6 citations · 11 across the 4 of their papers we have counts for
8 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…
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
Sherman Lo, Peter Watson, Peter Dueben +1
The accurate prediction of precipitation is important to allow for reliable warnings of flood or drought risk in a changing climate. However, to make trust-worthy predictions of pr…
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Rilwan Adewoyin, Peter Dueben, Peter Watson +2
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and strong precipitation events. However, these numerical simulators have difficulties r…
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…
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water
Ritabrata Dutta, Zacharias Faidon Brotzakis, Antonietta Mira
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters a…
Likelihood-free parameter estimation for dynamic queueing networks: case study of passenger flow in an international airport terminal
Anthony Ebert, Ritabrata Dutta, Kerrie Mengersen +3
Dynamic queueing networks (DQN) model queueing systems where demand varies strongly with time, such as airport terminals. With rapidly rising global air passenger traffic placing i…