  The paper proposes a new hybrid Bayesian network learning algorithm, termed
Forward Early Dropping Hill Climbing (FEDHC), devised to work with either
continuous or categorical variables. 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. Further, specifically for the
case of continuous data, a robust to outliers version of FEDHC, that can be
adopted by other BN learning algorithms, 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. 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}.
