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:2108.11481Code and model files can be found at:
Date made available10 Dec 2021
PublisherUniversity of Manchester Figshare
Date of data production2019 -

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI
  • Digital Futures


  • Gaussian Mixture Models
  • machine learning methods
  • Particle physics, standard model and beyond, quark flavor physics
  • statistical inference
  • scientific discovery, research, computational knowledge
  • Simulated datasets
  • Statistical Modelling
  • Particle Physics
  • Knowledge Representation and Machine Learning

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