy array shaped as (dimensions, 1) representing a column vector containing the coordinates \(\mathbf{m}\) upon which to operate by reference.
momentum (numpy.ndarray) – Numpy array shaped as (dimensions, 1) representing a column vector containing the momenta \(\mathbf{p}\) upon which to operate by reference.

dimensions() → int#

Dimensionality of misfit space.

This is an abstract parameter. If it is not defined either in your class directly or in its constructor (the __init__ function) then attempting to use the class will raise a NotImplementedError.

Access it like a parameter, not a function: distribution.dimensions.

misfit_bounds(coordinates: numpy.ndarray) → float#

Compute misfit of bounded distribution.

Method to compute the misfit associated with the truncated part of the distribution. Used internally.

normalize()#

Normalize distribution.

Method to compute the normalization constant of a distribution. As this might take significant time, it is not done in initialization.

Raises: AttributeError – An AttributeError is raised if the distribution provides no way to be normalized, e.g. when the normalization constant is intractable.

update_bounds(lower: Optional[numpy.ndarray] = None, upper: Optional[numpy.ndarray] = None)#

Update bounded distribution.

This method updates the bounds of a distribution. Note that invocating it, does not require both bounds to be passed.

If both vectors are passed, ensure that all upper bounds are above the corresponding lower bounds.

Parameters

lower (numpy.ndarray or None) – Either an array shaped as (dimensions, 1) with floats for the lower bounds, or None for no bounds. If some dimensions should be bounded, while others should not, use -numpy.inf within the vector as needed.
upper (numpy.ndarray or None) – Either an array shaped as (dimensions, 1) with floats for the upper bounds, or None for no bounds. If some dimensions should be bounded, while others should not, use numpy.inf within the vector as needed.

Raises

ValueError – A ValueError is raised if the supplied upper and lower bounds are incompatible.