The performance of three versions of the Gibbs sampling algorithm, and of two versions of the Langevin-Hastings algorithm were studied in a specific application involving an analysis of a reaction norm model. Two datasets ...

Markov chain Monte Carlo (MCMC) enables fitting complex hierarchical models that may adequately reflect the process of data generation. Some of these models may contain more parameters than can be uniquely inferred from the distribution of the data...

The Markov chain Monte Carlo (MCMC) strategy provides remarkable flexibility for fitting complex hierarchical models. However, when parameters are highly correlated in their posterior distributions and their number is large, a particular MCMC...