Bayesian Spatial Prediction with R-INLA

We are pleased to host Corinna Perchtold from French Geological Survey (BRGM) for this 90-minute hands-on tutorial with R-INLA. It is suitable for R users with no previous experience on INLA. We encourage you to participate in-person for the tutorial. You are also welcome join online via Zoom.

As we promote gender diversity and inclusion in the R community, all genders and skill levels in R are welcome!

Using real data, we will:

Build a complete spatial model step by step
Predict on a grid
Generate diagnostic plots
Check the predictions through cross-validation
If time permits, introduce spatio-temporal and areal models

ℹ️ Abstract
Environmental data are often coming from measurement stations. The goal is to map the data over the whole domain of interest, with an honest statement of uncertainty. Bayesian geostatistical models do this well, but fitting them with MCMC (Markov chain Monte Carlo) is slow, because each iteration works with a dense covariance matrix. INLA (Integrated Nested Laplace Approximation) offers a fast deterministic alternative for latent Gaussian models. Combined with the SPDE (stochastic partial differential equations) approach, it represents a Matérn field on a triangle mesh with a sparse precision matrix.

💡 What to prepare

Install the R-INLA package by following the guide at https://www.r-inla.org/download/, and bring your laptop to the session.

🗺️ Location
In-person or Online via Zoom
Seminar Room DA green 02A / Sem.R. DA grün 02 A
(DA02E08)
2th floor, green area
TU Wien Freihaus
Wiedner Hauptstraße 8-10, 1040 Vienna (map)

Zoom link will be made available to the participants before the event.

📍 How to find us
In the YELLOW area of TU Wien Freihaus, take the lift to the 2nd floor. After exiting the lift, walk straight pass the stairs within the yellow area and turn right to find the green area. Walk through the door and go straight to find the room.
Use this floor plan to help locate the room.