Modelling conditional probability distributions for periodic variables

I. T. Nabney, C. M. Bishop, C. Legleye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 publicationArtificial Neural Networks, 1995., Fourth International Conference on
Number of pages6
ISBN (Print)0-85296-641-5
Publication statusPublished - Jun 1995


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