In practice, we make use of an adaptive covariance matrix version of the Metropolis-Hastings algorithm which helps identify the directions in parameter space which have the highest likelihood values. At each iteration of the algorithm, the covariance matrix of the multivariate normal distribution is updated and a scalar value is also updated to define the width of the distribution. In the results presented in this paper, we run our MCMC chains for 10,000 samples and discard the first 5,000 samples as 'burn in' (see ). To ensure efficiency in our Monte-Carlo sampling, we first find the location of the optimum, that is, the maximum likelihood estimate of θ using a standard global minimisation algorithm (we use the cma-es algorithm ), which we use as a seed point for the MCMC algorithm as described in. In the results section, these samples are shown as histograms which illustrate the nature of the posterior distribution.