Contribuciones al análisis de problemas supercomplejos de toma de decisiones. - Concepcion Bielza (1996)

Markov chain Monte Carlo methods for decision analysis. - Concha Bielza; Peter Müller 003; David Ríos Insua (AISTATS, 1997)

A Comparison of Decision Trees, Influence Diagrams and Valuation Networks for Asymmetric Decision Problems. - Concha Bielza; Prakash P. Shenoy (AISTATS, 1997)

Influence Diagrams for Neonatal Jaundice Management. - Concha Bielza; Sixto Ríos-Insua; Manuel Gómez (AIMDM, 1999)

Structural, elicitation and computational issues faced when solving complex decision making problems with influence diagrams. - Concha Bielza; Manuel Gómez; Sixto Ríos-Insua; Juan A. Fernández del Pozo (Comput. Oper. Res., 2000)

Knowledge Organisation in a Neonatal Jaundice Decision Support System. - Juan A. Fernández del Pozo; Concha Bielza; Manuel Gómez (ISMDA, 2001)

An Interactive Framework for Open Queries in Decision Support Systems. - Juan A. Fernández del Pozo; Concha Bielza (IBERAMIA, 2002)

New Structures for Conditional Probability Tables. - Juan A. Fernández del Pozo; Concha Bielza (Probabilistic Graphical Models, 2002)

Inferentially Efficient Propagation in Non-Decomposable Bayesian Network with Hierarchical Junction Trees. - Roberto O. Puch; Jim Q. Smith; Concha Bielza (Probabilistic Graphical Models, 2002)

Finding and Explaining Optimal Treatments. - Concha Bielza; Juan A. Fernández del Pozo; Peter J. F. Lucas (AIME, 2003)

Optimal Decision Explanation by Extracting Regularity Patterns. - Concha Bielza; Juan A. Fernández del Pozo; Peter J. F. Lucas (SGAI Conf., 2003)

Node deletion sequences in influence diagrams using genetic algorithms. - Manuel Gómez; Concha Bielza (Stat. Comput., 2004)

A list-based compact representation for large decision tables management. - Juan A. Fernández del Pozo; Concha Bielza; Manuel Gómez (Eur. J. Oper. Res., 2005)

Machine learning in bioinformatics. - Pedro Larrañaga; Borja Calvo; Roberto Santana 001; Concha Bielza; Josu Galdiano; Iñaki Inza; José Antonio Lozano 001; Rubén Armañanzas; Guzmán Santafé; Aritz Pérez Martínez; Victor Robles (Briefings Bioinform., 2006)

A decision approach to competitive electronic sealed-bid auctions for land. - Enrique Ballestero; Concha Bielza; David Pla-Santamaria (J. Oper. Res. Soc., 2006)

A review of estimation of distribution algorithms in bioinformatics. - Rubén Armañanzas; Iñaki Inza; Roberto Santana 001; Yvan Saeys; Jose Luis Flores 001; José Antonio Lozano 001; Yves Van de Peer; Rosa Blanco; Víctor Robles; Concha Bielza; Pedro Larrañaga (BioData Min., 2008)

Explaining clinical decisions by extracting regularity patterns. - Concha Bielza; Juan A. Fernández del Pozo; Peter J. F. Lucas (Decis. Support Syst., 2008)

A Bayesian network model for surface roughness prediction in the machining process. - Maritza Correa; Concha Bielza; M. de J. Ramirez; José R. Alique (Int. J. Syst. Sci., 2008)

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems. - Roberto Santana 001; Concha Bielza; José Antonio Lozano 001; Pedro Larrañaga (GECCO, 2009)

Probabilistic Graphical Markov Model Learning: An Adaptive Strategy. - Elva Díaz; Eunice Ponce de León; Pedro Larrañaga; Concha Bielza (MICAI, 2009)

Predicting citation count of Bioinformatics papers within four years of publication. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Bioinform., 2009)

Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. - Maritza Correa; Concha Bielza; J. Pamies-Teixeira (Expert Syst. Appl., 2009)

