Bayesian deconvolution of noisy filtered point processes

The detection and estimation of filtered point processes using noisy data is an essential requirement in many seismic, ultrasonic, and nuclear applications. We address this joint detection/estimation problem using a Bayesian approach, which allows us to easily include any relevant prior information. Performing Bayesian inference for such a complex model is a challenging computational problem as it requires the evaluation of intricate high-dimensional integrals. We develop here an efficient stochastic procedure based on a reversible jump Markov chain Monte Carlo method to solve this problem and prove the geometric convergence of the algorithm. The proposed model and algorithm are demonstrated on an application arising in nuclear science.