Modelling conditional probability distributions for periodic variables

Christopher M. Bishop, Ian T. Nabney

Research output: Contribution to journalArticlepeer-review


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)209-214
Number of pages6
JournalNeural Computation
Issue number5
Publication statusPublished - 1 Jul 1996
EventInternational Conference on Artificial Neural Networks - Paris
Duration: 1 Oct 1995 → …

Bibliographical note

@ 1996 Massachusetts Institute of Technology


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


Dive into the research topics of 'Modelling conditional probability distributions for periodic variables'. Together they form a unique fingerprint.

Cite this