The FEDHC Bayesian network learning algorithm

A new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. FEDHC consists of a skeleton identification phase and a subsequent scoring phase that assigns the (causal) directions. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. In addition, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms as well is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Specifically, FEDHC yields more accurate Bayesian networks than PCHC with continuous data but less accurate with categorical data. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.