While continuous variables become more and more inevitable in Bayesian networks for modeling real-life applications
in complex systems, there are not much software tools to support it. Popular commercial Bayesian
network tools such as Hugin, and Netica etc., are either expensive or have to discretize continuous variables.
In addition, some free programs existing in the literature, commonly known as BNT, GeNie/SMILE, etc, have
their own advantages and disadvantages respectively. In this paper, we introduce a newly developed Java tool
for model construction and inference for hybrid Bayesian networks. Via the representation power of the script
language, this tool can build the hybrid model automatically based on a well defined string that follows the
specific grammars. Furthermore, it implements several inference algorithms capable to accommodate hybrid
Bayesian networks, including Junction Tree algorithm (JT) for conditional linear Gaussian model (CLG), and
Direct Message Passing (DMP) for general hybrid Bayesian networks with CLG structure. We believe this tool
will be useful for researchers in the field.