Learning Discrete Bayesian Networks from Continuous Data
This work addresses the challenge of discretizing continuous variables for Bayesian network learning, which is crucial for improving accuracy, speed, and interpretability in fields like data analysis and machine learning, though it is incremental in nature.
The paper tackles the problem of learning Bayesian networks from continuous data by introducing a principled Bayesian discretization method with quadratic complexity, which outperforms the established minimum description length algorithm in empirical demonstrations.
Learning Bayesian networks from raw data can help provide insights into the relationships between variables. While real data often contains a mixture of discrete and continuous-valued variables, many Bayesian network structure learning algorithms assume all random variables are discrete. Thus, continuous variables are often discretized when learning a Bayesian network. However, the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting models. This paper introduces a principled Bayesian discretization method for continuous variables in Bayesian networks with quadratic complexity instead of the cubic complexity of other standard techniques. Empirical demonstrations show that the proposed method is superior to the established minimum description length algorithm. In addition, this paper shows how to incorporate existing methods into the structure learning process to discretize all continuous variables and simultaneously learn Bayesian network structures.