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Activity Number: 478 - Scalable Bayesian Models for Time Series and Dynamic Networks: Making an Impact in Business and Socio-Economic Applications
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #300010 Presentation
Title: Bayesian Decouple/Recouple Modeling for Large-Scale Dynamic Network Flow Studies
Author(s): Xi Chen* and David Banks and Mike West
Companies: LinkedIn Corporation and SAMSI/Duke University and Duke University
Keywords: Bayesian model emulation; Decouple/Recouple; Dynamic network flow time series; Dynamic generalized linear models; Internet traffic; Parallel computing

In the context of a motivating study of dynamic network flow data on a large-scale e-commerce website, we develop Bayesian models for sequential analysis of monitoring and adapting to changes reflected in node-node traffic. For large-scale networks, we customize core Bayesian time series analysis methods using dynamic generalized linear models (DGLMs) integrated into the multivariate network context using the concept of decouple/recouple recently introduced in multivariate time series. This enables flexible dynamic modeling of flows on large-scale networks and exploitation of partial parallelization of analysis while critically maintaining coherence with an over-arching multivariate dynamic flow model. The methodology of dynamic network DGLMs will be of interest and utility in broad ranges of dynamic network flow studies. A primary motivating context is modelling and monitoring flows of customer traffic on web sites in e-commerce applications. Examples of such application are drawn from studies of streaming node-node flows over multiple days on a network of several hundred nodes.

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

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