A key element for the development of Bayesian Belief Network (BBN) models is the assessment of the Conditional Probability Tables (CPTs). For application domains with shortage of empirical data, expert judgment becomes the main source to populate the CPTs. For large-scale models, the direct elicitation of all the CPT elements (the Conditional Probability Distributions, CPDs) is time-consuming, may be impractical, and can lead to inconsistencies. Help may come from methods to determine CPTs from limited information, aiming at eliciting partial model information from which to build the CPTs indirectly. The aim of this paper is to provide an overview of these methods. The methods are grouped based on the underlying strategy to populate the CPTs: noisy-OR based, CPD interpolation, building functions, and analytical hierarchical process. A general description is given. Selected aspects of the knowledge elicitation are discussed: whether methods provide guidance for the elicitation, consideration of possible inconsistencies in the expert assessments, and input node incompatibility. The type and amount of information required is presented with reference to an illustrative BBN, allowing a partial evaluation of the efficiency of the methods. This overview is intended to help readers understand the main features of the different methods and select an appropriate one for a given application: a comprehensive review is planned for future work.