Bayesian Modelling Using R-INLA Course (BMIN04)
Learn Bayesian modelling with the R-INLA package. Build, fit, and interpret INLA models, define priors and latent effects, and apply INLA to real data in a five day course.
Duration: 5 Days, 7 hours per day
Next Date: May 4-8, 2026
Format: Live Online Format
UK (GMT+1) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£500Registration Fee
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Course Description
Learn practical Bayesian modelling in this live online Bayesian modelling course using INLA.
This 5-day training teaches Bayesian inference, hierarchical modelling, and spatial analysis in R using the powerful R-INLA framework.
Designed for researchers and statisticians, this hands-on R-INLA Bayesian modelling course shows how to build, fit, and interpret real applied Bayesian models efficiently.
This online Bayesian modelling course using INLA introduces the theory and practice of Bayesian statistics with a focus on real data analysis in R. You will learn how to define priors, construct hierarchical models, perform model comparison, and interpret posterior distributions using R-INLA.
This R-INLA workshop combines conceptual understanding with practical modelling workflows used in ecology, epidemiology, and applied statistics.
What You’ll Learn
By the end of this Bayesian modelling with INLA training, you will be able to:
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Understand Bayesian inference and prior specification
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Fit Bayesian hierarchical models in R-INLA
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Perform spatial and spatiotemporal Bayesian modelling
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Compare models using DIC and WAIC
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Interpret posterior distributions and uncertainty
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Apply Bayesian modelling to real research datasets
These skills are widely used in modern applied statistics and computational biology.
Course Format
Interactive Learning Format
Each day of this Bayesian Modelling using R-INLA course features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions of this R-INLA workshop are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
We encourage open discussion during our R-INLA Bayesian modelling course. These sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during this R-INLA workshop will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during this online Bayesian modelling course using INLA course.
Post-Course Support
Participants will receive continued support via email for 30 days following Bayesian Modelling using R-INLA, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This online Bayesian modelling course using INLA is ideal for:
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PhD students working with hierarchical or spatial models
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Statisticians and data scientists learning Bayesian inference
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Ecologists, epidemiologists, and social scientists analysing complex data
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Researchers interested in R-INLA Bayesian modelling workflows
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Anyone building applied Bayesian models in R
Participants should have basic familiarity with R and regression modelling.
Bayesian modelling provides powerful tools for analysing complex data structures but requires specialised statistical and computational skills. This R-INLA Bayesian modelling course teaches practical, reproducible workflows for fitting hierarchical and spatial Bayesian models and interpreting posterior results.
You will leave confident in performing Bayesian modelling using R-INLA and applying Bayesian inference methods to real research datasets.
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.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
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. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Virgilio Gómez-Rubio
Virgilio is a statistician with deep expertise in Bayesian inference, spatial statistics, and statistical computing. His research focuses on the development and application of Bayesian methods for complex data structures, particularly using the Integrated Nested Laplace Approximation (INLA). He has made significant contributions to the R programming ecosystem through the development of widely used packages and tools for Bayesian modeling and spatial analysis.
Virgilio is the author of Bayesian Inference with INLA, a widely adopted reference in the field that received the 2022 SEIO–BBVA Foundation Award in Data Science and Big Data. He is committed to making advanced statistical methods accessible to a broad audience through clear documentation, open-source software, and active engagement with the research community via platforms like GitHub and ResearchGate.
Education & Career
• PhD in Statistics
• Author of Bayesian Inference with INLA
• Contributor to the R ecosystem with a focus on Bayesian and spatial modeling
Research Focus
Virgilio’s work centres on efficient Bayesian computation and the modelling of spatial and spatio-temporal data. He is particularly interested in applied Bayesian inference using INLA, as well as the integration of statistical methods into reproducible and scalable R workflows.
Current Projects
• Development of R packages for Bayesian and spatial analysis
• Applied research in epidemiology, environmental science, and spatial statistics
• Contributions to the ongoing development and documentation of the INLA methodology
Professional Consultancy
Virgilio provides expert support to academic and applied research teams in the areas of Bayesian modeling, spatial analysis, and statistical computing. His consultancy includes guidance on model design, computational methods, and reproducible workflows.
Teaching & Skills
• Teaches Bayesian inference, spatial statistics, and INLA in R
• Experienced in package development, reproducible research, and scientific communication
• Advocates for open-source tools and transparent, reproducible science
Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub
Session 0 – 00:30:00 – Introduction to the course
Session 1 – 01:00:00 – Introduction to Bayesian Inference
Practical 1 – 01:00:00 – Introduction to Bayesian Inference
Session 2 – 01:00:00 – Bayesian computation
Practical 2 – 01:00:00 – Bayesian computation
Session 3 – 01:00:00 – Introduction to INLA
Practical 3 – 01:00:00 – Introduction to INLA
Q and A and end of day summary – 00:30:00
Session 4 – 01:00:00 – Model fitting with INLA
Practical 4 – 01:00:00 – Model fitting with INLA
Session 5 – 01:00:00 – GLMMs with INLA
Practical 5 – 01:00:00 – GLMMs with INLA
Session 6 – 01:00:00 – Multilevel models with INLA
Practical 6 – 01:00:00 – Multilevel models with INLA
Q and A and end of day summary – 00:30:00
Session 7 – 01:00:00 – Time series
Practical 7 – 01:00:00 – Time series
Session 8 – 01:00:00 – Priors with INLA
Practical 8 – 01:00:00 – Prior with INLA
Session 9 – 01:00:00 – Spatial Models
Practical 9 – 01:00:00 – Spatial Models
Q and A and end of day summary – 00:30:00
Session 10 – 01:00:00 – Advanced features
Practical 10 – 01:00:00 – Advanced features
Session 11 –01:00:00 – New latent effect for R-INLA
Practical 11 –01:00:00 – New latent effect for R-INLA
Session 12 – 01:00:00 – Missing values and imputation
Practical 12 –01:00:00 – Missing values and imputation
Q and A and end of day summary – 00:30:00
Case studies, own data and problem solving.
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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