Bishop, Christopher M. and Nabney, Ian T. (1996). Modelling conditional probability distributions for periodic variables. Neural Computation, 8 (5), pp. 209-214.
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.
| Publication DOI: | https://doi.org/10.1162/neco.1996.8.5.1123 |
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| Divisions: | ?? 50811700Jl ?? College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
| Additional Information: | @ 1996 Massachusetts Institute of Technology |
| Uncontrolled Keywords: | conditional probability densities,periodic variables,synthetic data,wind vector,radar scatterometer data,remote-sensing,satellite. |
| Publication ISSN: | 1530-888X |
| Last Modified: | 22 Nov 2024 08:04 |
| Date Deposited: | 23 Jun 2009 08:21 | PURE Output Type: | Article |
| Published Date: | 1996-07-01 |
| Authors: |
Bishop, Christopher M.
Nabney, Ian T. ( 
0000-0003-1513-993X)
|
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)