Conveners
Machine learning likelihoods (and statistical models)
- Andrea Coccaro (INFN Genova (IT))
- Riccardo Torre (INFN e Universita Genova (IT))
Description
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
