Bayesian Training
Bayesian Training equips statisticians with hands-on skills to craft priors, calculate posteriors, interpret vaccine efficacy using credible intervals, conduct robustness checks, and deliver clear visual summaries for solid, data-backed decisions in vaccine trials.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Bayesian Training delivers a targeted, practical roadmap for dissecting vaccine trial data with assurance. Master binomial models, realistic trial setups, expert-driven priors for vaccine efficacy, and Bayesian updates. Gain expertise in posterior summaries, robustness tests, dodging pitfalls, and conveying uncertainty sharply to decision-makers in live studies.
Elevify advantages
Develop skills
- Bayesian VE modeling: build and analyze binomial vaccine efficacy models fast.
- Prior elicitation: turn expert VE beliefs into practical Beta priors that work.
- Posterior analysis: compute VE means, credible intervals, and Pr(VE>50%).
- Robustness checks: run prior sensitivity, skeptical priors, and prior predictive.
- Communication: explain Bayesian VE results clearly to technical stakeholders.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
Your classes are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of my interest without needing to change platforms... I'm grateful for everything you do, I've already recommended you to other people...

Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can change chapters and skip content I don't need.

Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!

Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos help a lot with learning.

André FelipePrompt Engineering Student
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