Abstract
This paper describes a parallel version of the PC algorithm for learning the structure of a Bayesian network from data. The PC algorithm is a constraint-based algorithm consisting of five steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (conditional) independence tests. In this paper, we describe a new approach to parallelisation of the (conditional) independence testing as experiments illustrate that this is by far the most time consuming step. The proposed parallel PC algorithm is evaluated on data sets generated at random from five different real-world Bayesian networks. The results demonstrate that significant time performance improvements are possible using the proposed algorithm.
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Madsen, A.L., Jensen, F., Salmerón, A., Langseth, H., Nielsen, T.D. (2015). Parallelisation of the PC Algorithm. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_2
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DOI: https://doi.org/10.1007/978-3-319-24598-0_2
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