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.
| Original language | English |
|---|---|
| Pages (from-to) | 209-214 |
| Number of pages | 6 |
| Journal | Neural Computation |
| Volume | 8 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1 Jul 1996 |
| Event | International Conference on Artificial Neural Networks - Paris Duration: 1 Oct 1995 → … |
Bibliographical note
@ 1996 Massachusetts Institute of TechnologyKeywords
- conditional probability densities
- periodic variables
- synthetic data
- wind vector
- radar scatterometer data
- remote-sensing
- satellite.