56 citations · 144 across the 19 of their papers we have counts for
21 papers
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…
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…
Correcting for confounding in longitudinal experiments: positioning non-linear mixed effects modeling as implementation of standardization using latent conditional exchangeability
Christian Bartels, Martina Scauda, Neva Coello +3
Non-linear mixed effects modeling and simulation (NLME M&S) is evaluated to be used for standardization with longitudinal data in presence of confounders. Standardization is a well…
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…
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…
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…
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions
Jack Kuipers, Giusi Moffa
Describing the causal relations governing a system is a fundamental task in many scientific fields, ideally addressed by experimental studies. However, obtaining data under interve…
High-Dimensional Inference in Bayesian Networks
Fritz M. Bayer, Giusi Moffa, Niko Beerenwinkel +1
Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidde…
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.…
Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms
Felix L. Rios, Giusi Moffa, Jack Kuipers
Describing the relationship between the variables in a study domain and modelling the data generating mechanism is a fundamental problem in many empirical sciences. Probabilistic g…
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
Polina Suter, Jack Kuipers, Giusi Moffa +1
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum…
Learning Bayesian Networks from Ordinal Data
Xiang Ge Luo, Giusi Moffa, Jack Kuipers
Bayesian networks are a powerful framework for studying the dependency structure of variables in a complex system. The problem of learning Bayesian networks is tightly associated w…
The Variance of Causal Effect Estimators for Binary V-structures
Jack Kuipers, Giusi Moffa
Adjusting for covariates is a well established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of th…
Efficient Sampling and Structure Learning of Bayesian Networks
Jack Kuipers, Polina Suter, Giusi Moffa
Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery. Learning the unde…
Partition MCMC for inference on acyclic digraphs
Jack Kuipers, Giusi Moffa
Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of…
Addendum on the scoring of Gaussian directed acyclic graphical models
Jack Kuipers, Giusi Moffa, David Heckerman
We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman [Ann. Statist. 30 (2002) 1414-1440] and discuss how…
Uniform random generation of large acyclic digraphs
Jack Kuipers, Giusi Moffa
Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applic…
Sequential Monte Carlo EM for multivariate probit models
Giusi Moffa, Jack Kuipers
Multivariate probit models (MPM) have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for t…