Section: New Results

On-line changepoint detection and parameter estimation with application to genomic data

We propose in [10] an efficient on-line changepoint detection algorithm for an important class of Bayesian product partition models. The algorithm allows to estimate jointly on-line the static parameters of the model using a recursive maximum likelihood estimation strategy. This particle filter type algorithm has a computational complexity which scales linearly both in the number of data and the number of particles. We demonstrate our methodology on a synthetic and two real world datasets from RNA transcript analysis. On simulated data, it is shown that our approach outperforms standard techniques used in this context and hence has the potential to detect novel RNA transcripts.