How to fit reinforcement learning models to behavioral data using Bayesian inference. This part is focused on the hierarchical Bayesian modeling and particularly on the usage of hBayesDM package. Approaches for the model diagnostic, selection, validation are discussed. The post also goes over groups comparison using posterior distributions of model parameters. Additionally, a brief results comparison between Bayesian inference and Maximum Likelihood Estimation is provided.
How to fit reinforcement learning models to behavioral data using Maximum Likelihood Estimation (MLE). The main goal is to show how to answer research questions using modeling. Post goes over important steps of modeling, such as model selection, model validation, and data generation.
How to get maximum performance of your A/B test when you have any prior information about users.