Before revision:  The algorithm is a sequential Monte Carlo algorithm that approximates numerically the posterior distribution with a set of weighted samples . Results: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios , correlated sources.
After revision:  The algorithm is a sequential Monte Carlo algorithm that approximates numerically the posterior distribution with Monte Carlo techniques . Results: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios , correlated sources.