Clustering large networks of parametric dynamic generative models

Yunwen Xu, Sanggyun Kim, Srinivasa M. Salapaka, Carolyn L. Beck, Todd P. Coleman

Research output: Contribution to journalConference articlepeer-review


Analysis, prediction and control of parametric generative models for stochastic processes arise in numerous applications, such as in biology, telecommunications, geography, seismology and finance. In many of these applications, it is desirable to obtain an aggregated behavior from an underlying network of stochastic interactions. This paper focuses on the simplification of parametric models describing multiple stochastic processes, by aggregating the processes that have similar input-output behaviors in an ensemble. We propose a clustering-based method, which is general in the sense that the similarity metric upon which the aggregation relies can accommodate processes characterized by a variety of generative models. To illustrate our aggregation framework, we investigate an example system comprised of a set of point process models for earthquakes. Simulations are presented.

Original languageEnglish (US)
Article number6425894
Pages (from-to)5248-5253
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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