possible to normalise the posterior distribution or calculate the evidence. By calculating the ratio of the Bayesian evidence for the case where hyper-parameters are used, to where they are not, it can be inferred whether they should be included. If this ratio is much larger than unity then there is a significant relative inconsistency in the error estimates of the two data sets and their inclusion is warranted.

Implementation
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Implementation of the model fitting is performed using BAYESYS [@Skilling:2004], a powerful Bayesian optimiser that uses the Markov Chain Monte Carlo method. Estimates of the posterior probability distribution are obtained for the model parameters by using all of the proposal distribution engines available to the program. BAYESYS provides the modelling code with parameter samples in the form of a unit hyper-cube from which the SZ and X-ray surface brightness likelihoods are calculated. The program initialises with a burn-in phase that evolves the MCMC chains from sampling just the prior distribution to locating regions of significant posterior probability. This is performed in a series of steps known as selective annealing (due to the analogous relationship with annealing in thermodynamics), during which samples of parameters are drawn from a modified posterior given 