As a deterministic inference method for complicated problems, message passing methods and their applications in information fusion have drawn much attention in recent years. Message passing methods provide a Bayes-based, unified, scalable and efficient inference framework for large scale problems. Message passing methods pass messages between nodes of probabilistic graphical models. At first, probabilistic graphical models are briefly introduced. Then, the basic principles and a brief review of common message passing methods are given. Then, the recent applications of message passing methods in information fusion are presented from three aspects: state estimation and smoothing, target tracking and multi­source heterogeneous data fusion. Meanwhile, the appropriate scenarios of different message passing methods are summarized. Finally, the possible directions of future research of message passing methods in information fusion are discussed.