Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
This addresses the challenge of scalable probabilistic inference for researchers and practitioners in machine learning and AI, representing a strong incremental advance in efficient Bayesian network learning.
The paper tackles the problem of learning Bayesian networks with bounded treewidth from large datasets containing thousands of variables, resulting in a novel algorithm that consistently outperforms state-of-the-art methods on datasets with up to ten thousand variables.
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.