This paper presents a technical approach for fusing information from diverse sources. Fusion requires appropriate weighting of information based on the quality of the source of the information. A credibility model characterizes the quality of information based on the source and the circumstances under which the information is collected. In many cases credibility is uncertain, so inference is necessary. Explicit probabilistic credibility models provide a computational model of the quality of the information that allows use of prior information, evidence when available, and opportunities for learning from data. This paper provides an overview of the challenges, describes the advanced probabilistic reasoning tools used to implement credibility models, and provides an example of the use of credibility models in a multi-source fusion process.