Staged trees are probabilistic graphical models capable of representing any
class of non-symmetric independence via a coloring of its vertices. Several
structural learning routines have been defined and implemented to learn staged
trees from data, under the frequentist or Bayesian paradigm. They assume a data
set has been observed fully and, in practice, observations with missing entries
are either dropped or imputed before learning the model. Here, we introduce the
first algorithms for staged trees that handle missingness within the learning
of the model. To this end, we characterize the likelihood of staged tree models
in the presence of missing data and discuss pseudo-likelihoods that approximate
it. A structural expectation-maximization algorithm estimating the model
directly from the full likelihood is also implemented and evaluated. A
computational experiment showcases the performance of the novel learning
algorithms, demonstrating that it is feasible to account for different
missingness patterns when learning staged trees.

该研究介绍了处理缺失数据的阶梯树学习算法，通过模型的全似然性能直接估计模型，证明了在学习阶梯树时考虑不同的缺失模式是可行的。