Adaptive Path Interpolation for Sparse Systems: Application to a Simple Censored Block Model
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A new adaptive path interpolation method has been recently developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and generalized estimation, superposition codes, or low rank matrix and tensor estimation. For all these systems the method directly proves in a unified manner that the replica symmetric prediction is exact. When the underlying factor graph of the inference problem is sparse the replica prediction is considerably more complicated and rigorous results are often lacking or obtained by rather complicated methods. In this contribution we extend the adaptive path interpolation method to sparse systems. We concentrate on a Censored Block Model, where hidden variables are measured through a binary erasure channel, for which we fully prove the replica prediction.
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