A Novel Method to Estimate Parents and Children for Local Bayesian Network Learning

Sergio del Río, Edwin Villanueva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The Markov Blanket of a random variable is the minimum conditioning set of variables that makes the variable independent of all other variables. A core step to estimate the Markov Blanket is the identification of the Parents and Children (PC) variable set. This paper propose a novel Parents and Children discovery algorithm, called Max-Min Random Walk Parents and Children (MMRWPC), which improves the computational burden of the classical Max-Min Parents and Children method (MMPC). The improvement was achieved with a series of modifications, including the introduction of a random walk process to better identifying conditioning sets in the conditional independence (CI) tests, implying in a significantly reduction of expensive high-order CI tests. In a series of experiments with data sampled from benchmark Bayesian networks we show the suitability of the proposed method.
Original languageSpanish
Title of host publicationIntelligent Systems and Applications Conference (IntelliSys 2021)
Number of pages18
StatePublished - 1 Jan 2022
Externally publishedYes

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