Peter Orbanz
Associate Professor, Columbia University
Wednesday
16
January
2019
Download TALK slides
10:30 am
177 Huntington Ave
11th floor
ADD to calendar
Network models, sampling, and symmetry properties

A recent body of work, by myself and many others, aims to develop a statistical theory of network data for problems a single network is observed. Of the models studied in this area, graphon models are probably most widely known in statistics. I will explain the relationship between three aspects of this work: (1) Specific models, such as graphon models, graphex models, and edge-exchangeable graphs. (2) Sampling theory for networks, specifically in the case statisticians might refer to as an infinite-population limit. (3) Invariance properties, especially various forms of exchangeability. I will also present recent results that show how statistically relevant results (such as central limit theorems) can be derived from such invariance properties.

About the speaker
Peter Orbanz is associate professor of statistics at Columbia University. His research interests include network and relational data, Bayesian nonparametrics, symmetry principles in machine learning and statistics, and hierarchies of latent variables. He was an undergraduate student at the University of Bonn, a PhD student at ETH Zurich, and a postdoctoral fellow at the University of Cambridge.
Visiting Speaker
Peter Orbanz
Associate Professor, Columbia University
Network models, sampling, and symmetry properties
Wed
Jan 16, 2019
Download TALK slides
10:30 am
177 Huntington Ave
11th floor
ADD to calendar

A recent body of work, by myself and many others, aims to develop a statistical theory of network data for problems a single network is observed. Of the models studied in this area, graphon models are probably most widely known in statistics. I will explain the relationship between three aspects of this work: (1) Specific models, such as graphon models, graphex models, and edge-exchangeable graphs. (2) Sampling theory for networks, specifically in the case statisticians might refer to as an infinite-population limit. (3) Invariance properties, especially various forms of exchangeability. I will also present recent results that show how statistically relevant results (such as central limit theorems) can be derived from such invariance properties.

about the speaker
Peter Orbanz is associate professor of statistics at Columbia University. His research interests include network and relational data, Bayesian nonparametrics, symmetry principles in machine learning and statistics, and hierarchies of latent variables. He was an undergraduate student at the University of Bonn, a PhD student at ETH Zurich, and a postdoctoral fellow at the University of Cambridge.

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