Welcome to BayesJump

BayesJump provides the resources and support for instructors to make the leap into Bayesian statistics.
This is an open-source, collaborative repository designed to help you learn Bayesian concepts and effectively teach them in your classroom.

The Challenge:

In today’s STEM education—particularly in the life sciences—most textbooks and curricula are built around frequentist statistics.
This creates a cycle where instructors teach the methods they were taught, leaving little room for the powerful, intuitive framework of Bayesian inference.
We aim to break that cycle. We want to give instructors the confidence they need to be able to teach introductory Bayesian ideas.

Our Solution:

BayesJump is a central hub for high-quality, open-source educational materials that bridge this gap.
Here you will find:

• Instructor Guides: Primers and step-by-step guides to help you build confidence with Bayesian concepts yourself.
• Lesson Plans & Slides: Ready-to-use (or adapt) lecture materials, complete with learning objectives and notes.
• Code Notebooks: Practical examples and tutorials in R and Python that you and your students can run and explore.
• Real-World Datasets: Curated datasets, especially from the life sciences, that are perfect for Bayesian analysis.
• Modular curricula Units that you can incorporate into existing courses alongside your regular content.
• Full Course Resources Textbooks and syllabi for teaching full courses centered on Bayesian approaches.

Who Is This For?

This repository is for any educator looking to integrate Bayesian thinking into their curriculum, including:

• University and college instructors in biology, ecology, psychology, and other STEM fields.
• Graduate student teaching assistants (TAs).
• Curriculum designers and educational developers.
• Self-learners who want a teaching-focused approach to Bayesian stats.

How to Contribute

This is a developing community-driven project, and we welcome your contributions!

Whether you have a lesson plan to share, found a great dataset, or want to fix a typo, please see our contributions page for guidelines.

Are you ready to learn and teach Bayes? Let’s make the jump together!