Model selection by MCMC computation
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using penalized contrasts for the change-point problem
Signal Processing
Proceedings of the 13th annual ACM international conference on Multimedia
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Efficient computation of the hidden Markov model entropy for a given observation sequence
IEEE Transactions on Information Theory
Computational Statistics & Data Analysis
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In this paper, we consider the Integrated Completed Likelihood (ICL) as a useful criterion for estimating the number of changes in the underlying distribution of data, specifically in problems where detecting the precise location of these changes is the main goal. The exact computation of the ICL requires O(Kn^2) operations (with K the number of segments and n the number of data-points) which is prohibitive in many practical situations with large sequences of data. We describe a framework to estimate the ICL with O(K^2n) complexity. Our approach is general in the sense that it can accommodate any given model distribution. We checked the run-time and validity of our approach on simulated data and demonstrate its good performance when analyzing real Next-Generation Sequencing (NGS) data using a negative binomial model. Our method is implemented in the R package postCP and available on the CRAN repository.