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Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems
The use of local messages in a graphical model structure ensures that the approach is suited to distributed computing and can efficiently handle complex dependence structures.
Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning
While the opportunistic data available from past modelling activities provides a large and useful dataset for exploring APSIM emulation, it may not be sufficiently rich enough for successful deep learning of more complex model dynamics.
Modern strategies for time series regression
This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time.
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