Modeling Conditional Probability Distributions for Periodic Variables

Christopher M. Bishop, Ian T. Nabney

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1123-1133
Number of pages11
JournalNeural Computation
Issue number5
Publication statusPublished - 1 Jul 1996


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


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