Modelling conditional probability distributions for periodic variables

Bishop, Christopher M. and Nabney, Ian T. (1996). Modelling conditional probability distributions for periodic variables. Neural Computation, 8 (5), pp. 209-214.

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.

Publication DOI: https://doi.org/10.1162/neco.1996.8.5.1123
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: @ 1996 Massachusetts Institute of Technology
Uncontrolled Keywords: conditional probability densities,periodic variables,synthetic data,wind vector,radar scatterometer data,remote-sensing,satellite.
Published Date: 1996-07-01
Authors: Bishop, Christopher M.
Nabney, Ian T. ( 0000-0003-1513-993X)

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