bilby.core.prior.joint.MultivariateNormal

class bilby.core.prior.joint.MultivariateNormal(dist, name=None, latex_label=None, unit=None)

Bases: MultivariateGaussian

A synonym for the bilby.core.prior.MultivariateGaussian prior distribution.

__init__(dist, name=None, latex_label=None, unit=None)

This defines the single parameter Prior object for parameters that belong to a JointPriorDist

Parameters:

dist: ChildClass of BaseJointPriorDist

The shared JointPriorDistribution that this parameter belongs to

name: str

Name of this parameter. Must be contained in dist.names

latex_label: str

See superclass

unit: str

See superclass

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(dist[, name, latex_label, unit])	This defines the single parameter Prior object for parameters that belong to a JointPriorDist
cdf(val)	Generic method to calculate CDF, can be overwritten in subclass
from_json(dct)
from_repr(string)	Generate the prior from its __repr__
get_instantiation_dict()
is_in_prior_range(val)	Returns True if val is in the prior boundaries, zero otherwise
ln_prob(val)	Return the natural logarithm of the prior probability.
prob(val)	Return the prior probability of val
rescale(val, **kwargs)	Scale a unit hypercube sample to the prior.
sample([size])	Draw a sample from the prior.
to_json()

Attributes

boundary
is_fixed	Returns True if the prior is fixed and should not be used in the sampler.
latex_label	Latex label that can be used for plots.
latex_label_with_unit	If a unit is specified, returns a string of the latex label and unit
maximum
minimum
unit
width

cdf(val)

Generic method to calculate CDF, can be overwritten in subclass

classmethod from_repr(string)

Generate the prior from its __repr__

property is_fixed

Returns True if the prior is fixed and should not be used in the sampler. Does this by checking if this instance is an instance of DeltaFunction.

Returns:

bool: Whether it’s fixed or not!

is_in_prior_range(val)

Returns True if val is in the prior boundaries, zero otherwise

Parameters:

val: Union[float, int, array_like]

Returns:

np.nan

property latex_label

Latex label that can be used for plots.

Draws from a set of default labels if no label is given

Returns:

str: A latex representation for this prior

property latex_label_with_unit

If a unit is specified, returns a string of the latex label and unit

ln_prob(val)

Return the natural logarithm of the prior probability. Note that this will not be correctly normalised if there are bounds on the distribution.

Parameters:

val: array_like

value to evaluate the prior log-prob at

Returns

=======

float:

the logp value for the prior at given sample

prob(val)

Return the prior probability of val

Parameters:

val: array_like

value to evaluate the prior prob at

Returns:

float:

the p value for the prior at given sample

rescale(val, **kwargs)

Scale a unit hypercube sample to the prior.

Parameters:

val: array_like

value drawn from unit hypercube to be rescaled onto the prior

kwargs: dict

all kwargs passed to the dist.rescale method

Returns

=======

float:

A sample from the prior parameter.

sample(size=1, **kwargs)

Draw a sample from the prior.

Parameters:

size: int, float (defaults to 1)

number of samples to draw

kwargs: dict

kwargs passed to the dist.sample method

Returns

=======

float:

A sample from the prior parameter.