bilby.core.prior.analytical.Beta

class bilby.core.prior.analytical.Beta(alpha, beta, minimum=0, maximum=1, name=None, latex_label=None, unit=None, boundary=None)

Bases: Prior

__init__(alpha, beta, minimum=0, maximum=1, name=None, latex_label=None, unit=None, boundary=None)

Beta distribution

https://en.wikipedia.org/wiki/Beta_distribution

This wraps around https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html

Parameters:

alpha: float

first shape parameter

beta: float

second shape parameter

minimum: float

See superclass

maximum: float

See superclass

name: str

See superclass

latex_label: str

See superclass

unit: str

See superclass

boundary: str

See superclass

__call__()

Overrides the __call__ special method. Calls the sample method.

Returns:

float: The return value of the sample method.

Methods

__init__(alpha, beta[, minimum, maximum, ...])	Beta distribution
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)	Returns 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 appropriate Beta 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)

Returns the log prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

Union[float, array_like]: Prior probability of val

prob(val)

Return the prior probability of val.

Parameters:

val: Union[float, int, array_like]

Returns:

Union[float, array_like]: Prior probability of val

rescale(val)

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

This maps to the inverse CDF. This has been analytically solved for this case.

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