Joint Bayesian decomposition of a spectroscopic signal sequence with RJMCMC

This article presents a method for decomposing a sequence of spectroscopic signals into a sum of peaks whose centers, amplitudes and widths are estimated. Since the peaks exhibit a slow evolution through the sequence, the decomposition is performed jointly on every spectra. To this end, we have developed a Bayesian model where a Markov random field favors a smooth evolution of the peaks through the sequence. The main contribution concerns the estimation of the peak number using the reversible jump MCMC algorithm. We show the accuracy of this approach on synthetic and real data.