no code implementations • 10 Mar 2023 • David Quesada, Pedro Larrañaga, Concha Bielza
When we face patients arriving to a hospital suffering from the effects of some illness, one of the main problems we can encounter is evaluating whether or not said patients are going to require intensive care in the near future.
1 code implementation • 24 Jan 2023 • Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga, Gustav Eje Henter
Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidden Markov models can be used.
1 code implementation • 4 Mar 2022 • Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga
The approach was applied to a cancer benchmark problem, and the results justified the use of variational quantum algorithms for solving the Bayesian network structure learning problem.
2 code implementations • 7 Sep 2021 • David Atienza, Concha Bielza, Pedro Larrañaga
In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data.
no code implementations • 27 Oct 2020 • Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza
In a real life process evolving over time, the relationship between its relevant variables may change.
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 • 12 Nov 2018 • Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions.
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.