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Topological mixture estimation
Steve Huntsman

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #36

We introduce topological mixture estimation, a completely nonparametric and computationally efficient solution to the problem of estimating a one-dimensional mixture with generic unimodal components. We repeatedly perturb the unimodal decomposition of Baryshnikov and Ghrist to produce a topologically and information-theoretically optimal unimodal mixture. We also detail a smoothing process that optimally exploits topological persistence of the unimodal category in a natural way when working directly with sample data. Finally, we illustrate these techniques through examples.

Author Information

Steve Huntsman (BAE Systems FAST Labs)

Steve Huntsman is a mathematician whose work focuses on discrete geometric and probabilistic themes in physics, computation, and communication. Before joining BAE Systems, he initiated a data science program for a government organization. Previously he founded a network security startup applying methods from nonequilibrium statistical physics and was co-PI on the DARPA Scalable Network Monitoring program. He began his career as a researcher at the Institute for Defense Analyses and the Naval Postgraduate School.

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