Learn the basics of Bayesian linear regression using the excellent PyMC Probabilistic Programming package. This focuses on model formulation in PyMC, interpretation, and how to make predictions on out-of-sample data.
This tutorial covers the fundamentals of Bayesian approaches to time series, model construction, and practical implementation, using real-world data for hands-on learning.
Explore Bayesian Poisson regression for modeling count data with Julia and Turing.jl. This tutorial includes model setup, implementation, and performance assessment with a practical example.
Applying Turing.jl package in Julia for a probabilistic approach to a classification problem on a real-world dataset.
Learn the basics of Bayesian linear regression using Julia and Turing.jl. This tutorial covers model formulation, implementation, and interpretation through a practical example.