• A Parallel Algorithm for Bayesian Network Structure Learning from Large Data Sets 

      Madsen, Anders L.; Jensen, Frank; Salmeron, Antonio; Langseth, Helge; Nielsen, Thomas D. (Journal article; Peer reviewed, 2017)
      This paper considers a parallel algorithm for Bayesian network structure learning from large data sets. The parallel algorithm is a variant of the well known PC algorithm. The PC algorithm is a constraint-based algorithm ...
    • A Review of Inference Algorithms for Hybrid Bayesian Networks 

      Salmeron, Antonio; Rumi, Rafael; Langseth, Helge; Nielsen, Thomas D.; Madsen, Anders L. (Journal article; Peer reviewed, 2018)
      Hybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which ...
    • MAP inference in dynamic hybrid Bayesian networks 

      Ramos-López, Dario; Masegosa, Andres; Martinez, Ana M.; Salmeron, Antonio; Nielsen, Thomas D.; Langseth, Helge; Madsen, Anders L. (Journal article; Peer reviewed, 2017)
      In this paper, we study the maximum a posteriori (MAP) problem in dynamic hybrid Bayesian networks. We are interested in finding the sequence of values of a class variable that maximizes the posterior probability given ...
    • Modeling concept drift: A probabilistic graphical model based approach 

      Borchani, Hanen; Martinez, Ana M.; Masegosa, Andres; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio; Fernandez, Antonio; Madsen, Anders L.; Sáez, Ramón (Journal article; Peer reviewed, 2015-11-22)
      An often used approach for detecting and adapting to concept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, ...
    • Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks 

      Ramos-López, Dario; Masegosa, Andres; Salmeron, Antonio; Rumi, Rafael; Langseth, Helge; Nielsen, Thomas D.; Madsen, Anders L. (Journal article; Peer reviewed, 2018)
      In this paper we propose a scalable importance sampling algorithm for computing Gaussian mixture posteriors in conditional linear Gaussian Bayesian networks. Our contribution is based on using a stochastic gradient ascent ...
    • Scaling up Bayesian variational inference using distributed computing clusters 

      Masegosa, Andres; Martinez, Ana M.; Langseth, Helge; Nielsen, Thomas D.; Salmeron, Antonio; Ramos-López, Dario (Journal article; Peer reviewed, 2017)
      In this paper we present an approach for scaling up Bayesian learning using variational methods by exploiting distributed computing clusters managed by modern big data processing tools like Apache Spark or Apache Flink, ...