1.4 Outline

Let’s go on a journey together, learning the foundational approaches to dealing with correlated (temporal or spatial) data!

We’ll start with a review of important probability concepts and review/learn some basic matrix notation that simplifies our probability model notation by neatly organizing our model information. Then we’ll spend some time thinking about how we define or encode dependence between observations in models.

The course will be structured so that we spend a few weeks with each type of data structure. We’ll learn the characteristics and structure that define that type of correlated data and the standard models and approaches used to deal with the dependence over time or space. By the end of this course, you should have a foundational understanding of how to analyze correlated data and be able to learn more advanced methodologies within each of these data types.