EBSICL: Model Selection by Integrated Completed Likelihood criterion
In EBS: Exact Bayesian Segmentation

Description Usage Arguments Details Value Author(s) References See Also Examples

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)

1	EBSICL(x, prior=numeric())

`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

# changes for Poisson model
set.seed(1)
x<-c(rpois(125,1),rpois(100,5),rpois(50,1),rpois(75,5),rpois(50,1))
out <- EBSegmentation(x,model=1,Kmax=20)
bestK=EBSICL(out)$NbICL
print(bestK)