Bayesian Inference and Probabilistic Programming for Researchers
This course provides a rigorous foundation for researchers to implement Bayesian hierarchical models using probabilistic programming frameworks in Python. It equips staff to move beyond classical null-hypothesis significance testing, enabling more nuanced evidence synthesis and uncertainty quantification in complex research environments.
£520
Certified Bayesian Research Professional
- Pass mark 70%
- Certificate on completion
- Verifiable public certificate ID
- English · available globally
Bayesian Inference and Probabilistic Programming for Researchers: the short answers
- What is Bayesian Inference and Probabilistic Programming for Researchers?
- This course provides a rigorous foundation for researchers to implement Bayesian hierarchical models using probabilistic programming frameworks in Python. It equips staff to move beyond classical null-hypothesis significance testing, enabling more nuanced evidence synthesis and uncertainty quantification in complex research environments.
- Who is Bayesian Inference and Probabilistic Programming for Researchers for?
- It is pitched at Advanced level for Research Scientists working in Data Analytics with Python or R, and is studied online from any country.
- How long does Bayesian Inference and Probabilistic Programming for Researchers take?
- Around 28 hours of study, plus the assessed assignments and one applied project.
- How is the certification assessed?
- Every assignment and the final project are assessed automatically against the published criteria. The pass mark is 70%, and an item that does not pass can be resubmitted.
- What do I receive when I pass?
- Certified Bayesian Research Professional, issued as a certificate with a unique ID that employers can check in the public certificate register.
- How much does it cost?
- £520 once, with no subscription.
What you will be able to do
- Define generative models for complex data structures using PyMC
- Perform posterior distribution estimation via Markov Chain Monte Carlo techniques
- Execute model comparison using Leave-One-Out cross-validation
- Apply hierarchical modeling to account for nested data variance
- Interpret and communicate uncertainty intervals to stakeholders
Written by Dr. Elena Vance, Senior Statistician. Every submission is assessed automatically against the published criteria for Certified Bayesian Research Professional, and the certificate is issued the moment the assessment is complete.
20 topics in this subject area
Each topic is written to be read on its own. Open a topic to read its description.
The curriculum for this course is being written. Sign in to start reading.
Course Builder
Prepare your assignments and one applied project in your own words and submit them here. The AI assesses each piece as soon as you submit it, and your certificate is issued immediately once the assessment is complete.
Sign in to read the topics, write your assignments and project, and receive your certificate the moment the AI assessment is complete.