  The peak bagging analysis, namely the fitting and identification of single
oscillation modes in stars' power spectra, coupled to the very high-quality
light curves of red giant stars observed by Kepler, can play a crucial role for
studying stellar oscillations of different flavor with an unprecedented level
of detail. A thorough study of stellar oscillations would thus allow for deeper
testing of stellar structure models and new insights in stellar evolution
theory. However, peak bagging inferences are in general very challenging
problems due to the large number of observed oscillation modes, hence of free
parameters that can be involved in the fitting models. Effciency and robustness
in performing the analysis is what may be needed to proceed further. For this
purpose, we developed a new code implementing the Nested Sampling Monte Carlo
(NSMC) algorithm, a powerful statistical method well suited for Bayesian
analyses of complex problems. In this talk we show the peak bagging of a sample
of high signal-to-noise red giant stars by exploiting recent Kepler datasets
and a new criterion for the detection of an oscillation mode based on the
computation of the Bayesian evidence. Preliminary results for frequencies and
lifetimes for single oscillation modes, together with acoustic glitches, are
therefore presented.
