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In this work, two novel sequential algorithms for learning Bayesian networks are proposed. The presented sequential search methods are an adaptation of a pair of algorithms proposed to feature subset selection: Sequential Forward Floating Selection and Sequential Backward Floating Selection. As far as we know, these algorithms have never been used for learning Bayesian networks. An empirical comparison...
When the amount of available data is small with respect to the problem size, many Bayesian networks can account for the data in a similar way. In these cases model averaging offers a framework which allows us to make better predictions than model selection. Selective model averaging directly uses a subset of these networks with high probability mass to reason over the probability of the structural...
Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is superexponentially large. As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and tree-augmented Bayesian networks, which involves no search at all. There is substantial evidence in the literature that the performance...
To learn Bayesian networks, one must estimate the parameters of the network from the data. EM (Expectation-Maximization) and gradient-based algorithms are the two best known techniques to estimate these parameters. Although the theoretical properties of these two frameworks are well-studied, it remains an open question as to when and whether EM is to be preferred over gradients. We will answer this...
In a model selection procedure where many models are to be compared, computational efficiency is critical. For acyclic digraph (ADG) Markov models (aka DAG models or Bayesian networks), each ADG Markov equivalence class can be represented by a unique chain graph, called an essential graph (EG). This parsimonious representation might be used to facilitate selection among ADG models. Because EGs combine...
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