Hierarchical Bayesian parameter estimation with the Particle Metropolis within Gibbs sampler

Hierarchical Bayesian techniques have proven to be a powerful tool for the estimation of model parameters and individual random effects. However many existing methods for estimating in this way are extensions to previously used methods, and therefore are not necessarily efficient for this purpose.I present an implementation in R of a new sampler based on the paper by Gunawan et al. (2020, JMP). This new approach has the benefit of being built for hierarchical estimation from the ground up and is easily parallelised. Additionally it allows for the estimation of the full parameter covariance matrix, providing the ability to model two tasks jointly and directly estimate correlations between parameters in the two tasks.The poster will provide an introduction to the approach, a brief overview of important use cases for the sampler and a short tutorial on using the package. References to more detailed information and how to access the package will also be provided.