Statistics 697S - ST-Statistcl Network Inference

Network data is increasingly prevalent in scientific research and statistical inference, figuring prominently in such diverse fields as neuroscience, sociology, and public health, to name a few. In this course, we will investigate recent advances in statistical inference on network data. We will discuss the development of progressively more nuanced statistical models for networks, focusing on the recent advances in developing classical inference tasks within a network framework (e.g., hypothesis testing, clustering, classification, etc.). The class will be partially driven by the interests of the students, with students leading regular discussions of relevant papers from the literature.

Open to Graduate students only. Prerequisites: Math 545 or equivalent, a working knowledge of probability such as STAT 605 or STAT 607, and a working knowledge of statistics such as STAT 608 or equivalent.