56 citations · 178 across the 22 of their papers we have counts for
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stat.CO2024
Exact discovery is polynomial for certain sparse causal Bayesian networks
Felix L. Rios, Giusi Moffa, Jack Kuipers
Causal Bayesian networks are widely used tools for summarising the dependencies between variables and elucidating their putative causal relationships. By restricting the search to…
stat.ME2024★ 1 cited
A new way to evaluate G-Wishart normalising constants via Fourier analysis
Ching Wong, Giusi Moffa, Jack Kuipers
The G-Wishart distribution is a core component for the Bayesian analysis of Gaussian graphical models as the conjugate prior for the precision matrix. Evaluating the marginal likel…
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…