Modelling conditional probability distributions for periodic variables

Published 1 January 1995

Ian T. Nabney

DOI10.1049/cp:19950550

Citations5

TL;DR

This paper introduces two novel techniques for estimating conditional probability densities and applies them to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Abstract

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

Keywords

Earth and Planetary SciencesEnvironmental Science

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