Technological breakthroughs allow us to collect data with increasing spatio-temporal resolution from complex interaction systems. The combination of high-resolution observations, expressive dynamic models, and efficient machine learning algorithms can lead to crucial insights into complex interaction dynamics and the functions of these systems. In this paper, we formulate the dynamics of a complex interacting network as a stochastic process driven by a sequence of events, and develop expectation propagation algorithms to make inferences from noisy observations. To avoid getting stuck at a local optimum, we formulate the problem of minimizing Bethe free energy as a constrained primal problem and take advantage of the concavity of dual problem in the feasible domain of dual variables guaranteed by duality theorem. Our expectation propagation algorithms demonstrate better performance in inferring the interaction dynamics in complex transportation networks than competing models such as particle filter, extended Kalman filter, and deep neural networks.
Le Fang (University at Buffalo-SUNY)
Fan Yang (University at Buffalo)
Wen Dong (University at Buffalo)
Wen Dong is an Assistant Professor of Computer Science and Engineering at the State University of New York at Buffalo with a joint appointment in the Institute of Sustainable Transportation and Logistics. He focuses on modeling human interaction dynamics with stochastic process theory through combining the power of “big data” and the logic/reasoning power of agent-based models, to solve our societies most challenging problems such as transportation sustainability and efficiency. Wen Dong holds a Ph.D. in Media Arts and Sciences from Massachusetts Institute of Technology. His email address is email@example.com.
More from the Same Authors
2016 Poster: Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model »
Zhen Xu · Wen Dong · Sargur N Srihari