Calling R from Matlab – flat file communication

In the last post I showed how to fit a Bayesian hierarchical model in R to estimate experimental parameters. Our main data analysis pipeline uses Matlab, and we’d like to integrate these two environments for the purpose of analysis. The example involved JAGS, which does have a direct Matlab interface in addition to the more common R one, but more … More Calling R from Matlab – flat file communication

Adaptive Metropolis-Hastings – a plug-and-play MCMC sampler

Gibbs sampling is great but convergence is slow when parameters are correlated. If the covariance structure is known, you can reparametrize to get better mixing. Alternatively you can keep the same parametrization but switch to Metropolis-Hastings with a Gaussian proposal distribution whose covariance is similar to the model parameters. But what if you don’t know … More Adaptive Metropolis-Hastings – a plug-and-play MCMC sampler