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• W2396792969 abstract "In this paper, we develop a variational method to track and make predictions about a real-world system from continuous imperfect observations about this system, using an agent-based model that describes the system dynamics. By combining the power of big data with the power of model-thinking in the stochastic process framework, we can make many valuable predictions. We show how to track the spread of an epidemic at the individual level and how to make short-term predictions about traffic congestion. This method points to a new way to bring together modelers and data miners by turning the real world into a living lab." @default.
• W2396792969 created "2016-06-24" @default.
• W2396792969 creator A5047724274 @default.
• W2396792969 date "2016-05-09" @default.
• W2396792969 modified "2023-09-28" @default.
• W2396792969 title "Variational Inference with Agent-Based Models" @default.
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• W2396792969 doi "https://doi.org/10.5555/2936924.2937049" @default.
• W2396792969 hasPublicationYear "2016" @default.
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