56 citations · 178 across the 22 of their papers we have counts for
5 papers · 1 filter
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.…
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…
Determining subtree movement distance and consensus between cell trees
Luís Cunha, Jack Kuipers, Thiago Lopes
Tumor mutational heterogeneity can be represented by trees describing the evolutionary history of a tumor. However, noisy sequencing data may create uncertainty in the inferred tre…