no code implementations • 2 Jun 2020 • Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
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 received, with few notable exceptions, little attention so far, despite having a natural interpretation as a hidden variable model for ordered signal data.
no code implementations • 1 Dec 2018 • Irene Córdoba, Concha Bielza, Pedro Larrañaga
In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i. e. with respect to the instances, in order to be processed.
no code implementations • 28 Jun 2018 • Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga
We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation.
no code implementations • 23 Jun 2016 • Irene Córdoba, Concha Bielza, Pedro Larrañaga
Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties.