The Case for Continuous Time

  • Christian Shelton | University of California

Time is a continuous quantity. This talk begins with theoretical and experimental problems that arise when time is treated as a discrete quantity in stochastic systems. I will then discuss continuous time Bayesian networks (CTBNs), a variable-based representation of continuous-time Markov processes. I will cover their representation and semantics and a bit about inference and learning in the models. Finally, I will present my group’s recent work in employing CTBNs on real-world applications.

Speaker Details

Christian Shelton is an Associate Professor of Computer Science at the University of California at Riverside. He has been at UC Riverside since 2003. His research interests are in machine learning and dynamic systems. He has worked at the intersection of learning and applications as varied as computer vision, sociology, game theory, decision theory, and computational biology.

Dr. Shelton received his PhD from MIT and was a post-doctoral scholar at Stanford. He was the managing editor of the Journal of Machine Learning Research (JMLR) from 2003 through 2008, and currently serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR).

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      Jeff Running

Series: Microsoft Research Talks