An application of Bayesian inference for solar-like pulsators

As the amount of data collected by space-borne asteroseismic instruments (such as CoRoT and Kepler) increases drastically, it will be useful to have automated processes to extract a maximum of information from these data. The use of a Bayesian approach could be very help- ful for this goal. Only a few attempts have been made in this way (e.g. Brewer et al. 2007). We propose to use Markov Chain Monte Carlo simulations (MCMC) with Metropolis-Hasting (MH) based algorithms to infer the main stellar oscillation parameters from the power spec- trum, in the case of solar-like pulsators. Given a number of modes to be fitted, the algorithm is able to give the best set of parameters (frequency, linewidth, amplitude, rotational split- ting) corresponding to a chosen input model. We illustrate this algorithm with one of the first CoRoT targets: HD 49933.