Short Description

Model fitting and prediction in the presence of correlation due to temporal and/or spatial association. ARIMA models and Gaussian processes.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Explain, with examples, the main inferential tasks related to spatial or temporal data, and how the spatial or temporal associations make it possible to borrow statistical tools.
  2. Understand the ideas of autocorrelation and correlated errors, and be able to explain the importance of these ideas for temporal and spatial modelling.
  3. Fit temporal, spatial, and spatio-temporal models by implementing them in a probabilistic programming language, and interpret the results.
  4. Apply relevant visualization tools and draw correct conclusions from the analysis.

Reference Material

  • Shaddick, Gavin and Zidek, James V. Spatio-Temporal Methods in Environmental Epidemiology. CRC Press, 2016.
  • Chatfield, Chris. The Analysis of Time Series: An Introduction. CRC Press, 2003.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.