Modelling conditional probability distributions for periodic variables

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: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical 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.
PURE Output Type: Article
Published Date: 1996-07-01
Authors: Bishop, Christopher M.
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)

Download

[img]

Version: Published Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record