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. - Alfredo Cuesta-Infante; Roberto Santana 001; José Ignacio Hidalgo; Concha Bielza; Pedro Larrañaga (IEEE Congress on Evolutionary Computation, 2010)

Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (EvoBIO, 2010)

Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances. - Hanen Borchani; Pedro Larrañaga; Concha Bielza (IEA/AIE (1), 2010)

Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (IEA/AIE (3), 2010)

Modeling challenges with influence diagrams: Constructing probability and utility models. - Concha Bielza; Manuel Gómez; Prakash P. Shenoy (Decis. Support Syst., 2010)

Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. - Concha Bielza; Juan A. Fernández del Pozo; Pedro Larrañaga; Endika Bengoetxea (Expert Syst. Appl., 2010)

Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (IEEE Trans. Syst. Man Cybern. Part B, 2010)

The von Mises Naive Bayes Classifier for Angular Data. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (CAEPIA, 2011)

Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (EMO, 2011)

Affinity propagation enhanced by estimation of distribution algorithms. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (GECCO, 2011)

Regularized k-order markov models in EDAs. - Roberto Santana 001; Hossein Karshenas; Concha Bielza; Pedro Larrañaga (GECCO, 2011)

Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods. - Roberto Santana 001; Hossein Karshenas; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2011)

Bayesian Chain Classifiers for Multidimensional Classification. - Julio H. Zaragoza; Luis Enrique Sucar; Eduardo F. Morales 001; Concha Bielza; Pedro Larrañaga (IJCAI, 2011)

Predicting the h-index with cost-sensitive naive Bayes. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (ISDA, 2011)

Dealing with complex queries in decision-support systems. - Juan A. Fernández del Pozo; Concha Bielza (Data Knowl. Eng., 2011)

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data. - Concha Bielza; Víctor Robles; Pedro Larrañaga (Expert Syst. Appl., 2011)

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. - Endika Bengoetxea; Pedro Larrañaga; Concha Bielza; Juan A. Fernández del Pozo (J. Heuristics, 2011)

Classifying evolving data streams with partially labeled data. - Hanen Borchani; Pedro Larrañaga; Concha Bielza (Intell. Data Anal., 2011)

Multi-dimensional classification with Bayesian networks. - Concha Bielza; Guangdi Li; Pedro Larrañaga (Int. J. Approx. Reason., 2011)

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga; Ruth Benavides-Piccione; Javier DeFelipe (Neuroinformatics, 2011)

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Neuroinformatics, 2011)

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Scientometrics, 2011)

Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. - Rubén Armañanzas; Yvan Saeys; Iñaki Inza; Miguel García-Torres; Concha Bielza; Yves Van de Peer; Pedro Larrañaga (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

Maximizing the number of polychronous groups in spiking networks. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2012)

Regularized logistic regression and multiobjective variable selection for classifying MEG data. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Biol. Cybern., 2012)

Ensemble transcript interaction networks: A case study on Alzheimer's disease. - Rubén Armañanzas; Pedro Larrañaga; Concha Bielza (Comput. Methods Programs Biomed., 2012)

Lazy lasso for local regression. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Comput. Stat., 2012)

A review on probabilistic graphical models in evolutionary computation. - Pedro Larrañaga; Hossein Karshenas; Concha Bielza; Roberto Santana 001 (J. Heuristics, 2012)

A comparison of clustering quality indices using outliers and noise. - Luis Guerra; Víctor Robles; Concha Bielza; Pedro Larrañaga (Intell. Data Anal., 2012)

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). - Hanen Borchani; Concha Bielza; Pablo Martínez-Martín; Pedro Larrañaga (J. Biomed. Informatics, 2012)

Forward stagewise naïve Bayes. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Prog. Artif. Intell., 2012)

Semi-supervised Projected Clustering for Classifying GABAergic Interneurons. - Luis Guerra; Ruth Benavides-Piccione; Concha Bielza; Víctor Robles; Javier DeFelipe; Pedro Larrañaga (AIME, 2013)

Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (CAEPIA, 2013)

Learning Mixtures of Polynomials of Conditional Densities from Data. - Pedro L. López-Cruz; Thomas D. Nielsen; Concha Bielza; Pedro Larrañaga (CAEPIA, 2013)

