Introduction to Bayesian statistics and modelling

23 et 30 octobre 2026, 9h-18h

Dr Michaël Papinutto, Office Fédéral de la Statistique, StatisCau

This two-day course on Bayesian Statistics offers a comprehensive exploration of both theoretical framework and computational methods in Bayesian statistics. Starting with an historical overview of inferential statistics, contrasting the Bayesian and Frequentist paradigms, participants will recap frequentist statistics and delve gently into Bayesian concepts, including Bayes' Theorem, probability distributions, and Bayes Factors. Hands-on sessions with JASP and using Python with the PyMC library will facilitate the understanding and application of Bayesian inference. Finally, more advanced topics will be covered, enabling students to gain a deeper knowledge of the Bayesian framework through probabilistic programming, thus becoming autonomous in their approach to Bayesian inference and modelling.

En ligne

 Participants will need their laptops and should install the following software:

• JASP
• (Option 1) Have a Google Colaboratory environment ready to use (a subscription is not required)

OR

• (Option 2) Install and configure Visual Studio Code with Anaconda to run Notebooks and Python code. Required Python packages: Arviz, PyMC, Bambi, Pandas, Numpy, seaborn, matplotlib.

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