Abstract
Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel 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.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing System 7 |
| Editors | G. Tesauro, D. S. Touretzky, T. D. Leen |
| Place of Publication | Denver, US |
| Publisher | MIT |
| Pages | 641-648 |
| Number of pages | 8 |
| Volume | 7 |
| ISBN (Print) | 0262201046 |
| Publication status | Published - 28 Nov 1994 |
| Event | Advances in Neural Information Processing Systems 1994 - Singapore, Singapore Duration: 16 Nov 1994 → 18 Nov 1994 |
Other
| Other | Advances in Neural Information Processing Systems 1994 |
|---|---|
| Country/Territory | Singapore |
| City | Singapore |
| Period | 16/11/94 → 18/11/94 |
Bibliographical note
Copyright of the Massachusetts Institute of Technology Press (MIT Press)Keywords
- conditional probability densities
- periodic variables
- performance
- wind vector
- radar scatterometer
- remote-sensing satellite