# autofit.GaussianPrior#

class GaussianPrior[source]#

Bases: Prior

A prior with a uniform distribution, defined between a lower limit and upper limit.

The conversion of an input unit value, u, to a physical value, p, via the prior is as follows:

$p = \mu + (\sigma * sqrt(2) * erfcinv(2.0 * (1.0 - u))$

For example for prior = GaussianPrior(mean=1.0, sigma=2.0), an input prior.value_for(unit=0.5) is equal to 1.0.

The mapping is performed using the message functionality, where a message represents the distirubtion of this prior.

Parameters
• mean (float) – The mean of the Gaussian distribution defining the prior.

• sigma (float) – The sigma value of the Gaussian distribution defining the prior.

• lower_limit (float) – A lower limit of the Gaussian distribution; physical values below this value are rejected.

• upper_limit (float) – A upper limit of the Gaussian distribution; physical values below this value are rejected.

Examples

prior = af.GaussianPrior(mean=1.0, sigma=2.0, lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.5)

Methods

 assert_within_limits dict A dictionary representation of this prior for_class_and_attribute_name from_dict Returns a prior from a JSON representation. gaussian_prior_model_for_arguments has Does this instance have an attribute which is of type cls? instance_for_arguments make_indexes rtype Tuple[ndarray, ...] name_of_class A string name for the class, with the prior suffix removed. new Returns a copy of this prior with a new id assigned making it distinct next_id project random A random value sampled from this prior replacing_for_path Create a new model replacing the value for a given path with a new value unit_value_for Compute the unit value between 0 and 1 for the physical value. value_for Return a physical value for a value between 0 and 1 with the transformation described by this prior. with_limits Create a new gaussian prior centred between two limits with sigma distance between this limits. with_message

Attributes

 component_number factor A callable PDF used as a factor in factor graphs identifier label limits rtype lower_unit_limit The lower limit for this prior in unit vector space ndim How many dimensions does this variable have? parameter_string rtype str upper_unit_limit The upper limit for this prior in unit vector space width
classmethod with_limits(lower_limit, upper_limit)[source]#

Create a new gaussian prior centred between two limits with sigma distance between this limits.

Note that these limits are not strict so exceptions will not be raised for values outside of the limits.

This function is typically used in prior passing, where the result of a model-fit are used to create new Gaussian priors centred on the previously estimated median PDF model.

Parameters
Return type

A new GaussianPrior

dict()[source]#

A dictionary representation of this prior

Return type

dict