Bayesian inference is a probability-based reasoning discipline grounded in Bayes' rule. When used to support data fusion, Bayesian inference belongs to the class of data fusion algorithms that use a priori knowledge about events or objects in an observation space to make inferences about the identity of events or objects in that space. Bayesian inference provides a method for calculating the conditional a posteriori probability of a hypothesis being true given supporting evidence. Thus, Bayes' rule offers a technique for updating beliefs in response to information or evidence that would cause the belief to change.