We explore the joint posterior distribution of parameters by Markov Chain Monte Carlo (MCMC) sampling [37, 38] . We sampled parameters on a logscale using a random walk update scheme. b and g were jointly updated, while 1, m and n were updated singly. Five MCMC chains were started from a variety of start points within a credible range to assess convergence. Convergence was achieved within 100 000 iterations for all models from all starting parameter values. For each model, the chain was run for 500 000 iterations including a burn in of 100 000. Parameter estimates and equal-tailed 95 per cent credible intervals were obtained from the posterior distribution of 80 000 values thinned from the last 400 000 samples of the MCMC chains.