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.
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- mixture density network, direction modelling, conditional probability, distributions, neural networks, periodic variables, radar scatterometer data, remote-sensing, synthetic data, wind vector, directions, neural nets