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cWtil_0p1_full_400k_rivet_output.pickle (45.78 MB)
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cHWB_4p0_cWtil_m0p4_full_200k_rivet_output.dat (24.93 MB)
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cHWB_m2p0_cWtil_0p0_full_200k_rivet_output.dat (24.95 MB)
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cHWB_m4p0_cWtil_0p4_full_200k_rivet_output.dat (24.95 MB)
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cHWB_m4p0_cWtil_m0p2_full_200k_rivet_output.dat (24.92 MB)
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Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files
Neural network model files and Madgraph event generator outputs used as inputs to the results presented in the paper "Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences" arXiv:2108.11481
Code and model files can be found at:
https://github.com/darrendavidprice/science-discovery/tree/master/expressive_gaussian_mixture_models
Funding
Towards machine learning-enabled scientific discovery, Turing Project Award, Alan Turing Institute
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