Luca Tardella

The registration key will be delivered to all students during the first lecture. If you miss it please send an email to luca.tardella@uniroma1.it

A comprehensive overview as a pdf file can be downloaded from here: bit.ly/SMDS-2

This is an introductory course about Bayesian inference and Bayesian modelling for data analysis. We will balance between theoretical and analytical tools and practice. In particular for practical implementation of Bayesian models on real data we will make use of some software for Bayesian modelling and inference (R, BUGS/JAGS).

In this course we will cover:

• Introduction to Bayesian Thinking
  In this part we will cover basic definitions, Bayesian model, Bayes’ theorem. Subjective prior elicitation and some noninformative or default prior choices (e.g. Jeffreys’Rule). Conjugate analysis. Techniques and tools for characterizing and summarizing posterior distributions. Bayes Factor, introduction to multi-model inference and Bayesian model choice.
  
  
• Multiparameter Inference
  This part considers the multivariate normal model and multinomial models and introduce to approximate random sampling from a multivariate distribution. Bayesian inference in the presence of missing data.
  
  
• MC & MCMC
  Introduction to Monte Carlo and Monte Carlo Markov Chain sampling techniques for approximating expectations and distributions: Gibbs Sampling and Metropolis Hastings algorithms.