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Activity Number: 246 - Bayesian Nonparametrics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #302981
Title: Gaussian Process Classification with Network Inputs
Author(s): Nathan Josephs* and Eric Kolaczyk and Lizhen Lin and Steve Rosenberg
Companies: Boston University and Boston University and University of Notre Dame and Boston University
Keywords: Gaussian Process; Network Analysis; Binary Classification; Survival Analysis; Bayesian Nonparametrics; MCMC

Network analysis is a popular subfield of statistics. The study of a single network is fairly established, but technological advances are making the study of multiple networks increasingly possible for exploring such topics as brain connectomics, molecular representations, and gene co-expression networks. In this presentation, we formulate a Gaussian Process classifier with network inputs. In doing so, we address the challenge of obtaining a provably positive definite kernel by finding a class of distances that yields a PD squared exponential kernel, which also gives rise to a posterior consistency result. Moreover, we outline three computational approaches to our classifier. We present results on both a simulation study and a real dataset. Finally, we discuss how our work applies to the solutions to other statistical problems of interest, including survival analysis, anomaly detection, and hypothesis testing.

Authors who are presenting talks have a * after their name.

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