Modelling conditional probability distributions for periodic variables

Ian T Nabney, Christopher M. Bishop

Research output: Chapter in Book/Report/Conference proceedingChapter

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 International Conference on Artificial Neural Networks ICANN'95
EditorsF. Fougelman-Soulie, P. Gallinari
Place of PublicationParis (FR)
PublisherEC2 et Cie
Pages209-214
Number of pages6
Volume2
ISBN (Print)2-910085-19-8
Publication statusPublished - 1995
EventInternational Conference on Artificial Neural Networks - Paris
Duration: 1 Oct 1995 → …

Conference

ConferenceInternational Conference on Artificial Neural Networks
CityParis
Period1/10/95 → …

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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  • Cite this

    Nabney, I. T., & Bishop, C. M. (1995). Modelling conditional probability distributions for periodic variables. In F. Fougelman-Soulie, & P. Gallinari (Eds.), Proceedings International Conference on Artificial Neural Networks ICANN'95 (Vol. 2, pp. 209-214). EC2 et Cie.