Computes the exact ICL criterion: -Loglikelihood (data,K) + H(m|K) where H is the entropy of the segmentation, and chooses the optimal number of segments as k= argmin(ICL)
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x |
An object of class EBS returned by function EBSegmentation applied to data of interest. |
prior |
A vector of length Kmax giving prior probabilities on the value of K. Default value is uniform on 1:Kmax. |
This function is used to compute the entropy of the segmentation in k segments (for k in 1 to Kmax) and choose the optimal K according to the ICL criteria.
NbICL |
An integer containing the choice of the optimal number of segments. |
ICL |
Vector of length x$Kmax containing the ICL values. |
Alice Cleynen
Rigaill, Lebarbier & Robin (2012): Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems Statistics and Computing
EBSegmentation
, EBSBIC
, EBSPostK
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