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Zurich R Courses

R-INLA: Bayesian inference for latent Gaussian models

Dozierende Prof. Dr. Sara Martino, Norwegian University of Science and Technology
Abschluss Confirmation of participation
Zielpublikum

Novice and advanced R users from all professional groups.

Kosten
  • CHF 600.- for members of UZH/ETH and associated institutes
  • CHF 800.- for alumni of UZH/ETH, members of other universities, the public sector and non-profit organizations
  • CHF 1200.- for companies

Persons without current employment can register for the UZH/ETH fee upon request.

Kurssprache English
Beschreibung

This 2-day course with practical sessions aims to give an introduction to the R packages INLA and inlabru, which provide a simple way to perform Bayesian inference for latent Gaussian models (LGMs).

LGMs are among the most commonly used classes of models in statistical applications and include generalized linear models, generalized additive models, smoothing spline models, linear state space models, log-­Gaussian Cox processes and more. For this class of models INLA provides a fast and reliable inference that is also easy to implement in practice thanks to the R-INLA library. We will also introduce the  inlabru library. This allowes to   extend the GAM-like model class to more general nonlinear predictor expressions. Both libraries  use the  "formula'' framework of R  to specify a wide variety of models in a familiar and streamlined way which only requires small changes in the code to add or remove random effects, temporal effects, spatial effects and so on.

Topics of the course include:

  • introducing the class of latent Gaussian models ­ describing the "big picture'' of the INLA algorithm ­ introducing the basic elements of the R package INLA, such as model definition and output inspection
  • expanding the class of models amenable to inla: inlabru and importance sampling with inla.
  • prediction and model choice ­implementing joint models in R-INLA
  • outlining further advanced features

Examples will be presented from the fields of biostatistics, spatial statistics,  ecology etc.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.

Daten March 17-18 2022