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    • Chapter 1: Why spatio-temporal epidemiology?
    • Chapter 2: Modelling health risks
    • Chapter 3: The importance of uncertainty
    • Chapter 4: Embracing uncertainty: the Bayesian approach
    • Chapter 5: The Bayesian approach in practice
    • Chapter 6: Strategies for modelling
    • Chapter 7: Is `real' data always quite so real?
    • Chapter 8: Spatial patterns in disease
    • Chapter 9: From points to fields: Modelling environmental hazards over space
    • Chapter 10: Why time also matters
    • Chapter 11: The interplay between space and time in exposure assessment
    • Chapter 12: Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
    • Chapter 13: Better exposure measurements through better design
    • Chapter 14: New frontiers
  • Courses
    • Statistical methods in epidemiology
    • Spatio-temporal Methods in epidemiology
    • Advanced statistical modelling in space and time
    • BUC1 (CIMAT) When populations and hazards collide: modelling exposures
      and health
    • BUC2 (UNAM) Thinking Globally: The Role of Big Data
    • Detecting Pattens in Space and Time (CMM)
    • BUC4 (Bath) New Frontiers: Advanced Modelling in Space and Time
    • Big Data in Environmental Research
    • Statistics and Data Science in Research: unlocking the power of your data
    • Bayesian Hierarchical Models
    • BUCX (UNAM) Quantifying the Health Impacts of Air Pollution
    • Environmental Health Impact Assessment using R (IOM)
  • Computing resources
    • WinBUGS
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    • EnviroStat
  • Book's webpage @ CRC

Bayesian Hierarchical Models

course outline

This course provides an introduction to Bayesian Hierarchical Models, with the aim of providing an interactive experience
for students and researchers from a variety of fields and to allow them to experience state of the art statistical methodology
and its application. It will cover modelling relationships in both space and time with particular focus on fitting complex
models to big data. The course covered both theoretical and applied examples, the latter specifically through practical
‘hands-on’ computer sessions, using R and R-INLA, in which participants will be guided through the analyses of real data
with both temporal and spatial structure.

The course will be delivered at the Latin American Congress of Probability and Mathematical Statistics (CLAPEM) in
Universidad de Costa Rica between 6th-9th December 2016. The presenters are Gavin Shaddick, Millie Green and
Matthew Thomas.

The slides, practical sheets, and other relevant resources can be found below.

Slides

Day 1

Day 2

Day 3

Day 4


Practicals

An introduction to R

Day 2

Day 4


Data

Data


Required R Packages

INLA

CARBayes

spdep

shapefiles

rgdal

rgeos


Other Resources

How to install R and RStudio

  • ©Gavin Shaddick and James V. Zidek 2015