Instructor - Dr Virgillio Gomez Rubio - His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data.
29th January - 2nd february 2024
Please feel free to share!
Courses are recorded to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes.
COURSE OVERVIEW - 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 for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporal data.
By the end of the course participants should:
Know the different types of spatial and spatio-temporal data available and how to work with them in R.
Know the different modelling approaches for spatial and spatio-temporal data.
Know how to visualize and produce maps of spatial and spatio-temporal data.
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 to spatial and spatio-temporal data.
Know how to define the different parts of a model with INLA.
Have the confidence to use INLA for their own projects.
Please email oliverhooker@prstatistics.com with any questions
Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01)
https://www.prstats.org/course/spatial-and-spatial-temporal-modelling-using-r-inla-sstm01/
Instructor - Dr Virgillio Gomez Rubio - His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data.
29th January - 2nd february 2024
Please feel free to share!
Courses are recorded to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes.
COURSE OVERVIEW - 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 for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporal data.
By the end of the course participants should:
Know the different types of spatial and spatio-temporal data available and how to work with them in R.
Know the different modelling approaches for spatial and spatio-temporal data.
Know how to visualize and produce maps of spatial and spatio-temporal data.
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 to spatial and spatio-temporal data.
Know how to define the different parts of a model with INLA.
Have the confidence to use INLA for their own projects.
Please email oliverhooker@prstatistics.com with any questions