Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated...

Reversible jump methods are the most commonly used Markov chain Monte Carlo tool for exploring variable dimension statistical models. Recently, however, an alternative approach based on birth-and-death processes has been proposed by Stephens for mixtures of distributions. We show that the...

Heterogeneity in patient populations is an important issue in health economic evaluations, as the cost-effectiveness of an intervention can vary between patient subgroups, and an intervention which is not cost-effective in the overall population may be cost-effective in particular subgroups....