Transformer-based large language models (LLMs) have demonstrated strong reasoning abilities across diverse domains, from solving programming challenges to competing in strategy-intensive games such as chess. Prior work has shown that LLMs can develop emergent world models in games of perfect information, where internal representations correspond to latent states of the environment. In this paper, we extend this line of investigation to domains of incomplete information, focusing on poker as a canonical partially observable Markov decision process (POMDP). We pretrain a GPT-style model on Poker Hand History (PHH) data and probe its internal activations. Our results demonstrate that the model learns both deterministic structure—such as hand rank—and stochastic features—such as winning equity—without explicit supervision. Linear and nonlinear probes reveal that these representations are linearly decodable and correlate with theoretical belief states, suggesting that LLMs spontaneously approximate Bayesian updates over hidden game variables. Furthermore, visualization of activation spaces shows that the model organizes poker states into meaningful clusters aligned with game dynamics. These findings provide evidence that LLMs not only encode latent world representations in incomplete-information settings but also maintain structured belief states that support probabilistic reasoning.