Hierarchical Bayesian Estimation for Cognitive Models using Particle Metropolis within Gibbs (PMwG): A tutorial

Estimating quantitative cognitive models from data is a staple of modern psychological science, but can be difficult and inefficient. Particle Metropolis within Gibbs (PMwG) is a robust and efficient sampling algorithm which supports model estimation in a hierarchical Bayesian framework. This talk will provide an overview of how cognitive modelling can proceed efficiently using PMwG, a new open-source package for the R language. PMwG, and the PMwG package, has the potential to move the field of psychology ahead in new and interesting directions, and to resolve questions that were once too hard to answer with previously available sampling methods.