Parallelization of the PC Algorithm
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 fi ve steps where the first step is to perform a set of (conditional) independence tests while the remaining four...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10835/4856 |
| Summary: | 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 fi ve 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 parallelization 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 signi cant time
performance improvements are possible using the proposed algorithm. |
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