Modelling conditional probability distributions for periodic variables

Ian T Nabney, Christopher M. Bishop

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

2 Citations (Scopus)
11 Downloads (Pure)

Abstract

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Artificial Neural Networks (ICANN'95)
EditorsF. Fougelman-Soulie, P. Gallinari
PublisherEC2 et Cie
Pages209-214
Number of pages6
Volume2
ISBN (Print)9782910085193, 2910085198
Publication statusPublished - 1995

Bibliographical note

International Conference on Artificial Neural Networks, Paris (FR), October 2005.

Keywords

  • estimating conditional probability densities, periodic variables, distribution of wind vector directions, radar scatterometer data, remote-sensing satellite

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