Parallel Importance Sampling in Conditional Linear Gaussian Networks. - Antonio Salmerón; Darío Ramos-López; Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (CAEPIA, 2015)

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. - Antonio Salmerón; Anders L. Madsen; Frank Jensen; Helge Langseth; Thomas D. Nielsen; Darío Ramos-López; Ana M. Martínez; Andrés R. Masegosa (ECAI, 2016)

Financial Data Analysis with PGMs Using AMIDST. - Rafael Cabañas 001; Ana M. Martínez; Andrés R. Masegosa; Darío Ramos-López; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (ICDM Workshops, 2016)

d-VMP: Distributed Variational Message Passing. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Probabilistic Graphical Models, 2016)

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. - Darío Ramos-López; Antonio Salmerón; Rafael Rumí; Ana M. Martínez; Thomas D. Nielsen; Andrés R. Masegosa; Helge Langseth; Anders L. Madsen (Probabilistic Graphical Models, 2016)

Optimal sampling patterns for Zernike polynomials. - Darío Ramos-López; Miguel Ángel Sánchez Granero; Manuel Fernández-Martínez; Andrei Martínez-Finkelshtein (Appl. Math. Comput., 2016)

Bayesian Models of Data Streams with Hierarchical Power Priors. - Andrés R. Masegosa; Thomas D. Nielsen; Helge Langseth; Darío Ramos-López; Antonio Salmerón; Anders L. Madsen (ICML, 2017)

Scaling up Bayesian variational inference using distributed computing clusters. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Int. J. Approx. Reason., 2017)

MAP inference in dynamic hybrid Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Ana M. Martínez; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (Prog. Artif. Intell., 2017)

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Antonio Salmerón; Rafael Rumí; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Int. J. Approx. Reason., 2018)

AMIDST: A Java toolbox for scalable probabilistic machine learning. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Rafael Cabañas 001; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Knowl. Based Syst., 2019)

Analyzing concept drift: A case study in the financial sector. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Intell. Data Anal., 2020)

Analysis of Corneal Data in R with the rPACI Package. - Darío Ramos-López; Ana D. Maldonado (R J., 2021)

Contributions on Mixtures of Polynomials for Hybrid Bayesian Networks. - Juan Carlos Luengo; Darío Ramos-López; Rafael Rumí; Ana D. Maldonado (IDEAL (1), 2024)

Modeling continuous distributions in hybrid Bayesian networks using mixtures of polynomials with tails. - Juan Carlos Luengo; Darío Ramos-López; Rafael Rumí (Comput. Stat. Data Anal., 2025)

Sampling patterns for Zernike-like bases in non-standard geometries. - Sergio Díaz-Elbal; Andrei Martínez-Finkelshtein; Darío Ramos-López (CoRR, 2025)