Bayesian Hierarchical

Bayesian Hierarchical Modeling with R/BUGS

The course is for researchers, who need to analyze data with a hierarchical or multi-level structure, missing values, imprecise measurement data, complex correlation patterns and other complexities found in practice. Bayesian hierarchical models offer a natural approach to handling these types of problems by the construction of statistical models which reflect the complexity of the data.

In this course we make emphasis in visualizing and exploring hierarchical data by using powerful graphical tools in R. The model building is performed by using Bayesian graphical models and computations by linking OpenBUGS (or JAGS) software with R.

Participants should be familiar in classical data analysis, ideally they should have some experience in applying mixed effects models (e.g. by using R, SPSS or SAS). To attend the course you do NOT need experience with Bayesian analysis, R or with OpenBUGS, these topics are covered during the first day of the course.

This is a 3 days intensive training course with 8 hours per day including lecturing and exercises. The course presentation is practical with many worked examples. Lectures are given in English. Discussions can be in English, German or Spanish.

This course is very popular with students and post-graduates. Therefore, we are offering a special reduced price for students..

Trainer und Dozenten

Dr. rer. nat. Pablo E. Verde arbeitet auf dem Gebiet der statistischen Modellierung in der medizinischen und klinischen Forschung, aktuell in der Evidenz-Synthese (Meta-Analysen). Er leitet die Arbeitsgruppe Biometrie im Koordinierungszentrum für klinische Studien der Heinrich-Heine-Universität Düsseldorf und forscht am Institut für Medizinische Soziologie der Heinrich-Heine Universität Düsseldorf. Er hat mehr als 20 Jahre internationale Erfahrung in statistischer Beratung, Forschung und Lehre auf den Gebieten Medizin, Landwirtschaft, Gesundheitsforschung und Risikoanalyse Finanz-Econometrie.

Pablo ist Experte der Statistiksoftware R und WinBUGS für MCMC Berechnungen. Seit 1998 ist er aktives Mitglied der R Community, wo er für die Übersetzung von R ins Spanische verantwortlich ist.

Seit 1990 lehrt er die Anwendung von S und R, zunächst für die Finanz-Branche, seit 2000 im akademischen Bereich.
Pablo ist visiting Lecturer am Department of Statistics an der Stanford University und seit 2007 Stanford community member. Seit 2000 ist Pablo Mitglied der Royal Statistical Society.


Day 1: Introduction to Bayesian data analysis and R/BUGS

  • Introduction to Bayesian analysis in practice
  • Getting started with R/OpenBUGS/JAGS
  • Bayesian analysis for multiple parameters models
  • Bayesian graphical models and MCMC computations
  • Bayesian regression modeling

Day 2: Bayesian Hierarchical Modeling with R/BUGS

  • Visualization hierarchical data with R
  • Statistical framework for Bayesian hierarchical models
  • Prior distributions for hierarchical models
  • Bayesian hierarchical models for longitudinal data
  • Predictions, model checking and model comparison for hierarchical models

Day 3: Advance topics in Bayesian Hierarchical Modeling

  • Multilevel modeling, complex patters of variation and other extensions
  • Hierarchical models for missing data problems
  • Time series, dynamic modeling and smoothing with hierarchical models
  • Bayesian evidence synthesis and meta-analysis