Position in Lausanne
27 October 2020Statistical Programming Manager Required: Leading CRO
27 October 2020Position in Lausanne
27 October 2020Statistical Programming Manager Required: Leading CRO
27 October 2020
ONLINE COURSE – Introduction to Bayesian modelling with INLA (BMIN01)
This course will be delivered live
TIME ZONE – Central European Standard Time (CEST) – however all
sessions will be recorded and made available allowing attendees from
different time zones to follow a day behind (please email
oliverhooker@prstatistics.com for full details or to discuss how we
can accommodate you).
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. This course will cover the basics on the
INLA methodology as well as practical modelling of different types of
data.
By the end of the course participants should:
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.
Please email oliverhooker@prstatistics.com with any enquiries.