Augmented Semi-naive Bayes Classifier. - Bojan Mihaljevic; Pedro Larrañaga; Concha Bielza (CAEPIA, 2013)

Bayesian networks to answer challenging neuroscience questions. - Pedro Larrañaga; Concha Bielza (CBMS, 2013)

Towards optimal neuronal wiring through estimation of distribution algorithms. - Laura Anton-Sanchez; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2013)

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. - Rubén Armañanzas; Concha Bielza; Kallol Ray Chaudhuri; Pablo Martínez-Martín; Pedro Larrañaga (Artif. Intell. Medicine, 2013)

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. - Hanen Borchani; Concha Bielza; Carlos Toro 003; Pedro Larrañaga (Artif. Intell. Medicine, 2013)

Regularized continuous estimation of distribution algorithms. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Appl. Soft Comput., 2013)

Sparse regularized local regression. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Comput. Stat. Data Anal., 2013)

An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Int. J. Artif. Intell. Tools, 2013)

Network measures for information extraction in evolutionary algorithms. - Roberto Santana 001; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (Int. J. Comput. Intell. Syst., 2013)

Classification of neural signals from sparse autoregressive features. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2013)

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. - Miguel García-Torres; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (Inf. Sci., 2013)

A review on evolutionary algorithms in Bayesian network learning and inference tasks. - Pedro Larrañaga; Hossein Karshenas; Concha Bielza; Roberto Santana 001 (Inf. Sci., 2013)

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks - A Case Study for the Optimal Ordering of Tables. - Concha Bielza; Juan A. Fernández del Pozo; Pedro Larrañaga (J. Comput. Sci. Technol., 2013)

Bayesian Sparse Partial Least Squares. - Diego Vidaurre; Marcel A. J. van Gerven; Concha Bielza; Pedro Larrañaga; Tom Heskes (Neural Comput., 2013)

Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000-2009. - Alfonso Ibáñez; Concha Bielza; Pedro Larrañaga (Scientometrics, 2013)

Cluster methods for assessing research performance: exploring Spanish computer science. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Scientometrics, 2013)

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Probabilistic Graphical Models, 2014)

Discrete Bayesian Network Classifiers: A Survey. - Concha Bielza; Pedro Larrañaga (ACM Comput. Surv., 2014)

Semi-supervised projected model-based clustering. - Luis Guerra; Concha Bielza; Víctor Robles; Pedro Larrañaga (Data Min. Knowl. Discov., 2014)

Bayesian networks in neuroscience: a survey. - Concha Bielza; Pedro Larrañaga (Frontiers Comput. Neurosci., 2014)

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. - Bojan Mihaljevic; Concha Bielza; Ruth Benavides-Piccione; Javier DeFelipe; Pedro Larrañaga (Frontiers Comput. Neurosci., 2014)

Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2014)

Bayesian network modeling of the consensus between experts: An application to neuron classification. - Pedro L. López-Cruz; Pedro Larrañaga; Javier DeFelipe; Concha Bielza (Int. J. Approx. Reason., 2014)

Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. - Alfonso Ibáñez; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2014)

Multi-label classification with Bayesian network-based chain classifiers. - Luis Enrique Sucar; Concha Bielza; Eduardo F. Morales 001; Pablo Hernandez-Leal; Julio H. Zaragoza; Pedro Larrañaga (Pattern Recognit. Lett., 2014)

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (IEEE Trans. Evol. Comput., 2014)

Multi-Dimensional Classification with Super-Classes. - Jesse Read; Concha Bielza; Pedro Larrañaga (IEEE Trans. Knowl. Data Eng., 2014)

Regularized Multivariate von Mises Distribution. - Luis Rodriguez-Lujan; Concha Bielza; Pedro Larrañaga (CAEPIA, 2015)

Towards Gaussian Bayesian Network Fusion. - Irene Córdoba-Sánchez; Concha Bielza; Pedro Larrañaga (ECSQARU, 2015)

Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control. - Javier Díaz; Concha Bielza; Jose L. Ocaña; Pedro Larrañaga (ML4CPS, 2015)

