Estimating Conditional Probability Densities for Periodic Variables

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Chris M. Bishop, Claire Legleye


Most of the common techniques for estimating conditional prob(cid:173) ability densities are inappropriate for applications involving peri(cid:173) odic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance us(cid:173) ing 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.