applied statistics

A new class of random field models for data on networks

David Bolin, Associate Professor, Statistics

Oct 3, 12:00 - 13:00

B9 L2 R2322 H1

applied statistics machine learning Gaussian processes

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.