Training Scientists to Perform Robust Bayesian Inference

In this talk, I will share my perspectives and experiences training scientists in Bayesian statistical inference, giving examples of what students have learned and applied. In particular, I will highlight:

• The importance of domain knowledge in model building.
• Development of tailor-made models, as opposed to off-the-shelf techniques.
• Visualization of results.
• Robust workflows to avoid statistical pitfalls.
• Use of real data sets in pedagogy and practice.

Please join the event.

About Justin Bois

Justin Bois is a Teaching Professor in the Division of Biology and Biological Engineering at the California Institute of Technology. He teaches nine different classes there, nearly all of which heavily feature Python. He is dedicated to empowering students in the biological sciences with quantitative tools, particularly data analysis skills.