GeneralizedGaussianPrior#
- class gammapy.modeling.models.GeneralizedGaussianPrior[source]#
Bases:
PriorOne-dimensional Generalized Gaussian Prior.
- Parameters:
- mufloat, optional
Mean of the Gaussian distribution. Default is 0.
- sigmafloat, optional
Standard deviation of the Gaussian distribution. Default is 1.
- eta
float, optional eta is a shape parameter For eta=1 it is identical to a Laplace distribution (scaled by sqrt(2)). For eta=0.5 it is identical to a normal distribution. Default is 0.5.
Attributes Summary
Methods Summary
evaluate(value, mu, sigma, eta)Evaluate the Gaussian prior.
Attributes Documentation
- default_parameters = <gammapy.modeling.parameter.PriorParameters object>#
- eta#
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.
Examples
For a usage example see Priors tutorial.
- 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.
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.
Examples
For a usage example see Priors tutorial.
- tag = ['GeneralizedGaussianPrior']#
Methods Documentation
- __init__(**kwargs)#
- classmethod __new__(*args, **kwargs)#