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

arXiv: https://arxiv.org/abs/1904.04485 · HTML: https://mlsanity.com/paper/1904.04485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing](https://mlsanity.com/paper/2402.07366/) (2024)
- [A Unitary Transform Based Generalized Approximate Message Passing](https://mlsanity.com/paper/2210.08861/) (2022)

## Abstract (arXiv)

The recent work `A unified Bayesian inference framework for generalized linear models' citemeng1 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 citeRangan1. While in citeRangan1, a max-sum GAMP is also proposed. What is their intrinsic relationship? This comment aims to answer this.