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NoiseScale = class NoiseScale(HyperParameter) |
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NoiseScale(scale=1.0, isFixed=True, prior=None, limits=None, copy=None)
Hyperparameter for the scale of a ScaledErrorDistribution
it is a measure of the noise.
Information about the scale of the noise is stored in his class.
It is either in the form of a fixed number, when the noise scale
is known or in the form of a Prior with limits.
By default this prior is a JeffreysPrior..
The full use of priors is reserved for Bayesian calculations as
in NestedSampler
Attributes
----------
scale : float
the value of the noiseScale. Default: 1.0
stdev : float
the standard deviation of the noise scale. Default: None
prior : Prior
the prior for the noiseScale. Default: JeffreysPrior
fixed : boolean
keep the noise scale fixed at the value given by scale.
default: True
minimum : boolean
automatic noise scaling with a minimum. default: False
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- Method resolution order:
- NoiseScale
- HyperParameter
- builtins.object
Constructor:
- NoiseScale( scale=1.0, isFixed=True, prior=None, limits=None, copy=None )
- Constructor.
Parameters
----------
scale : float
float value of the noise scale
isFixed : bool
True: Consider the hyperparameter as fixed
False: Optimize the parameter too (when relevant)
It might need a prior and/or limits to be set
The default prior is JeffreysPrior
prior : None or Prior
None : no prior set
Prior : the prior probability on scale
limits : None or list of 2 floats
None : no limits set
[lo,hi] : limits to be passed to the Prior.
If limits are set, the default for Prior is JeffreysPrior
copy : NoiseScale
NoiseScale to copy
Methods defined here:
- copy()
- Return a copy.
- minimumScale( scale=None )
- Fit the noise scale with a minimum value.
Parameters
----------
scale : float
the value of the noise scale. Default: noiseScale.scale
Methods inherited from HyperParameter:
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