Affine Independent Variational Inference
Abstract
We present a method for approximate inference for a broad class of non-conjugate probabilistic models. In particular, for the family of generalized linear model target densities we describe a rich class of variational approximating densities which can be best fit to the target by minimizing the Kullback-Leibler divergence. Our approach is based on using the Fourier representation which we show results in efficient and scalable inference.
Cite
Text
Challis and Barber. "Affine Independent Variational Inference." Neural Information Processing Systems, 2012.Markdown
[Challis and Barber. "Affine Independent Variational Inference." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/challis2012neurips-affine/)BibTeX
@inproceedings{challis2012neurips-affine,
title = {{Affine Independent Variational Inference}},
author = {Challis, Edward and Barber, David},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2186-2194},
url = {https://mlanthology.org/neurips/2012/challis2012neurips-affine/}
}