Learning All Credible Bayesian Network Structures for Model Averaging
This work addresses the need for scalable and accurate model averaging in Bayesian network learning, which is crucial for applications in knowledge discovery and prediction, though it appears incremental in improving efficiency over prior methods.
The paper tackles the problem of model averaging for Bayesian networks, which is limited by scalability and structural restrictions in existing methods, and introduces a novel approach that efficiently scales to larger networks while focusing on credible, near-optimal models.
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.