Texas Hold'em poker provides an interesting test-bed for AI research with characteristics such as uncertainty and imperfect information, which can also be found in domains like medical decision making. Poker introduces these characteristics through its stochastic nature and limited information about other players strategy and hidden cards. This thesis presents the development of a Bayesian Case-based Reasoner for Poker (BayCaRP). BayCaRP uses a Bayesian network to model opponent behaviour and infer information about their most likely cards. The case-based reasoner uses this information to make an informed betting decision. Our results suggests that the two reasoning methodologies combined achieve a better performance than either could on its own.