Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
This work addresses the challenge of structure learning in Bayesian Networks for researchers and practitioners, but it is incremental as it focuses on benchmarking existing methods rather than introducing new ones.
The authors tackled the problem of learning Bayesian Network structures from data by providing a detailed quantitative comparison of various state-of-the-art methods, including assessments on simulated data with discrete and continuous variables under different noise conditions.
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions, and by the fact that the problem is NP-hard. Hence, full enumeration of all the possible solutions is not always feasible and approximations are often required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before. For this reason, in this work, we provide a detailed comparison of many different state-of-the-arts methods for structural learning on simulated data considering both BNs with discrete and continuous variables, and with different rates of noise in the data. In particular, we investigate the performance of different widespread scores and algorithmic approaches proposed for the inference and the statistical pitfalls within them.