Andrew W. Thomas

TL;DR: How and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design are discussed and how the framework may be extended.

TL;DR: A balanced critical appraisal of the BUGS software is provided, highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects.

TL;DR: Introduction: Probability and Parameters Probability Probability distributions Calculating properties of probability distributions Monte Carlo integration Monte Carlo Simulations Using BUGS using BUGs to simulate from distributions Transformations of random variables Complex calculations using Monte Carlo Multivariate Monte Carlo analysis Predictions with unknown parameters

TL;DR: It is described how a full Bayesian analysis can deal with unresolved issues, such as the choice between fixed- and random-effects models, the choice of population distribution in a random- effects analysis, the treatment of small studies and extreme results, and incorporation of study-specific covariates.

TL;DR: A thorough discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD is provided, in an easy to follow format, so that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.