parameterspace.priors.base

BasePrior

Base class defining the API of the priors.

The priors enable the incorporation of domain knowledge into the parameter definition by allowing the specification of a PDF/PMF. These are used to sample random values and to compute the loglikelihood of a given value.

loglikelihood(self, value)

Compute the log PDF (up to an additive constant) of a given value.

Note

Values for the priors are always after the transformation!

Parameters:

Name	Type	Description	Default
value		[description]	required

Returns:

Type	Description
	[descriptions]

Source code in parameterspace/priors/base.py

def loglikelihood(self, value):
    """
    Compute the log PDF (up to an additive constant) of a given value.

    Note:
        Values for the priors are always after the transformation!

    Args:
        value: [description]

    Returns:
        [descriptions]
    """
    return np.log(self.pdf(value))

pdf(self, value)

Computes the PDF of a given value.

Note

Values for the priors are always after the transformation!

Parameters:

Name	Type	Description	Default
value		[description]	required

Returns:

Type	Description
	[descriptions]

Source code in parameterspace/priors/base.py

@abc.abstractmethod
def pdf(self, value):
    """
    Computes the PDF of a given value.

    Note:
        Values for the priors are always after the transformation!

    Args:
        value: [description]

    Returns:
        [descriptions]
    """

sample(self, num_samples=1)

Draw random samples from the prior.

Parameters:

Name	Type	Description	Default
num_samples	int	[description]	1

Returns:

Type	Description
	[descriptions]

Source code in parameterspace/priors/base.py

@abc.abstractmethod
def sample(self, num_samples: int = 1):
    """
    Draw random samples from the prior.

    Args:
        num_samples: [description]

    Returns:
        [descriptions]
    """