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Sample = class Sample(builtins.object) |
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Sample(id, parent, start, model, parameters=None,
fitIndex=None, copy=None)
Sample is weighted random draw from a Posterior distribution as
provided by a Sampler
Each Sample maintains 5 attributes
Attributes
----------
id : int
identification number
parent : int
id of the parent (-1 for Adam/Eve)
model : Model
the model being used
logL : float
log Likelihood = log Prob( data | params )
logW : float
log Weights of the log of the weight of the sample.
The weight is the relative contribution to the evidence integral.
logW = logL + log( width )
The logZ, the evidence, equals the log of the sum of the contributions.
logZ = log( sum( exp( logW ) ) )
parameters : array_like
parameters (of the model)
nuisance : array_like (optional)
nuisance parameters (of the problem)
hyper : array_like (optional)
list of hyper parameters (of the error distribution)
fitIndex : array_like or None
list of allpars to be fitted.
allpars : array_like (read only)
list of parameters, nuisance parameters and hyperparameters
Author Do Kester
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Constructor:
- Sample( id, parent, start, model, parameters=None,
fitIndex=None, copy=None)
- Constructor.
Parameters
----------
id : int
id of the sample
parent : int
id of the parent (-1 for Adam/Eve)
start : int
iteration in which the walker was constructed
model : Model
the model being used. Parameters are copied from this model.
parameters : array_like
list of model parameters
fitIndex : array_like
list of indices in allpars that need fitting
copy : Sample
the sample to be copied
Methods defined here:
- copy()
- Copy.
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