Laplace Propagation
Abstract
We present a novel method for approximate inference in Bayesian mod- els and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of condi- tional probabilities in factorizing distributions, much akin to Minka’s Expectation Propagation. In the jointly normal case, it coincides with the latter and belief propagation, whereas in the general case, it provides an optimization strategy containing Support Vector chunking, the Bayes Committee Machine, and Gaussian Process chunking as special cases.
Cite
Text
Eskin et al. "Laplace Propagation." Neural Information Processing Systems, 2003.Markdown
[Eskin et al. "Laplace Propagation." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/eskin2003neurips-laplace/)BibTeX
@inproceedings{eskin2003neurips-laplace,
title = {{Laplace Propagation}},
author = {Eskin, Eleazar and Smola, Alex J. and Vishwanathan, S.v.n.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {441-448},
url = {https://mlanthology.org/neurips/2003/eskin2003neurips-laplace/}
}