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One way of revealing the nature of the coronal heating mechanism is bycomparing simple theoretical one dimensional hydrostatic loop models withobservations at the temperature and/or density structure along these features.The most well-known method for dealing with comparisons like that is the$\chi^2$ approach. In this paper we consider the restrictions imposed by thisapproach and present an alternative way for making model comparisons usingBayesian statistics. In order to quantify our beliefs we use Bayes factors andinformation criteria such as AIC and BIC. Three simulated datasets are analyzedin order to validate the procedure and assess the effects of varying error barsize. Another two datasets (Ugarte-Urra et al., 2005; Priest et al., 2000) arere-analyzed using the method described above. In one of these two datasets(Ugarte-Urra et al., 2005), due to the error estimates in the observedtemperature values, it is not posible to distinguish between the differentheating mechanisms. For this we suggest that both Classical and Bayesianstatistics should be applied in order to make safe assumptions about the natureof the coronal heating mechanisms.

Constraining coronal heating: employing Bayesian analysis techniques to improve the determination of solar atmospheric plasma parameters