3 citations · 6 across the 2 of their papers we have counts for
10 papers
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga
Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide…
Semiparametric Bayesian Networks
David Atienza, Concha Bielza, Pedro Larrañaga
We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both co…
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
Irene Córdoba, Concha Bielza, Pedro Larrañaga +1
The sparse Cholesky parametrization of the inverse covariance matrix can be interpreted as a Gaussian Bayesian network; however its counterpart, the covariance Cholesky factor, has…
A community-based transcriptomics classification and nomenclature of neocortical cell types
Rafael Yuste, Michael Hawrylycz, Nadia Aalling +68
To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological…
On generating random Gaussian graphical models
Irene Córdoba, Gherardo Varando, Concha Bielza +1
Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (i…
Towards Gaussian Bayesian Network Fusion
Irene Córdoba, Concha Bielza, Pedro Larrañaga
Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are le…