On Using the PC Algorithm for Learning Continuous Bayesian Networks: An Experimental Analysis

Fernández, Antonio; Pérez-Bernabé, Inmaculada; Salmerón, Antonio

doi:10.1007/978-3-642-40643-0_35

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The most straightforward approach is to discretize the continuous variables, so that statistical tests of independence can be carried out, and therefore, algorithms such as the PC can be applied. This approach has been explored by Fernández et al [25].…”

Section: Handling Continuous Variablesmentioning

confidence: 99%

A survey on Bayesian network structure learning from data

2019

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A necessary step in the development of artificial intelligence is to enable a machine to represent how the world works, building an internal structure from data. This structure should hold a good trade-off between expressive power and querying efficiency. Bayesian networks have proven to be an effective and versatile tool for the task at hand. They have been applied to modeling knowledge in a variety of fields, ranging from bioinformatics to law, from image processing to economic risk analysis. A crucial aspect is learning the dependency graph of a Bayesian network from data. This task, called structure learning, is NP-hard and is the subject of intense, cutting-edge research. In short, it can be thought of as choosing one graph over the many candidates, grounding our reasoning over a collection of samples of the distribution generating the data. The number of possible graphs increases very quickly at the increase in the number of variables. Searching in this space, and selecting a graph over the others, becomes quickly burdensome. In this survey, we review the most relevant structure learning algorithms that have been proposed in the literature. We classify them according to the approach they follow for solving the problem and we also show alternatives for handling missing data and continuous variable. An extensive review of existing software tools is also given.

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Section: Handling Continuous Variablesmentioning

confidence: 99%

A survey on Bayesian network structure learning from data

2019

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Parameter learning in hybrid Bayesian networks using prior knowledge

Pérez-Bernabé

Fernández

Rumí

et al. 2015

Data Min Knowl Disc

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Mixtures of truncated basis functions have been recently proposed as a generalisation of mixtures of truncated exponentials and mixtures of polynomials for modelling univariate and conditional distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning the parameters of marginal and conditional MoTBF densities when both prior knowledge and data are available. Incorporating prior knowledge provide a valuable tool for obtaining useful models, especially in domains of applications where data are costly or scarce, and prior knowledge is available from practitioners. We explore scenarios where the prior knowledge can be expressed as an MoTBF density that is afterwards combined with another MoTBF density estimated from the available data. The resulting model remains within the MoTBF class which is a convenient property from the point of view of inference in hybrid Bayesian networks. The performance of the proposed method is tested in a series of experiments carried out over synthetic and real data.

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On Using the PC Algorithm for Learning Continuous Bayesian Networks: An Experimental Analysis

Cited by 2 publications

References 12 publications

A survey on Bayesian network structure learning from data

A survey on Bayesian network structure learning from data

Parameter learning in hybrid Bayesian networks using prior knowledge

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