1 code implementation • 20 Jun 2023 • 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 parents in the network.
1 code implementation • 5 May 2022 • Jack Kuipers, Giusi Moffa
To this end, we define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown.
1 code implementation • 16 Dec 2021 • 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.
1 code implementation • 16 Dec 2021 • Fritz M. Bayer, Giusi Moffa, Niko Beerenwinkel, Jack Kuipers
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 hidden variables.
1 code implementation • 8 Jul 2021 • Felix L. Rios, Giusi Moffa, Jack Kuipers
To facilitate the benchmarking of different methods, we present a novel Snakemake workflow, called Benchpress for producing scalable, reproducible, and platform-independent benchmarks of structure learning algorithms for probabilistic graphical models.
no code implementations • 2 May 2021 • Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel
The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data.
1 code implementation • 21 Mar 2018 • 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.
1 code implementation • 20 Apr 2015 • Jack Kuipers, Giusi Moffa
Finally the method can be combined with edge reversal moves to improve the sampler further.
no code implementations • 27 Feb 2014 • 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.
no code implementations • 29 Feb 2012 • Jack Kuipers, Giusi Moffa
Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions.