Bayesian Modeling

Diffusion/Wiener Model Analysis with brms – Part II: Model Diagnostics and Model Fit

This is the considerably belated second part of my blog series on fitting diffusion models (or better, the 4-parameter Wiener model) with brms. The first part discusses how to set up the data and model. This second part is concerned with perhaps the most important steps in each model based data analysis, model diagnostics and […]

Diffusion/Wiener Model Analysis with brms – Part I: Introduction and Estimation

Stan is probably the most interesting development in computational statistics in the last few years, at least for me. The version of Hamiltonian Monte-Carlo (HMC) implemented in Stan (NUTS, ) is extremely efficient and the range of probability distributions implemented in the Stan language allows to fit an extremely wide range of models. Stan has […]

Hierarchical MPT in Stan I: Dealing with Convergent Transitions via Control Arguments

I have recently restarted working with Stan and unfortunately ran into the problem that my (hierarchical) Bayesian models often produced divergent transitions. And when this happens, the warning basically only suggests to increase adapt_delta: Warning messages: 1: There were X divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. 2: Examine the pairs() plot […]