56 citations · 144 across the 19 of their papers we have counts for
4 papers · 1 filter
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…