Under the background of learning Bayesian network structure, we proposed a new method based on the KNN algorithm and dynamic Gibbs sampling to fill in the missing data, which is mainly used to solve the problem of how to learn the Bayesian network structure better with missing data sets. The experiments based on Asia Network show that, this method can restore the original data very well, which will make it available to use some Bayesian network structure learning algorithm only based on complete data. This method will expand the scope and improve the effect of Bayesian networks application.
bnstruct: an R package for Bayesian Network structure learning in the presence of missing data
