Bayesian data analysis

Bayesian model selection at the group level

In experimental psychology and neuroscience the classical approach when comparing different models that make quantitative predictions about the behavior of participants is to aggregate the predictive ability of the model (e.g. as quantified by Akaike Information criterion) across participants, and then see which one provide on average the best performance. Although correct, this approach neglect the possibility that different participants might use different strategies that are best described by alternative, competing models.

Bayesian multilevel models using R and Stan (part 1)

Photo ©Roxie and Lee Carroll, In my previous lab I was known for promoting the use of multilevel, or mixed-effects model among my colleagues. (The slides on the /misc section of this website are part of this effort.) Multilevel models should be the standard approach in fields like experimental psychology and neuroscience, where the data is naturally grouped according to “observational units”, i.e. individual participants. I agree with Richard McElreath when he writes that “multilevel regression deserves to be the default form of regression” (see here, section 1.