LogUniformPrior

class gammapy.modeling.models.LogUniformPrior

Bases: Prior

LogUniform Prior.

Equivalent to a uniform prior on the log of the parameter

Parameters:

minfloat, optional

Minimum value. Default is 1e-14.

maxfloat, optional

Maximum value. Default is 1e-10.

Attributes Summary

default_parameters
max	Parameter of a Prior.
min	Parameter of a Prior.
tag

Methods Summary

evaluate(value, min, max)

Evaluate the likelihood penalization term (hence -2*).

Attributes Documentation

default_parameters = <gammapy.modeling.parameter.PriorParameters object>

max

Parameter of a Prior.

A prior is a probability density function of a model parameter and can take different forms, including but not limited to Gaussian distributions and uniform distributions. The prior includes information or knowledge about the dataset or the parameters of the fit.

Parameters:

namestr

Name.

valuefloat or Quantity

Value.

unitUnit or str, optional

Unit. Default is “”.

Examples

For a usage example see Priors tutorial.

min

Parameter of a Prior.

A prior is a probability density function of a model parameter and can take different forms, including but not limited to Gaussian distributions and uniform distributions. The prior includes information or knowledge about the dataset or the parameters of the fit.

Parameters:

namestr

Name.

valuefloat or Quantity

Value.

unitUnit or str, optional

Unit. Default is “”.

Examples

For a usage example see Priors tutorial.

tag = ['LogUniformPrior']

Methods Documentation

static evaluate(value, min, max)

Evaluate the likelihood penalization term (hence -2*). Note that this is currently a different scaling that the Uniform or Gaussian priors. With current implementation the TS of a source with/without LogUniform prior would be different… TBD

__init__(**kwargs)

classmethod __new__(*args, **kwargs)