1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2024★ 1 cited
Bayesian Causal Inference with Gaussian Process Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Causal discovery and inference from observational data is an essential problem in statistics posing both modeling and computational challenges. These are typically addressed by imp…
stat.ML2023
A Bayesian Take on Gaussian Process Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its paren…
stat.ML2021
The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges.…