The MCMC sampler was allowed to equilibrate with convergence being evaluated visually from the likelihood and parameter traces. For the simpler models, 5,000 iterations were sufficient for equilibration, while this increased to 20,000 for the most complex models. Also, using log scale sampling, we verified that the chains were able to converge even if started with initial parameter values far from the final posterior mean values. Posterior distributions were estimated from 100,000 iterations. The rate of the acceptance varies from model to model. For the baseline model we achieved a 25% rate of acceptance and for the most complex model (8 parameters), a rate of approx 10%. These values compare well with the ''golden'' acceptance rate for Random Walk Metropolis Hastings of 23% (Roberts [26] ).