# Variational inference for count response semiparametric regression

arXiv: https://arxiv.org/abs/1309.4199 · HTML: https://mlsanity.com/paper/1309.4199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Variational Inference for Bayesian Bridge Regression](https://mlsanity.com/paper/2205.09515/) (2022)

## Abstract (arXiv)

Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure.