Modelling conditional probability distributions for periodic variables


Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two 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.

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Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Event Title: 4th International Conference on Artificial Neural Networks
Event Type: Other
Event Dates: 1995-06-26 - 1995-06-28
Uncontrolled Keywords: mixture density network,direction modelling,conditional probability,distributions,neural networks,periodic variables,radar scatterometer data,remote-sensing,synthetic data,wind vector,directions,neural nets
ISBN: 0852966415
Last Modified: 27 Jun 2024 12:04
Date Deposited: 10 Dec 2008 17:20
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... document/497812 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1995-06-26
Authors: Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)
Bishop, Christopher M.
Legleye, C.



Version: Published Version

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