Modelling conditional probability distributions for periodic variables

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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 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.

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Original languageEnglish
Pages (from-to)209-214
Number of pages6
JournalNeural Computation
Volume8
Issue5
StatePublished - 1 Jul 1996
EventInternational Conference on Artificial Neural Networks - Paris

    Keywords

  • conditional probability densities, periodic variables, synthetic data, wind vector, radar scatterometer data, remote-sensing, satellite.

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