New ABM papers on data assimilation and emulation
New ABM papers on data assimilation and emulation
![Counts of pedestrians at different sensors.](/figures/paper_figures/emulators-sensors.jpeg)
We have recently published two new papers from the DUST project. The first, by Dan Tang, introduces a new method that allows ABMs to be sampled using efficient Markov chain sampling. The second, by Minh Kieu, explores the possibilities of emulating ABMs using machine learning.
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Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. Open Research Europe 2(70). DOI: 10.12688/openreseurope.14800.1 (open access)
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Kieu, M., H. Nguyen, J.A. Ward and N. Malleson (2022). Towards real-time predictions using emulators of agent-based models. Journal of Simulation 1–18. DOI: 10.1080/17477778.2022.2080008. [PDF].