bilby.core.prior.slabspike.SlabSpikePrior

class bilby.core.prior.slabspike.SlabSpikePrior(slab, spike_location=None, spike_height=0)

Bases: Prior

__init__(slab, spike_location=None, spike_height=0)

‘Slab-and-spike’ prior, see e.g. https://arxiv.org/abs/1812.07259 This prior is composed of a slab, i.e. any common prior distribution, and a Dirac spike at a fixed location. This can effectively be used to emulate sampling in the number of dimensions (similar to reversible- jump MCMC).

SymmetricLogUniform and FermiDirac are currently not supported.

Parameters:

slab: Prior

Any instance of a bilby prior class. All general prior attributes from the slab are copied into the SlabSpikePrior. Note that this hasn’t been tested for conditional priors.

spike_location: float, optional

Location of the Dirac spike. Must be between minimum and maximum of the slab. Defaults to the minimum of the slab

spike_height: float, optional

Relative weight of the spike compared to the slab. Must be between 0 and 1. Defaults to 0, i.e. the prior is just the slab.

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(slab[, spike_location, spike_height])	'Slab-and-spike' prior, see e.g. https://arxiv.org/abs/1812.07259 This prior is composed of a slab, i.e. any common prior distribution, and a Dirac spike at a fixed location.
cdf(val)	Return the CDF of the prior.
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 Log prior probability of val.
prob(val)	Return the prior probability of val.
rescale(val)	'Rescale' a sample from the unit line element 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
slab_fraction	Relative prior weight of the slab.
spike_height
spike_location
unit
width

cdf(val)

Return the CDF of the prior. This calls to the slab CDF and adds a discrete step at the spike location.

Parameters:

val: Union[float, int, array_like]

Returns:

array_like: CDF value of val

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 Log prior probability of val. Returns np.inf for the spike location

Parameters:

val: Union[float, int, array_like]

Returns:

array_like: Prior probability of val

prob(val)

Return the prior probability of val. Returns np.inf for the spike location

Parameters:

val: Union[float, int, array_like]

Returns:

array_like: Prior probability of val

rescale(val)

‘Rescale’ a sample from the unit line element to the prior.

Parameters:

val: Union[float, int, array_like]

A random number between 0 and 1

Returns:

array_like: Associated prior value with input value.

sample(size=None)

Draw a sample from the prior

Parameters:

size: int or tuple of ints, optional

See numpy.random.uniform docs

Returns:

float: A random number between 0 and 1, rescaled to match the distribution of this Prior

property slab_fraction

Relative prior weight of the slab.