6 papers
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…
Scaling Up Bayesian DAG Sampling
Daniele Nikzad, Alexander Zhilkin, Juha Harviainen +3
Bayesian inference of Bayesian network structures is often performed by sampling directed acyclic graphs along an appropriately constructed Markov chain. We present two techniques…
The covariance of causal effect estimators for binary v-structures
Jack Kuipers, Giusi Moffa
Previously [Journal of Causal Inference, 10, 90-105 (2022)], we computed the variance of two estimators of causal effects for a v-structure of binary variables. Here we show that a…
On a conjecture of Roverato regarding G-Wishart normalising constants
Ching Wong, Giusi Moffa, Jack Kuipers
The evaluation of G-Wishart normalising constants is a core component for Bayesian analyses for Gaussian graphical models, but remains a computationally intensive task in general.…
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…
A Latent Causal Inference Framework for Ordinal Variables
Martina Scauda, Jack Kuipers, Giusi Moffa
Ordinal variables, such as on the Likert scale, are common in applied research. Yet, existing methods for causal inference tend to target nominal or continuous data. When applied t…