Pros and Cons of Bayesian Pharmacometric Modeling Using BUGS

Bayesian modelingTwo distinguishing core notionsBasic elements of Baysian inferenceJust as with maximum likelihood methods, we begin with a modeldescribing the relationship between the data y and theunknown-valued parameters θ — a likelihood function p (y|θ).Prior knowledge (or belief) about model parameters θ isquantitatively described in terms of a probability distribution — aprior distribution p (θ).Bayes rule provides a rigorous basis for quantitative statisticalinference that considers both prior knowledge and new data viathe posterior distribution: p (θ|y) ∝ p (y|θ) p (θ)c○2009 Metrum Institute Bayesian Modeling Using BUGS AAPS 2009 3 / 26