Abstract:
Coronal seismology is based on the comparison between theoretical and observed properties of magnetohydrodynamic waves and oscillations in coronal magnetic structures. The aim is to infer unknown physical parameters and to assess model evidence. Bayesian model comparison relies on the analysis of two measures of evidence, the marginal likelihood and the Bayes factor. This is in contrast to approaches that are based on fitting a model to data. In this seminar, we first explain the principles and methods for calculating the Bayesian evidence in support of a model, conditional on observed data. We then illustrate this approach with example applications that aim to assess the plausibility of models for the structure of coronal and prominence waveguides and for the damping of their transverse oscillations.