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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