Bayesian Modelling Using R-INLA (BMIN03) SOLD OUT
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: February 23-27, 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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- Overview
- Instructors
- Schedule
Course Description
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics of Bayesian inference and the INLA methodology as well as practical modelling of different types of data.
What You’ll Learn
During the course we will cover the following:
- Understand the basics of Bayesian inference.
- Understand how the INLA method works and its main differences with MCMC methods.
- Be able to fit models with the R-INLA package.
- Know how to interpret the output from model fitting.
- Be confident with the use of INLA for data analysis.
- Understand the different models that can be fit with INLA.
- Know how to define the different parts of a model with INLA.
- Be able to develop new latent effects not implemented in the R-INLA package.
- Know how to define new priors not included in the R-INLA package.
- Have the confidence to use INLA for their own projects.
Course Format
Interactive Learning Format
Each day 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 are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion 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 the course 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 the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is aimed at academics and postgraduate students working on projects related to data analysis and modelling who want to add the INLA methodology for Bayesian inference to their toolbox. It is also useful for applied researchers and analysts in public, private, or third-sector organisations who require the reproducibility, speed, and flexibility of a command-line language such as R. The course is designed for intermediate to advanced R users interested in data analysis and modelling, and participants should ideally have some background in probability, statistics, and data analysis.
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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