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BAYESIAN ESTIMATION OF POSTERIOR DENSITIES FOR PHARMOCOKINETIC MODELS HAVING CHANGING PARAMETER VALUES

This paper considers the updating of posterior densities for pharmacokinetic models which have changing parameter values. For estimation purposes, the interactive multiple model (IMM) estimation algorithm is proposed, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and MAP Bayesian estimation methods, which are presently popular for pharmacokinetic problems where parameter values do not change. The methods are evaluated using both simulated and real clinical data for the drug Tobramycin.