Global system analysis (GSA) was applied to parameter estimation of dynamic process models. First, the posterior distribution of the model parameters was estimated by quasi-Monte Carlo (QMC) simulations or uncertainty analysis. The expected variance of the estimated parameters by GSA was in general smaller than those were obtained by local search for the maximum likelihood. Second, sensitivity analysis was performed as an alternative application of GSA for the same mathematical models and testing data. The total effect index should serve as a quantitative measure of the robustness of each estimated parameter. Two process models were studied to demonstrate effectiveness of the proposed methodology based on GSA: a bio-reactor and a catalytic reactor. Parallelised computation allowed for sampling as many as 500,000 combinations of the model parameters in reasonable amount of time.
Part of the book: New Insights into Bayesian Inference