Strengthen your analytical toolkit for marine mammal research with our live online course Bayesian Modelling Using R & INLA (BMIN04).

https://prstats.org/course/bayesian-modelling-using-r-inla-bmin04/

Instructor: Dr Virgilio Gómez-Rubio, a leading expert in the application of INLA and author of Bayesian Inference with INLA.

Bayesian approaches are increasingly central to marine mammal science, particularly when dealing with sparse sightings, imperfect detection, spatial structure, hierarchical population processes, and complex survey designs. This applied training provides a practical introduction to Bayesian inference using the Integrated Nested Laplace Approximation (INLA) framework in R, combining conceptual foundations with hands-on implementation.

The methods taught are directly applicable to marine mammal research, including:

• Spatial modelling of distribution and density

• Spatio-temporal modelling of abundance trends

• Hierarchical population and demographic models

• Integrating multiple data sources (e.g., survey, telemetry, acoustic)

• Modelling imperfect detection and observation error

The course covers:

• Bayesian fundamentals and interpretation

• Specification and fitting of Bayesian models in R using INLA

• Model selection, comparison, and diagnostics

• Handling spatial, temporal, and spatio-temporal data

• Hierarchical and mixed-effects modelling in a Bayesian framework

• Practical strategies for reproducible Bayesian workflows

Delivered live online, this course offers direct interaction with the instructor, opportunities to discuss your own marine mammal datasets, and structured guidance through applied examples. Participants receive full course materials, example code, and post-course support.

Course details
Dates: 4–8 May 2026
Duration: 5 days (approximately 7 hours per day)
Format: Live online
Fee: £500

This course is suitable for postgraduate students, researchers, and professionals working in marine mammal ecology, conservation, and population assessment who want to apply Bayesian methods confidently using R and INLA.