Nov 8 – 12, 2021
CERN (online only)
Europe/Zurich timezone


Machine learning likelihoods (and statistical models)

Nov 12, 2021, 3:00 PM
CERN (online only)

CERN (online only)


Machine learning likelihoods (and statistical models)

  • Andrea Coccaro (INFN Genova (IT))
  • Riccardo Torre (INFN e Universita Genova (IT))


We intend to discuss the main ideas related to interpolating likelihoods and statistical models using (Deep) Neural Networks. The main topics and open questions/issues are:
- Bayesian vs Frequentist statistical approaches and their relations to the neural network representation of the Likelihood (e.g. combination of likelihoods and double counting of constraint terms vs priors, likelihood vs statistical model)
- Interpolation of full statistical models through NN vs other established approaches
- Regression vs density estimation (supervised vs unsupervised Likelihood learning)
- Practical implementations within experiments
- Practical implementations outside experiments (fitting groups)
- Examples

Presentation materials

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