Giusi Moffa

CoRR, 2024

2023

The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., October, 2023

Bayesian Structure Learning and Sampling of Bayesian Networks with the <i>R</i> Package BiDAG.

[BibT_eX]

[DOI]

J. Stat. Softw., 2023

A Bayesian Take on Gaussian Process Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Efficient Sampling and Structure Learning of Bayesian Networks.

[BibT_eX]

[DOI]

Polina Suter

J. Comput. Graph. Stat., July, 2022

The Dual PC Algorithm for Structure Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

Learning Bayesian Networks from Ordinal Data.

[BibT_eX]

[DOI]

Xiang Ge Luo

J. Mach. Learn. Res., 2021

Marginalization in Bayesian Networks: Integrating Exact and Approximate Inference.

[BibT_eX]

[DOI]

CoRR, 2021

Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models.

[BibT_eX]

[DOI]

Felix L. Rios

CoRR, 2021

2018

Efficient Structure Learning and Sampling of Bayesian Networks.

[BibT_eX]

[DOI]

Polina Suter

CoRR, 2018

2015

Uniform random generation of large acyclic digraphs.

[BibT_eX]

[DOI]

Stat. Comput., 2015

2014

Sequential Monte Carlo EM for multivariate probit models.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2014

2013

Considering Unknown Unknowns: Reconstruction of Nonconfoundable Causal Relations in Biological Networks.

[BibT_eX]

[DOI]

Mohammad Javad Sadeh

Rainer Spang

J. Comput. Biol., 2013

Considering Unknown Unknowns - Reconstruction of Non-confoundable Causal Relations in Biological Networks.

[BibT_eX]

[DOI]

Mohammad Javad Sadeh