Bayesian deconvolution of cyclostationary processes based on point processes
In this paper we address the problem of the Bayesian de-convolution of a widely spread class of processes, filtered point processes, whose underlying point process is a self-excited point process. In order to achieve this de-convolution, we perform powerful stochastic algorithm, the Markov chains Monte Carlo (MCMC), which despite their power have not been yet widely used in signal processing.