"Ideal Parent" Structure Learning for Continuous Variable Networks
This work addresses a bottleneck in Bayesian network learning for continuous variables, which is incremental as it improves efficiency in an existing framework.
The paper tackles the computationally expensive problem of learning Bayesian network structures for continuous variables, especially with hidden variables, by presenting a method that significantly speeds up structure search and efficiently adds new hidden variables, demonstrating its effectiveness on several datasets.
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.