LogNormalPrior

class gammapy.modeling.models.LogNormalPrior

Bases: Prior

Log-normal prior.

Equivalent to a gaussian prior on the log of the parameter i.e. log(value).

Parameters:

mufloat, optional

Median of the distribution (i.e. mean of log(value) is log(mu)). Default is 1.

sigmafloat, optional

Standard deviation of log(value). Default is 1.

Attributes Summary

default_parameters
mu	Parameter of a Prior.
sigma	Parameter of a Prior.
tag

Methods Summary

evaluate(value, mu, sigma)

Evaluate the log-normal prior (gaussian in log(value)).

Attributes Documentation

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

mu

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.

sigma

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 = ['LogNormalPrior']

Methods Documentation

static evaluate(value, mu, sigma)

Evaluate the log-normal prior (gaussian in log(value)).

__init__(**kwargs)

classmethod __new__(*args, **kwargs)