In Corsaro et al. (2015a), we used the Bayesian estimation code DIAMONDS for a peak-bagging analysis of 19 oscillating red giants. DIAMONDS uses the nested sampling method in which the prior is sampled under the hard constraint that the likelihood of each new point must be better than the one of the worst point, after which the latter is discarded from the “live” sample. To avoid a high rejection rate by sampling lowlikelihood regions, the algorithm only samples inside ellipsoids around the most interesting regions of the parameter space. These ellipsoids are constructed using the covariance matrix of live points, but since they do not exactly coincide with the true iso-likelihood contours, they are enlarged by a factor f to avoid missing relevant parts of the sampling space. To boost the performance, this enlargement is then gradually phased out as the live sample concentrates on the high-likelihood regions and the ellipsoids are becoming a progressively better approximation of the iso-likelihood contours.
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