Bayesian estimation and detection of shot noise processes using reversible jumps
We propose an original algorithm for the Bayesian joint estimation and detection of shot noise processes. The solution we propose relies on Markov chain Monte Carlo methods and provides the a posteriori probability density of the unknown parameters conditionally to the observations. The solution we propose provides many degrees of freedom for the inclusion of any a priori knowledge.