  Solar flares are large-scale releases of energy in the solar atmosphere,
which are characterised by rapid changes in the hydrodynamic properties of
plasma from the photosphere to the corona. Solar physicists have typically
attempted to understand these complex events using a combination of theoretical
models and observational data. From a statistical perspective, there are many
challenges associated with making accurate and statistically significant
comparisons between theory and observations, due primarily to the large number
of free parameters associated with physical models. This class of ill-posed
statistical problem is ideally suited to Bayesian methods. In this paper, the
solar flare studied by Raftery et al. (2009) is reanalysed using a Bayesian
framework. This enables us to study the evolution of the flare's temperature,
emission measure and energy loss in a statistically self-consistent manner. The
Bayesian-based model selection techniques imply that no decision can be made
regarding which of the conductive or non-thermal beam heating play the most
important role in heating the flare plasma during the impulsive phase of this
event.
