Bayesian Methods

BA in Statistics · Universitat de Barcelona – Universitat Politècnica de Catalunya (UB–UPC) · 2026–27

Bayesian Methods (BA in Statistics, UB–UPC): Joel Ardiaca teaches the practical sessions, where students fit Bayesian models in R and JAGS.

Practical sessions

My role

I teach the practical sessions: computer labs in which students work through case studies and fit the models from the theory classes in R and JAGS. In 2026–27 the course is coordinated by Ignasi Puig.

About the course

A course in the interuniversity BA in Statistics (UB–UPC). 6 ECTS: 30 hours of theory classes and 30 hours of computer practicals. Students are expected to know basic probability, statistical inference and R.

The goal is for students to master Bayesian modelling in both theory and practice: from designing the study that produces the data to analysing it and drawing conclusions.

  1. The Bayesian model: likelihood, posterior distribution, prior and posterior predictive distributions, choosing the prior
  2. Bayesian inference: the posterior as an estimator, point and interval estimation, hypothesis tests
  3. Bayesian computation: Markov chain Monte Carlo (MCMC) simulation and chain convergence
  4. Hierarchical models
  5. Model checking and model building

Assessment: quizzes (10%), practical tests (20%), exam (40%) and a project (30%).

Syllabus

Syllabus (PDF, in Catalan)

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