AIC

AIC(*args)

Compute the Akaike information criterion.

Refer to Akaike Information Criterion (AIC).

Parameters:
sample2-d sequence of float

Tested sample.

modelDistribution or DistributionFactory

Tested distribution.

n_parametersint, 0 \leq k, optional

The number of parameters in the distribution that have been estimated from the sample. This parameter must not be provided if a DistributionFactory was provided as the second argument (it will internally be set to the number of parameters estimated by the DistributionFactory). It can be specified if a Distribution was provided as the second argument, but if it is not, it will be set equal to 0.

Returns:
estimatedDistDistribution

Estimated distribution (case factory as argument)

AICfloat

The Akaike information criterion.

Notes

This is used for model selection. In case we set a factory argument, the method returns both the estimated distribution and AIC value. Otherwise it returns only the AIC value.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> ot.FittingTest.AIC(sample, distribution)
2.793869...
>>> ot.FittingTest.AIC(sample, distribution, 2)
2.92720...
>>> fitted_dist, aic = ot.FittingTest.AIC(sample, ot.NormalFactory())
>>> aic
2.917389...