Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
This work addresses computational efficiency in learning probabilistic graphical models for cumulative phenomena, but it is incremental as it compares existing search strategies and regularization methods on simulated datasets.
The authors tackled the NP-hard problem of structural learning for Bayesian Networks by focusing on Suppes-Bayes Causal Networks (SBCNs) with constraints to model cumulative phenomena, showing that these constraints dramatically improve inference accuracy by reducing the solution space and providing temporal ordering.
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science.