  The recent work `A unified Bayesian inference framework for generalized
linear models' \cite{meng1} shows that the GLM can be solved via iterating
between the standard linear module (SLM) (running with standard Bayesian
algorithm) and the minimum mean squared error (MMSE) module. The proposed
framework utilizes expectation propagation and corresponds to the sum-product
version \cite{Rangan1}. While in \cite{Rangan1}, a max-sum GAMP is also
proposed. What is their intrinsic relationship? This comment aims to answer
this.
