GaussianPrior

class gammapy.modeling.models.GaussianPrior

Bases: Prior

One-dimensional Gaussian Prior.

Parameters:

mufloat, optional

Mean of the Gaussian distribution. Default is 0.

sigmafloat, optional

Standard deviation of the Gaussian distribution. 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 Gaussian prior.

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 = ['GaussianPrior']

Methods Documentation

static evaluate(value, mu, sigma)

Evaluate the Gaussian prior.

__init__(**kwargs)

classmethod __new__(*args, **kwargs)