Bayesian linear mixed-effects models as an alternative to Frequentist method

Yair Haendler (LLF)
Bayesian linear mixed-effects models as an alternative to Frequentist method

Topics:

• What is Bayesian analysis.
• Explanation of basic concepts (prior, likelihood, posterior).
• Hypothesis testing with Bayesian models (estimating posteriors for model parameters).
• Types of priors.
• Fitting models with brms.
• Choosing the appropriate model for the data.
• Drawing inference from the posteriors.

Requisites: Extensive theoretical knowledge of, and experience working with, linear mixed-effects models; participation in the workshop Linear mixed-effects models in R – Frequentist method (lme4 package).