Advanced Bayesian Statistics and Hierarchical Models

Some time ago, I posted my lecture materials whose aim was to give an introduction (and a solid theoretical basis) to Bayesian statistics. I promised to post a second lecture that will be more specialized... and here it is. In this lecture we will talk about:

• Bayesian asymptotic (not fundamental though)
• Monte Carlo Markov Chain algorithms such as Metropolis-Hastings and Gibbs samplers
• Bayesian hierarchical models
• Probabilistic graphical models, e.g., Directed Acyclic Graphs (DAG)
• Finite mixture models (briefly)
• Approximate Bayesian Computation (ABC)
• Apply the above methodologies to various applications on time to event data, football scoring models, longitudinal data (evolution of distance between hypophysis and the pterygomaxillary fissure), topic modelling (Latent Dirichlet Allocation)

As you can see it is a mix between theory and applications (and fun!). I hope you will find it useful. You can find all the materials here!