  We use freeness assumptions of random matrix theory to analyze the dynamical
behavior of inference algorithms for probabilistic models with dense coupling
matrices in the limit of large systems. For a toy Ising model, we are able to
recover previous results such as the property of vanishing effective memories
and the analytical convergence rate of the algorithm.
