Modelling Conditional Probability Distributions for Periodic Variables

Christopher Bishop, IanT. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and test them using synthetic data. We then apply them to the problem of extracting the distribution of wind vector directions from radar scatterometer data.
Original languageEnglish
Title of host publicationMathematics of Neural Networks
Subtitle of host publicationModels, Algorithms and Applications
EditorsStephenW. Ellacott, JohnC. Mason, IainJ. Anderson
PublisherSpringer US
Pages118-122
Number of pages5
ISBN (Electronic) 978-1-4615-6099-9
ISBN (Print)978-1-4613-7794-8
DOIs
Publication statusPublished - 1997

Publication series

NameOperations Research/Computer Science Interfaces Series
PublisherSpringer US
Volume8
ISSN (Print)1387-666X

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