We applied the classic random walk Metropolis Hastings algorithm [21, 22] and a block-sampling of parameters due to the computationally expensive form of the likelihood [23, 24] . A log scale has been used for sampling as the parameters were all positive definite and were expected to potentially vary by orders of magnitude. However, linear scale sampling yielded similar results. The convergence of the chains was also very much improved (see Robert [25] for more on perfect sampling and reparameterization issues) compared with sampling on a linear scale. The model was coded in C and parallelized using OpenMP 2.0.