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 test them using synthetic data. We then apply them to the problem of extracting the distribution of wind vector directions from radar scatterometer data.
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© 1997 Springer Science+Business Media New York
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Bishop, C.M., Nabney, I.T. (1997). Modelling Conditional Probability Distributions for Periodic Variables. In: Ellacott, S.W., Mason, J.C., Anderson, I.J. (eds) Mathematics of Neural Networks. Operations Research/Computer Science Interfaces Series, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6099-9_17
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DOI: https://doi.org/10.1007/978-1-4615-6099-9_17
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