Lecture

Network Averaging: Theory and Applications

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Description

This lecture by the instructor on May 22, 2020, delves into the concept of averaging networks, exploring both labeled and unlabeled networks. The presentation covers the behavior of averages, statistical methods, and the challenges of network analysis. It discusses the development of 'Statistics 101' for network data objects, the geometry of network spaces, and the computation of Fréchet means. The lecture also touches on practical considerations, such as covariance estimation and large-sample testing theory. Through examples from the 1000 Functional Connectomes Project, the instructor illustrates the application of network averaging in MRI data analysis. The talk concludes by emphasizing the importance of expanding the statistical toolbox to effectively analyze networks.

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Statistics (from German: Statistik, () "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal".

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