bilby.core.prior.joint.MultivariateNormal

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

Bases: MultivariateGaussian

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

__init__(dist, name=None, latex_label=None, unit=None)[source]

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__()[source]

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)[source]

Generic method to calculate CDF, can be overwritten in subclass

classmethod from_repr(string)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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.