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ONLINE COURSE – Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live

29 January 2024 - 2 February 2024

£500.00
ONLINE COURSE – Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live

Event Date

Monday, January 29th, 2024

Course Format

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.

Course Program

TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details

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-temporaldata.

By the end of the course participants should:

  1. Know the different types of spatial and spatio-temporal data available and how to work with them in R.
  2. Know the different modelling approaches for spatial and spatio-temporal data.
  3. Know how to visualize and produce maps of spatial and spatio-temporal data.
  4. Be able to fit models with the R-INLA package.
  5. Know how to interpret the output from model fitting.
  6. Be confident with the use of INLA for data analysis.
  7. Understand the different models that can be fit with INLA to spatial and spatio-temporal data.
  8. Know how to define the different parts of a model with INLA.
  9. Have the confidence to use INLA for their own projects.
Intended Audiences

Academics and post-graduate students working on projects related to spatial and spatio-temporal 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
Venue – Delivered remotely
Course Information
Time zone – Central European Standard Time (CEST)

Availability – 20 places

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 oliverhooker@prstatistics.com. 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.

Teaching Format

he 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.

https://cran.r-project.org/

Download RStudio

UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge

Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).

Assumed computer background

Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Tickets

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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 oliverhooker@prstatistics.com. 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.

If you are unsure about course suitability, please get in touch by email to find out more

oliverhooker@prstatistics.com

Course Programme

Monday 29th

Classes from 14:00 to 21:00 CET

DAY 1

LECTURE 1 – Intro to INLA

PRACTICAL 1 – Intro to INLA

LECTURE 2 – Model fitting with INLA

PRACTICAL 2 – Model fitting with INLA

LECTURE 3 – GLMM’s with INLA

PRACTICAL 3 – GLMM’s with INLA

Q and A and end of day summary

Tuesday 30th

Classes from 14:00 to 21:00 CET

DAY 2

LECTURE 4 – Spatial Data

PRACTICAL 4 – Spatial Data

LECTURE 5 – Spatio-Temporal Data

PRACTICAL 5 – Spatio-Temporal Data

LECTURE 6 – Advanced Visualisation

PRACTICAL 6 – Advanced Visualisation

Q and A and end of day summary

Wednesday 31st

Classes from 14:00 to 21:00 CET

DAY 3

LECTURE 7 – Spatial Models for Lattice Data

PRACTICAL 7 – Spatial Models for Lattice Data

LECTURE 8 – Spatial Models for Continuous Data

PRACTICAL 8 – Spatial Models for Continuous Data

LECTURE 9 – Spatial Models for Point Patterns

PRACTICAL 9 – Spatial Models for Point Patterns

Q and A and end of day summary

Thursday 1st

Classes from 14:00 to 21:00 CET

DAY 4

LECTURE 10 – Spatio-Temporal Models for Lattice Data

PRACTICAL 10 – Spatio-Temporal Models for Lattice Data

LECTURE 11 – Spatio-Temporal Models  for Continuous Data

PRACTICAL 11 – Spatio-Temporal Models  for Continuous Data

LECTURE 12 – Spatio-Temporal Models  for Point Patterns

PRACTICAL 12 – Spatio-Temporal Models  for Point Patterns

Q and A and end of day summary

Friday 2nd

Classes from 14:00 to 21:00 CET

DAY 5

Case studies, own data and problem solving.

Dr Virgillio Gomez Rubio

Dr Virgillio Gomez Rubio

Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. 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. You can find more information about him on here
 

Details

Start:
29 January 2024
End:
2 February 2024
Cost:
£500.00
Event Categories:
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Venue

Delivered remotely (United Kingdom)
Western European Time, United Kingdom + Google Map

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
Tickets are no longer available