We present an automatic classification method for astronomical catalogs with
missing data. We use Bayesian networks, a probabilistic graphical model, that
allows us to perform inference to pre- dict missing values given observed data
and dependency relationships between variables. To learn a Bayesian network
from incomplete data, we use an iterative algorithm that utilises sampling
methods and expectation maximization to estimate the distributions and
probabilistic dependencies of variables from data with missing values. To test
our model we use three catalogs with missing data (SAGE, 2MASS and UBVI) and
one complete catalog (MACHO). We examine how classification accuracy changes
when information from missing data catalogs is included, how our method
compares to traditional missing data approaches and at what computational cost.
Integrating these catalogs with missing data we find that classification of
variable objects improves by few percent and by 15% for quasar detection while
keeping the computational cost the same.

本文提出了一种利用贝叶斯网络进行天文目录分类的自动分类方法，利用基于采样的期望最大化算法来学习贝叶斯网络，并在 SAGE、2MASS 和 UBVI 等缺失数据的目录和一个完整的目录上测试了该方法，发现该方法在减少计算成本的同时提高了变量物体分类准确性。