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The recent work `A unified Bayesian inference framework for generalizedlinear models' \cite{meng1} shows that the GLM can be solved via iteratingbetween the standard linear module (SLM) (running with standard Bayesianalgorithm) and the minimum mean squared error (MMSE) module. The proposedframework utilizes expectation propagation and corresponds to the sum-productversion \cite{Rangan1}. While in \cite{Rangan1}, a max-sum GAMP is alsoproposed. What is their intrinsic relationship? This comment aims to answerthis.

A comment on the "A unified Bayesian inference framework for generalized linear models"