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Particle Metropolis within Gibbs: An R package for Bayesian Hierarchical Modelling

Authors
Reilly Innes
University of Newcastle ~ School of Psychology
Jon-Paul Cavallaro
University of Newcastle, Australia
Mr. Gavin Cooper
University of Newcastle ~ School of Psychology
Caroline Kuhne
University of Newcastle, Australia
Guy Hawkins
University of Newcastle ~ School of Psychological Sciences
Dr. Scott Brown
University of Newcastle ~ School of Psychology
Abstract

Bayesian Hierarchical modelling techniques are widely used in mathematical psychology, however, many existing methods of estimation are restricted to extensions of previous methods. Following a paper by Gunawan et al (2020, JMP), we present a new R package for a novel sampling methodology - Particle Metropolis within Gibbs (PMwG). This method of particle Markov chain Monte-Carlo provides a more efficient and reliable method of hierarchical model estimation. The R package provides simple functionality, allowing models to be built from the ground up by the user, and is easily parallelisable. Further, the method allows the full parameter covariance matrix to be estimated, which is highly useful in joint-modelling applications. Here, we introduce the PMwG methodology, provide a short tutorial for the ready to use R package and highlight several extensions of the method from the original paper.

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Cite this as:

Innes, R., Cavallaro, J.-P., Cooper, G., Kuhne, C., Hawkins, G., & Brown, S. (2021, February). Particle Metropolis within Gibbs: An R package for Bayesian Hierarchical Modelling. Paper presented at Australasian Mathematical Psychology Conference 2021. Via mathpsych.org/presentation/400.