Modeling Conditional Probability Distributions for Periodic Variables
Abstract
Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using 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 Nabney. "Modeling Conditional Probability Distributions for Periodic Variables." Neural Computation, 1996. doi:10.1162/NECO.1996.8.5.1123Markdown
[Bishop and Nabney. "Modeling Conditional Probability Distributions for Periodic Variables." Neural Computation, 1996.](https://mlanthology.org/neco/1996/bishop1996neco-modeling/) doi:10.1162/NECO.1996.8.5.1123BibTeX
@article{bishop1996neco-modeling,
title = {{Modeling Conditional Probability Distributions for Periodic Variables}},
author = {Bishop, Christopher M. and Nabney, Ian T.},
journal = {Neural Computation},
year = {1996},
pages = {1123-1133},
doi = {10.1162/NECO.1996.8.5.1123},
volume = {8},
url = {https://mlanthology.org/neco/1996/bishop1996neco-modeling/}
}