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A gentle introduction to Bayesian Estimation

  • 12 - 16 August 2024
  • Utrecht
  • Methodology
  • 1.5 ECTS

We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, sensitivity analyses, and the usefulness of a specific class of priors called shrinkage priors. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics). We have prepared many exercises to enable students to get hands-on experience.

The popularity of Bayesian statistics has increased over the years. However, Bayesian methods are not a part of the statistics curricula in most graduate programmes internationally. The Bayesian framework can handle some commonly encountered problems in classical statistics, such as the lack of power in small sample research and convergence issues in complex models. Furthermore, some researchers prefer the Bayesian framework because it sequentially updates knowledge with new data instead of requiring that each new study tests the null hypothesis that there is no effect in the population. The main focus of the course is on conceptually understanding Bayesian inference and applying Bayesian methods.

The instructors will clarify the differences between the philosophies and interpretations in classical and Bayesian frameworks. They illustrate how different types of research questions can be answered using Bayesian methods. This course will also give students experience running Bayesian analyses and interpreting results and instruct participants on the prevailing ‘best practices’ for writing a scientific article based on Bayesian statistics. Participants will emerge from the course with knowledge about how to apply Bayesian methods to answer their research questions and with the ability to understand articles that examine and use Bayesian methods.

  • Utrecht University