matstanlib: A MATLAB library for visualization, processing, and analysis of output from Bayesian models in Stan

It is increasingly common to use Bayesian modeling techniques that rely on Markov chain Monte Carlo (MCMC) methods, such as the variant of Hamiltonian Monte Carlo implemented by Stan (mc-stan.org). While excellent tools for the processing, visualization, and analysis of output from Stan exist in R (bayesplot, posterior) and Python (ArviZ), few such resources exist for MATLAB users. I created the matstanlib library to fill this gap. In this fast talk, I will demonstrate how matstanlib supports multiple stages of a modern Bayesian modeling workflow in MATLAB. First, I will show how matstanlib automates a full set of computational diagnostic checks, consistent with current best practices for Bayesian sampling methods (e.g., Vehtari et al., 2021; Betancourt, 2018). Next, I’ll review matstanlib’s diagnostic plots, from trace plots to ESS interval plots to parameter recovery plots, which can be used to better understand model performance. Finally, I’ll show how matstanlib can facilitate model-based inference with plotting functions for the visualization of posterior densities and intervals, and analysis functions for the computation of density estimates and model comparison metrics. This fast talk will also serve as a quick-start guide for working with the matstanlib library.