We investigate a stochastic process called ''Bayesian Allocation Model'' where tokens are allocated randomly to a tensor and the probability for each index is specified by a graphical model. By integrating out some parameters of the model analytically, we obtain a Polya Urn Model, which enables us to calculate statistics about the model more efficiently by respecting the sparsity of the tensor.