Classifying GABAergic interneurons with semi-supervised projected model-based clustering. - Bojan Mihaljevic; Ruth Benavides-Piccione; Luis Guerra; Javier DeFelipe; Pedro Larrañaga; Concha Bielza (Artif. Intell. Medicine, 2015)

Interval-based ranking in noisy evolutionary multi-objective optimization. - Hossein Karshenas; Concha Bielza; Pedro Larrañaga (Comput. Optim. Appl., 2015)

Recent Advances in Probabilistic Graphical Models. - Concha Bielza; Serafín Moral; Antonio Salmerón (Int. J. Intell. Syst., 2015)

Conditional Density Approximations with Mixtures of Polynomials. - Gherardo Varando; Pedro L. López-Cruz; Thomas D. Nielsen; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2015)

Decision boundary for discrete Bayesian network classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (J. Mach. Learn. Res., 2015)

Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. - Concha Bielza; João Gama 001; Alípio Jorge; Indre Zliobaite (Mach. Learn., 2015)

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons. - Bojan Mihaljevic; Ruth Benavides-Piccione; Concha Bielza; Javier DeFelipe; Pedro Larrañaga (Neuroinformatics, 2015)

Directional naive Bayes classifiers. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (Pattern Anal. Appl., 2015)

A survey on multi-output regression. - Hanen Borchani; Gherardo Varando; Concha Bielza; Pedro Larrañaga (WIREs Data Mining Knowl. Discov., 2015)

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. - Ignacio Leguey; Concha Bielza; Pedro Larrañaga (CAEPIA, 2016)

Hybrid Gaussian and von Mises Model-Based Clustering. - Sergio Luengo-Sanchez; Concha Bielza; Pedro Larrañaga (ECAI, 2016)

Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment. - Alberto Ogbechie; Javier Diaz-Rozo; Pedro Larrañaga; Concha Bielza (ML4CPS, 2016)

Learning Tractable Multidimensional Bayesian Network Classifiers. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Probabilistic Graphical Models, 2016)

Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot. - David Atienza; Concha Bielza; Javier Díaz; Pedro Larrañaga (STAIRS, 2016)

A review of undirected and acyclic directed Gaussian Markov model selection and estimation. - Irene Córdoba-Sánchez; Concha Bielza; Pedro Larrañaga (CoRR, 2016)

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. - Hanen Borchani; Pedro Larrañaga; João Gama 001; Concha Bielza (Intell. Data Anal., 2016)

Decision functions for chain classifiers based on Bayesian networks for multi-label classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2016)

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. - Alfonso Ibáñez; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (J. Assoc. Inf. Sci. Technol., 2016)

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons. - Laura Anton-Sanchez; Concha Bielza; Ruth Benavides-Piccione; Javier DeFelipe; Pedro Larrañaga (Neuroinformatics, 2016)

Learning Bayesian networks with low inference complexity. - Marco Benjumeda; Pedro Larrañaga; Concha Bielza (Prog. Artif. Intell., 2016)

Architecture for anomaly detection in a laser heating surface process. - Javier Mesonero; Concha Bielza; Pedro Larrañaga (ETFA, 2017)

Network design through forests with degree- and role-constrained minimum spanning trees. - Laura Anton-Sanchez; Concha Bielza; Pedro Larrañaga (J. Heuristics, 2017)

Frobenius Norm Regularization for the Multivariate Von Mises Distribution. - Luis Rodriguez-Lujan; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2017)

Univariate and bivariate truncated von Mises distributions. - Pablo Fernandez-Gonzalez; Concha Bielza; Pedro Larrañaga (Prog. Artif. Intell., 2017)

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. - Irene Córdoba; Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato; Concha Bielza; Pedro Larrañaga (CAEPIA, 2018)

Asymmetric Hidden Markov Models with Continuous Variables. - Carlos Puerto-Santana; Concha Bielza; Pedro Larrañaga (CAEPIA, 2018)

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (IDEAL (1), 2018)

Multi-dimensional Bayesian Network Classifier Trees. - Santiago Gil-Begue; Pedro Larrañaga; Concha Bielza (IDEAL (1), 2018)

