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Bayesian dynamic modelling of network flows

Presented by: 
Mike West Duke University
Wednesday 27th July 2016 - 10:00 to 10:30
INI Seminar Room 1
I discuss Bayesian dynamic modelling for sequential analysis of network flow count data, linking two classes of models which allow fast, scalable and interpretable Bayesian inference. The first class involves sets of "decoupled" univariate state-space models for streaming count data, able to adaptively characterize and quantify network dynamics in real-time. These are then "recoupled" to define "emulation" of a second class of more structured, time-varying gravity models that allow closer and formal dissection of network dynamics and interactions among network nodes. Evolving internet flows on a defined network of web domains in e-commerce applications provide context, data and examples. Bayesian model monitoring theory defines a strategy for sequential model assessment and adaptation in cases of signaled departures of network flow data from model-based predictions.

This work builds on the more general concepts and strategy of "decouple/recouple" for Bayesian model emulation. That is, we use decoupled, parallel and scalable analyses of a set of simpler and computationally efficient univariate models, then recouple- on a sound theoretical basis- to rebuild the larger multivariate process for more formal inferences.

Co-authors: Xi Chen (Duke University), Kaoru Irie (University of Tokyo), David Banks (Duke University), Jewell Thomas (MaxPoint Interactive Inc.), and Rob Haslinger (The Sync Project).

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons