Bayesian Inference using Stan
London, UK | July 17, 2019
We will begin by surveying probability theory, Bayesian inference, Bayesian computation, and a robust Bayesian workflow in practice, culminating in an introduction to Stan and the implementation of that workflow. With a solid foundation, we will continue with a discussion of regression modelling techniques along with their efficient implementation in Stan, spanning linear regression, discrete regression, and homogeneous and heterogeneous logistic regression. Finally, we will discuss the basics of hierarchical modelling and, time permitting, multilevel modelling.