A partial orthogonalization method for simulating covariance and concentration graph matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (PGM, 2018)

Learning Bayesian network classifiers with completed partially directed acyclic graphs. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (PGM, 2018)

Discrete model-based clustering with overlapping subsets of attributes. - Fernando Rodriguez-Sanchez; Pedro Larrañaga; Concha Bielza (PGM, 2018)

Towards a supervised classification of neocortical interneuron morphologies. - Bojan Mihaljevic; Pedro Larrañaga; Ruth Benavides-Piccione; Sean L. Hill; Javier DeFelipe; Concha Bielza (BMC Bioinform., 2018)

Markov Property in Generative Classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga; Eva Riccomagno (CoRR, 2018)

Tractability of most probable explanations in multidimensional Bayesian network classifiers. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2018)

Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes. - Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (IEEE Internet Things J., 2018)

A regularity index for dendrites - local statistics of a neuron's input space. - Laura Anton-Sanchez; Felix Effenberger; Concha Bielza; Pedro Larrañaga; Hermann Cuntz (PLoS Comput. Biol., 2018)

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. - Sergio Luengo-Sanchez; Isabel Fernaud; Concha Bielza; Ruth Benavides-Piccione; Pedro Larrañaga; Javier DeFelipe (PLoS Comput. Biol., 2018)

bnclassify: Learning Bayesian Network Classifiers. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (R J., 2018)

Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors. - Pablo Fernandez-Gonzalez; Concepcion Bielza; Pedro Larrañaga (IEEE Access, 2019)

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas. - Sergio Luengo-Sanchez; Pedro Larrañaga; Concha Bielza (IEEE Access, 2019)

Learning tractable Bayesian networks in the space of elimination orders. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Artif. Intell., 2019)

Circular Bayesian classifiers using wrapped Cauchy distributions. - Ignacio Leguey; Concha Bielza; Pedro Larrañaga (Data Knowl. Eng., 2019)

A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. - Ignacio Leguey; Pedro Larrañaga; Concha Bielza; Shogo Kato (Inf. Sci., 2019)

Tractable learning of Bayesian networks from partially observed data. - Marco Benjumeda; Sergio Luengo-Sanchez; Pedro Larrañaga; Concha Bielza (Pattern Recognit., 2019)

BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. - Nikolas Bernaola; Mario Michiels; Concha Bielza; Pedro Larrañaga (PGM, 2020)

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. - Irene Córdoba; Concha Bielza; Pedro Larrañaga; Gherardo Varando (IEEE Access, 2020)

Incremental Learning of Latent Forests. - Fernando Rodriguez-Sanchez; Pedro Larrañaga; Concha Bielza (IEEE Access, 2020)

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. - Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (Eng. Appl. Artif. Intell., 2020)

On generating random Gaussian graphical models. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2020)

Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (CEC, 2021)

Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding. - David Quesada; Concha Bielza; Pedro Larrañaga (HAIS, 2021)

An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs. - Carlos Puerto-Santana; Pedro Larrañaga; Javier Diaz-Rozo; Concha Bielza (SOCO, 2021)

Multi-dimensional Bayesian network classifiers: A survey. - Santiago Gil-Begue; Concha Bielza; Pedro Larrañaga (Artif. Intell. Rev., 2021)

Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. - David Quesada; Gabriel Valverde; Pedro Larrañaga; Concha Bielza (Eng. Appl. Artif. Intell., 2021)

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks. - Bojan Mihaljevic; Pedro Larrañaga; Concha Bielza (Frontiers Neuroinformatics, 2021)

Multidimensional continuous time Bayesian network classifiers. - Carlos Villa-Blanco; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2021)

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. - Mario Michiels; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2021)

Bayesian networks for interpretable machine learning and optimization. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2021)

Quantum parametric circuit optimization with estimation of distribution algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (GECCO Companion, 2022)

Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection. - Jorge Casajús-Setién; Concha Bielza; Pedro Larrañaga (PGM, 2022)

Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling. - Enrique Valero-Leal; Pedro Larrañaga; Concha Bielza (PGM, 2022)

Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers. - Carlos Villa-Blanco; Alessandro Bregoli; Concha Bielza; Pedro Larrañaga; Fabio Stella (PGM, 2022)

Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks. - David Quesada; Concha Bielza; Pedro Fontán; Pedro Larrañaga (Int. J. Intell. Syst., 2022)

Multipartition clustering of mixed data with Bayesian networks. - Fernando Rodriguez-Sanchez; Concha Bielza; Pedro Larrañaga (Int. J. Intell. Syst., 2022)

PyBNesian: An extensible python package for Bayesian networks. - David Atienza; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2022)

Asymmetric HMMs for Online Ball-Bearing Health Assessments. - Carlos Puerto-Santana; Concha Bielza; Javier Diaz-Rozo; Guillem Ramirez-Gargallo; Filippo Mantovani; Gaizka Virumbrales; Jesús Labarta; Pedro Larrañaga (IEEE Internet Things J., 2022)

Semiparametric Bayesian networks. - David Atienza; Concha Bielza; Pedro Larrañaga (Inf. Sci., 2022)

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (J. Comb. Optim., 2022)

Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Anomaly Detection with Laser Heat Treatment Thermal Videos. - David Atienza; Concha Bielza; Javier Diaz-Rozo; Pedro Larrañaga (IEEE DataPort, 2022)

Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (CEC, 2023)

High-Dimensional Feature Characterization of Single Nucleotide Variants in Hypertrophic Cardiomyopathy. - Dafne Lozano-Paredes; Luis Bote; Concha Bielza; Pedro Larrañaga; María Sabater-Molina; Juan Ramón Gimeno; Sergio Muñoz; Francisco Javier Gimeno-Blanes; José Luis Rojo-Álvarez (CinC, 2023)

Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models. - Jorge Casajús-Setién; Concha Bielza; Pedro Larrañaga (CSR, 2023)

Context-specific kernel-based hidden Markov model for time series analysis. - Carlos Puerto-Santana; Concha Bielza; Pedro Larrañaga; Gustav Eje Henter (CoRR, 2023)

Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks. - David Quesada; Pedro Larrañaga; Concha Bielza (CoRR, 2023)

Causal reinforcement learning based on Bayesian networks applied to industrial settings. - Gabriel Valverde; David Quesada; Pedro Larrañaga; Concha Bielza (Eng. Appl. Artif. Intell., 2023)

Quantum approximate optimization algorithm for Bayesian network structure learning. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (Quantum Inf. Process., 2023)

Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. - Carlos Villa-Blanco; Alessandro Bregoli; Concha Bielza; Pedro Larrañaga; Fabio Stella (Int. J. Approx. Reason., 2023)

Efficient search for relevance explanations using MAP-independence in Bayesian networks. - Enrique Valero-Leal; Concha Bielza; Pedro Larrañaga; Silja Renooij (Int. J. Approx. Reason., 2023)

Feature subset selection for data and feature streams: a review. - Carlos Villa-Blanco; Concha Bielza; Pedro Larrañaga (Artif. Intell. Rev., 2023)

Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2023)

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. - Nikolas Bernaola; Mario Michiels; Pedro Larrañaga; Concha Bielza (PLoS Comput. Biol., 2023)

Multi-objective Counterfactuals in Bayesian Classifiers with Estimation of Distribution Algorithms. - Daniel Zaragoza-Pellicer; Concha Bielza; Pedro Larrañaga (PGM, 2024)

EDAspy: An extensible python package for estimation of distribution algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2024)

Feature Saliencies in Asymmetric Hidden Markov Models. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (IEEE Trans. Neural Networks Learn. Syst., 2024)

Semiparametric Estimation of Distribution Algorithms for Continuous Optimization. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (IEEE Trans. Evol. Comput., 2024)

Estimation of Distribution Algorithms in Machine Learning: A Survey. - Pedro Larrañaga; Concha Bielza (IEEE Trans. Evol. Comput., 2024)

Bandwidth Selectors on Semiparametric Bayesian Networks. - Victor Alejandre; Concha Bielza; Pedro Larrañaga (CoRR, 2025)

Binned semiparametric Bayesian networks. - Rafael Sojo; Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (CoRR, 2025)