Computes the exact BIC criterion: -Loglikelihood (data,K) and chooses the optimal number of segments as k= argmin(BIC)
Usage
EBSBIC(x, prior=numeric())
Arguments
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.
Value
NbBIC
An integer containing the choice of the optimal number of segments.
BIC
A vector of length Kmax returning -Loglikelihood (data,K).
Details
This function is used to choose the optimal K according to the BIC criteria.
References
Rigaill, Lebarbier & Robin (2012): Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems Statistics and Computing