Estimating conditional probability densities for periodic variables

Bishop, Christopher M. and Legleye, C. (1994). Estimating conditional probability densities for periodic variables. IN: Advances in Neural Information Processing System 7. Tesauro, G.; Touretzky, D. S. and Leen, T. D. (eds) SGP: MIT.


Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three 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.

Divisions: Aston University (General)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Event Title: Advances in Neural Information Processing Systems 1994
Event Type: Other
Event Dates: 1994-11-16 - 1994-11-18
Uncontrolled Keywords: conditional probability densities,periodic variables,performance,wind vector,radar scatterometer,remote-sensing satellite
Full Text Link:
Related URLs: http://mitpress ... type=2&tid=8420 (Publisher URL)
Published Date: 1994-11-28
Authors: Bishop, Christopher M.
Legleye, C.



Version: Published Version

Export / Share Citation


Additional statistics for this record