Recent developments in instrumentation ( e.g. , in particular the Kepler and CoRoT satellites ) provide a new opportunity to improve the models of stellar pulsations .
Surface layers , rotation , and magnetic fields imprint erratic frequency shifts , trends , and other non-random behavior in the frequency spectra .
As our observational uncertainties become smaller , these are increasingly important and difficult to deal with using standard fitting techniques .
To improve the models , new ways to compare their predictions with observations need to be conceived .
In this paper we present a completely probabilistic ( Bayesian ) approach to asteroseismic model fitting .
It allows for varying degrees of prior mode identification , corrections for the discrete nature of the grid , and most importantly implements a treatment of systematic errors , such as the “ surface effects. ” It removes the need to apply semi-empirical corrections to the observations prior to fitting them to the models and results in a consistent set of probabilities with which the model physics can be probed and compared .
As an example , we show a detailed asteroseismic analysis of the Sun .
We find a most probable solar age , including a 35 \pm 5 million year pre-main sequence phase , of 4.591 billion years , and initial element mass fractions of X _ { 0 } = 0.72 , Y _ { 0 } = 0.264 , Z _ { 0 } = 0.016 , consistent with recent asteroseismic and non-asteroseismic studies .