Introduction to Bayesian Hierarchical Modelling
Learn Bayesian hierarchical modelling with R, JAGS & Stan. A 24 hour on-demand course ideal for scientists and analysts working with complex structured data.
Duration: 24 Hours
Format: Recorded ‘on-demand’ Format
£350Registration Fee
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- Overview
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Course Description
This 24 hour course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.
What You’ll Learn
During the course will cover the following:
- Hierarchical regression models
- Hierarchical models for non-Gaussian data
- Hierarchical models vs mixed effects models
- Multivariate and multi-layer hierarchical models
- Advanced hierarchical models
- Shrinkage and variable selection
- Hierarchical models and partial pooling
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is designed for anyone interested in using R for data science or statistical analysis. R is a widely adopted tool across academic research, as well as in the public and private sectors, valued for its flexibility and robust statistical capabilities. Participants should have a basic understanding of regression methods and generalised linear models, along with prior experience using R. Specifically, they should be comfortable importing and exporting data, manipulating data frames, fitting basic statistical models, and generating simple exploratory and diagnostic plots.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop.
Prof. Andrew Parnell
Andrew is a statistician and professor working at the intersection of statistics, machine learning, and real-world scientific applications. His research focuses on developing and applying statistical methods for large, structured datasets, with applications spanning climate science, 3D printing, bioinformatics, and more. He works with a wide array of techniques, including Bayesian hierarchical models, time series analysis, and modern machine learning tools.
Andrew holds the Hamilton Professorship of Statistics at the Hamilton Institute, Maynooth University. He has co-authored over 90 peer-reviewed publications in high-impact journals such as Science, Nature Communications, and PNAS, as well as in leading statistical journals including Statistics and Computing, The Annals of Applied Statistics, JCGS, and JRSS Series C. He has extensive experience teaching Bayesian statistics, statistical learning, and applied modelling across undergraduate, postgraduate, and doctoral levels.
Education & Career
• Hamilton Professor of Statistics, Hamilton Institute, Maynooth University
• PhD in Statistics (Bayesian Methods for Complex Data)
• Internationally published researcher with over 90 peer-reviewed papers
• Active collaborator with interdisciplinary teams in science and engineering
Research Focus
Andrew’s work is centred on statistical methodology and its integration with machine learning for complex, structured data. He is particularly interested in how Bayesian inference and scalable modelling techniques can enhance data-driven research in the natural sciences, engineering, and public policy.
Current Projects
• Hierarchical Bayesian models for environmental and ecological datasets
• Machine learning methods for analysing high-dimensional, structured data
• Time series modelling for dynamic systems in science and industry
• Statistical approaches to reproducible, transparent modelling practices
Professional Consultancy
Andrew collaborates widely across disciplines, providing expert statistical advice on model development, uncertainty quantification, and data analysis pipelines. His applied consulting includes climate modelling, bioinformatics, additive manufacturing, and data-driven public health initiatives.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• ResearchGate
• Google Scholar
• ORCID
• LinkedIn
• GitHub
Session 1 – 03:00:00 – Simple hierarchical regression models
Session 2 – 03:00:00 – Hierarchical models for non-Gaussian data
Session 3 – 02:00:00 – Practical: Fitting hierarchical models
Session 4 – 03:00:00 – Hierarchical models vs mixed effects models
Session 5 – 03:00:00 – Multivariate and multi-layer hierarchical models
Session 6 – 02:00:00 – Practical: Advanced examples of hierarchical models
Session 9 – 03:00:00 – Shrinkage and variable selection
Session 9 – 03:00:00 – Hierarchical models and partial pooling
Session 9 – 02:00:00 – Practical: Shrinkage modelling
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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