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In this article, we report on a performance comparison study of inferences on graphical models for model-to-image registration. Both Markov chain Monte Carlo (MCMC) and nonparametric belief propagation (NBP) are widely used for inferring marginal posterior distributions of random variables on graphical models. It is known that the accuracy of the inferred distributions changes according to the methods used for the inference and to the structures of graphical models. In this article, we focus on a model-to-image registration method, which registers a surface model to given 3D images based on the inference on a graphical model. We applied MCMC and NBP for the inference and compared the accuracy of the registration on different structures of graphical models. Then, MCMC outperformed NBP significantly in the accuracy.