Modelling conditional probability distributions for periodic variables

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Abstract

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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Details

Publication date26 Jun 1995
Publication titleFourth International Conference on Artificial Neural Networks
PublisherIEEE
Pages177-182
Number of pages6
Volume4
ISBN (Print)0852966415
Original languageEnglish
Event4th International Conference on Artificial Neural Networks - Cambridge, United Kingdom

Conference

Conference4th International Conference on Artificial Neural Networks
CountryUnited Kingdom
CityCambridge
Period26/06/9528/06/95

Bibliographic note

©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.

    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

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