Estimating Conditional Probability Densities for Periodic Variables
Abstract
Most of the common techniques for estimating conditional prob(cid:173) ability densities are inappropriate for applications involving peri(cid:173) odic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance us(cid:173) ing synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.
Cite
Text
Bishop and Legleye. "Estimating Conditional Probability Densities for Periodic Variables." Neural Information Processing Systems, 1994.Markdown
[Bishop and Legleye. "Estimating Conditional Probability Densities for Periodic Variables." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/bishop1994neurips-estimating/)BibTeX
@inproceedings{bishop1994neurips-estimating,
title = {{Estimating Conditional Probability Densities for Periodic Variables}},
author = {Bishop, Chris M. and Legleye, Claire},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {641-648},
url = {https://mlanthology.org/neurips/1994/bishop1994neurips-estimating/}
}