Search Results for author: Giusi Moffa

Found 10 papers, 7 papers with code

A Bayesian Take on Gaussian Process Networks

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.

Gaussian Processes

The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions

1 code implementation5 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.

Causal Inference

The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks

1 code implementation16 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.

High-Dimensional Inference in Bayesian Networks

1 code implementation16 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.

Benchmarking Vocal Bursts Intensity Prediction

Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms

1 code implementation8 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.


Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG

no code implementations2 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.

Graph Sampling

Efficient Sampling and Structure Learning of Bayesian Networks

1 code implementation21 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.

Causal Discovery

Partition MCMC for inference on acyclic digraphs

1 code implementation20 Apr 2015 Jack Kuipers, Giusi Moffa

Finally the method can be combined with edge reversal moves to improve the sampler further.

Addendum on the scoring of Gaussian directed acyclic graphical models

no code implementations27 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.

Uniform random generation of large acyclic digraphs

no code implementations29 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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