EBSBIC: Model Selection by BIC Criterion
In EBS: Exact Bayesian Segmentation

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

Computes the exact BIC criterion: -Loglikelihood (data,K) and chooses the optimal number of segments as k= argmin(BIC)

1	EBSBIC(x, prior=numeric())

`x`	An object of class EBS returned by function EBSegmentation applied to data of interest.
`prior`	A vector of size Kmax giving prior probabilities for segment numbers.

This function is used to choose the optimal K according to the BIC criteria.

`NbBIC`	An integer containing the choice of the optimal number of segments.
`BIC`	A vector of length Kmax returning -Loglikelihood (data,K).

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, EBSICL, 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)
bestKBIC=EBSBIC(out)$NbBIC
print(bestKBIC)