This thesis describes work done on improving the knowledge representation, betting strategy, and opponent modeling of the Bayesian Poker Player (BPP), a poker-playing program developed at Monash University, which uses Bayesian networks to model the program's poker hand, the opponent's hand and the opponent's playing behaviour. An approach to model the dependence between opposing players hands is introduced as well as the various techniques used to refine and improve the way that the game is represented by the program. The opponent modeling aspect of the program is also of specific focus, with a number of beneficial improvements presented. Most significantly, a new betting strategy is described which makes use of decision networks for action selection. Experimental results show that a number of these enhancements represent a major advance in the strength of BPP and a discussion of the relative merits of each feature as well as a number of likely ways of improving play are provided.