autofit.UniformPrior#

class autofit.UniformPrior(lower_limit: float = 0.0, upper_limit: float = 1.0, id_=None)[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:

\[\]

For example for prior = UniformPrior(lower_limit=0.0, upper_limit=2.0), an input prior.value_for(unit=0.5) is equal to 1.0.

[Rich describe how this is done via message]

Parameters:

lower_limit – The lower limit of the uniform distribution defining the prior.
upper_limit – The upper limit of the uniform distribution defining the prior.

Examples

prior = af.UniformPrior(lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.2)

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`
`log_prior_from_value`	Returns the log prior of a physical value, so the log likelihood of a model evaluation can be converted to a posterior as log_prior + log_likelihood.
`logpdf`
`make_indexes`
`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`	Returns a physical value from an input unit value according to the limits of the uniform prior.
`with_limits`	Create a new instance of the same prior class with the passed limits.
`with_message`

Attributes

`component_number`
`factor`	A callable PDF used as a factor in factor graphs
`identifier`
`label`
`limits`
`lower_unit_limit`	The lower limit for this prior in unit vector space
`ndim`	How many dimensions does this variable have?
`parameter_string`
`upper_unit_limit`	The upper limit for this prior in unit vector space
`width`

value_for(unit: float, ignore_prior_limits: bool = False) → float[source]#

Returns a physical value from an input unit value according to the limits of the uniform prior.

Examples

prior = af.UniformPrior(lower_limit=0.0, upper_limit=2.0)

physical_value = prior.value_for(unit=0.2)

static log_prior_from_value(value)[source]#

Returns the log prior of a physical value, so the log likelihood of a model evaluation can be converted to a posterior as log_prior + log_likelihood.

This is used by certain non-linear searches (e.g. Emcee) in the log likelihood function evaluation.

For a UniformPrior this is always zero, provided the value is between the lower and upper limit.