This course on Bayesian statistics offers an in-depth exploration of both the theoretical framework and calculation methods in Bayesian statistics. Practical sessions with Jasp and Python will facilitate understanding and application of Bayesian inference. Finally, more advanced topics will be discussed.

This course presents the basics of Causal inference. how to create directed models without cycle and how to evaluate them to find unbiased effect. A processing string will be implemented using Python.

MCMC Sampler

This application allows you to visualize the operation and dExplore the different methods ofSampling used in Bayesian methods

Distributions and Bayes Formula

This application makes it possible to manipulate the parameters of different distributions, in order to see their impacts and to visualize theImpact of Prior and Likelihood on posterior distribution

Model Builder

This application allows you to build models interactively andExport an image of the created template