hyperspy.utils.model_selection module

hyperspy.utils.model_selection.AIC(model)

Calculates the Akaike information criterion

AIC = 2 k - 2 ln(L)

where L is the maximum likelihood function value, k is the number of free parameters.

hyperspy.utils.model_selection.AICc(model)

hyperspy.utils.model_selection.BIC(model)

Calculates the Bayesian information criterion

BIC = -2 * ln(L) + k * ln(n)

where L is the maximum likelihood function, k is the number of free parameters, and n is the number of data points (observations) / sample size.