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1 code implementation • NeurIPS 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 • 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 • 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 • 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.

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