Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles
Abstract
We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded span(cid:173) ning trees, it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other meth(cid:173) ods, the embedded trees algorithm also computes exact error co(cid:173) variances. The error covariance computation is most efficient for graphs in which removing a small number of edges reveals an em(cid:173) bedded tree. In this context, we demonstrate that sparse loopy graphs can provide a significant increase in modeling power rela(cid:173) tive to trees, with only a minor increase in estimation complexity.
Cite
Text
Wainwright et al. "Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles." Neural Information Processing Systems, 2000.Markdown
[Wainwright et al. "Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/wainwright2000neurips-treebased/)BibTeX
@inproceedings{wainwright2000neurips-treebased,
title = {{Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles}},
author = {Wainwright, Martin J. and Sudderth, Erik B. and Willsky, Alan S.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {661-667},
url = {https://mlanthology.org/neurips/2000/wainwright2000neurips-treebased/}
}