35 citations · 76 across the 6 of their papers we have counts for
7 papers
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…
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben +1
Probabilistic forecasting relies on past observations to provide a probability distribution for a future outcome, which is often evaluated against the realization using a scoring r…
Generalized Bayesian Likelihood-Free Inference
Lorenzo Pacchiardi, Sherman Khoo, Ritabrata Dutta
We propose a posterior for Bayesian Likelihood-Free Inference (LFI) based on generalized Bayesian inference. To define the posterior, we use Scoring Rules (SRs), which evaluate pro…
Score Matched Neural Exponential Families for Likelihood-Free Inference
Lorenzo Pacchiardi, Ritabrata Dutta
Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations. In A…
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic
Ritabrata Dutta, Susana Gomes, Dante Kalise +1
A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounti…
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…
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi +5
ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering…