  Transfer learning where the behavior of extracting transferable knowledge
from the source domain(s) and reusing this knowledge to target domain has
become a research area of great interest in the field of artificial
intelligence. Probabilistic graphical models (PGMs) have been recognized as a
powerful tool for modeling complex systems with many advantages, e.g., the
ability to handle uncertainty and possessing good interpretability. Considering
the success of these two aforementioned research areas, it seems natural to
apply PGMs to transfer learning. However, although there are already some
excellent PGMs specific to transfer learning in the literature, the potential
of PGMs for this problem is still grossly underestimated. This paper aims to
boost the development of PGMs for transfer learning by 1) examining the pilot
studies on PGMs specific to transfer learning, i.e., analyzing and summarizing
the existing mechanisms particularly designed for knowledge transfer; 2)
discussing examples of real-world transfer problems where existing PGMs have
been successfully applied; and 3) exploring several potential research
directions on transfer learning using PGM.
