Modelling conditional probability distributions for periodic variables

Ian T. Nabney, Christopher M. Bishop, C. Legleye

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

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationFourth International Conference on Artificial Neural Networks
Place of PublicationUnited States
PublisherInstitution of Engineering and Technology (IET)
Number of pages6
ISBN (Print)0852966415
Publication statusPublished - 1 Jun 1995

Publication series

ISSN (Print)0537-9989

Bibliographical 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.


  • 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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