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The following three sections mirror as much as possible the documentation for PyStan:

• Getting Started
• Optimization in Stan
• Avoiding recompilation of Stan models

Some useful tips

Before running

• Compiling Stan components from Matlab
• Compilation options
• Setting Stan parameters
• Setting initial conditions for parameters
• Using 'parfor'

While running

• Printing progress information to screen
• Using listeners to notify when sampling is complete
• Blocking the Matlab command line
• Stopping model fitting early

After running

• Extracting samples from StanFit object
• Printing CmdStan's fit summary

Examples

• Multivariate gaussian mixture model
• Watanabe-Akaike information criterion (WAIC)

Work in progress

• Linear regression
• Logistic regression
• Vector autoregression with covariates
• Hierarchical growth curves
• State space models

Aki Vehtari has translated the examples from Bayesian Data Analysis, 3rd ed into Matlab, with some examples using MatlabStan here.

Troubleshooting

• Solving issues with GLIBCXX and libstdc (Linux)