Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sparsely sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this first workshop we will understand the idea of stationary random fields, positive definite functions, and the fundamental building blocks of Gaussian random fields. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the foundations of Gaussian Process Regression, a commonly used technique in Machine Learning and AI.