Stratified Graphical Models - Context-Specific Independence in Graphical Models
This work addresses the need for more flexible modeling in applications like genetic mapping and risk evaluation, though it is incremental as it builds on existing generalizations of graphical models.
The authors tackled the problem of overly stringent constraints in graphical models by introducing stratified graphical models, which allow for context-specific independence, and developed a Bayesian learning method for these models, demonstrating its effectiveness on real and synthetic datasets.
Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc. Despite of their generic applicability and wide adoptance, the constraints imposed by undirected graphical models and Bayesian networks have also been recognized to be unnecessarily stringent under certain circumstances. This observation has led to the proposal of several generalizations that aim at more relaxed constraints by which the models can impose local or context-specific dependence structures. Here we consider an additional class of such models, termed as stratified graphical models. We develop a method for Bayesian learning of these models by deriving an analytical expression for the marginal likelihood of data under a specific subclass of decomposable stratified models. A non-reversible Markov chain Monte Carlo approach is further used to identify models that are highly supported by the posterior distribution over the model space. Our method is illustrated and compared with ordinary graphical models through application to several real and synthetic datasets.