Figure 3.

Parameter covariation is an important type of information recovered by model calibration. (A) A parameter vector drawn from the peak of the joint posterior distribution (corresponding to a best-fit parameter vector) does not always have components whose values correspond to the peaks of the corresponding marginal distributions (as illustrated for relatively identifiable parameters k₁, k₃₆, k₆₄ and k₇₃). In this case, the k₁ component of the best-fit vector matches the mean value of the marginal distribution for k₁, but this is not true of k₃₆, k₆₄ and k₇₃ or in general. (B) Predicted trajectories arising from three different types of parameter sampling. In each set of panels, pink, blue and green denote representative good, fair and poor fits. Manifold sampling: left panel shows goodness-of-fit to experimental data of a few simulated EC-RP trajectories, and right panel shows the distribution of 1000 –ln(posterior) values sampled from the joint posterior distribution (right; joint sampling). Independent sampling: similar plots but for parameters sampled from independent marginal distributions ignoring covariation. The inset panel expands the distribution for the smallest values of –ln(posterior). Covariance matrix sampling: similar plots but sampling from a multivariate log-normal distribution with mean and covariance computed from the MCMC walk.