6 citations · 11 across the 2 of their papers we have counts for
2 papers
stat.CO2022★ 5 cited
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
Lorenzo Pacchiardi, Ritabrata Dutta
Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator models with intractable likelihood. Recently, many works trained neural networks to approxim…
stat.CO2019★ 6 cited
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Lorenzo Pacchiardi, Pierre Kunzli, Marcel Schoengens +2
Approximate Bayesian computation (ABC) provides us with a way to infer parameters of models, for which the likelihood function is not available, from an observation. Using ABC, whi…