Short Course on Bayesian Modeling and Inference for High-Dimensional Spatial-Temporal Data

Abhi Datta (Johns Hopkins University), Sudipto Banerjee (UCLA), Andrew O. Finley (Michigan State University)

In this course, we will present scalable Bayesian models and related Markov chain Monte Carlo based methods that can provide fast analysis of big spatial and spatio-temporal data using modest computing resources. We will begin with an introduction on point-referenced spatial data accompanied by an overview of statistical methods used to analyze them. We will briefly cover exploratory data analysis techniques like variogram fitting, basics of geo -statistical approaches like kriging and Gaussian Processes, and fundamentals of Bayesian hierarchical geospatial models. We will then highlight some of the computational issues experienced by Gaussian Process models when used to model large spatial data. In this context, we will present new scalable Bayesian models that can deliver fully model based inference for massive spatial data and demostrate how to analyze large datasets using these models in R.

  • Software: R. The packages that will be used for the course can be installed by running the command given in the Install_Packages.R file.
  • Main reference text: Hierarchical Modeling and Analysis for Spatial Data, Second Edition
  • Course Schedule