Belnap-Dunn logic (BD), sometimes also known as First Degree Entailment, is a four-valued propositional logic that complements the classical truth values of True and False with two non-classical truth values Neither and Both. The latter two are to account for the possibility of the available information being incomplete or providing contradictory evidence. We present a probabilistic extension of BD that permits agents to have probabilistic beliefs about the truth and falsity of a proposition. We briefly look into the axiomatization for the framework defined and also identify policies for conditionalization and aggregation.