Many real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.