we take q(θ cand |θ i ) to be a multivariate normal distribution. If the proposed parameter set contains any parameters outside the range of the prior, then the parameter set is assigned an acceptance probability of 0, i.e. it is rejected, and the previously accepted parameter set is added to the Markov chain -that is, θ i+1 = θ i . Otherwise, we compare θ cand to the current parameter set θ i by calculating the ratio of the posteriors of the two parameter sets. If the candidate parameter set has a greater posterior density value than the existing parameter set then it will be added to the Markov chain, that is θ i+1 = θ cand . Otherwise, (making use of Eq. 11) the proposed parameter is accepted with probability, r, given by