Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files

Dataset

Description

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:https://github.com/darrendavidprice/science-discovery/tree/master/expressive_gaussian_mixture_models
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

Keywords

  • 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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