ONLINE COURSE – Introduction to Bayesian modelling with INLA (BMIN01) This course will be delivered live
9 November 2020 - 13 November 2020£520
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
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 firstname.lastname@example.org for full details or to discuss how we can accommodate you).
Please feel free to email email@example.com with any questions, full course detials below.
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.
Academics and post-graduate students working on projects related to data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.
Venue – Delivered remotely
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
The course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference.
Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful.
Equipment and software requirements
A laptop/personal computer with any operating system (Linux, Windows, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Monday 9th – Classes from 10:00 to 17:00
Introduction to the course
Key concepts related to Bayesian inference
Models with conjugate priors
Introduction to Bayesian hierarchical models
Computational methods for Bayesian inference
Introduction to the INLA methodology
Fitting generalized linear models with INLA and the R-INLA package
Understanding and manipulating the output from model fitting with R-INLA
Tuesday 8th – Classes from 10:00 to 17:00
Fitting generalized linear mixed models with R-INLA
Types of latent effects in R-INLA
Models with i.i.d. latent effects
Fitting multilevel models with R-INLA
Models with correlated latent effects
Fitting time series models with R-INLA
Wednesday 9th – Classes from 10:00 to 17:00
Priors in R-INLA
Setting priors in R-INLA
Introduction to Penalized Complexity priors (PC-priors)
Defining new priors in R-INLA
Spatially correlated random effects
Fitting spatial models with R-INLA
Visualizing the output from spatial models and mapping
Thursday 10th – Classes from 10:00 to 17:00
Advanced features in R-INLA
Computing linear combinations of the latent effects
Fitting models with several likelihoods
Models with shared terms
Adding linear constraints to the latent effects
Implementing new latent models in R-INLA
Imputation and missing covariates in R-INLA
Friday 11th – Classes from 10:00 to 17:00
Case studies and own data.