  A novel tool, based on Bayesian filtering framework and expectation
maximization algorithm, is numerically and experimentally demonstrated for
accurate frequency comb noise characterization. The tool is statistically
optimum in a mean-square-error-sense, works at wide range of SNRs and offers
more accurate noise estimation compared to conventional methods.
