  In the context of high-quality asteroseismic data provided by the NASA Kepler
mission, we developed a new code, termed Diamonds (high-DImensional And
multi-MOdal NesteD Sampling), for fast Bayesian parameter estimation and model
comparison by means of the Nested Sampling Monte Carlo (NSMC) algorithm, an
efficient and powerful method very suitable for high-dimensional problems (like
the peak bagging analysis of solar-like oscillations) and multi-modal problems
(i.e. problems that show multiple solutions). We applied the code to the peak
bagging analysis of solar-like oscillations observed in a challenging F-type
star. By means of Diamonds one is able to detect the different backgrounds in
the power spectrum of the star (e.g. stellar granulation and faculae activity)
and to understand whether one or two oscillation peaks can be identified or
not. In addition, we demonstrate a novel approach to peak bagging based on
multimodality, which is able to reduce significantly the number of free
parameters involved in the peak bagging model. This novel approach is therefore
of great interest for possible future automatization of the entire analysis
technique.
