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.
Keywords
- Synthetic Data
- Periodic Variable
- Conditional Probability Distribution
- Conditional Probability Density
- Model Order Selection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
C M Bishop, Mixture density networks. Technical Report NCRG/4288, Neural Computing Research Group, Aston University, U.K. (1994).
C. M. Bishop, Neural Networks for Pattern Recognition. Oxford University Press (1995).
C. M. Bishop and C. Legleye, Estimating conditional probability distributions for periodic variables, in: D. S. Touretzky, G. Tesauro, and T. K. Leen, editors, Advances in Neural Information Processing Systems, Vol. 7 (1995), pp641–648, Cambridge MA, MIT Press.
R A Jacobs, M I Jordan, S J Nowlan, and G E Hinton, Adaptive mixtures of local experts, Neural Computation, Vol. 3 (1991), pp79–87.
Y Liu, Robust neural network parameter estimation and model selection for regression, in: Advances in Neural Information Processing Systems, Vol.6 (1994), pp192–199, Morgan Kaufmann.
K V Mardia, Statistics of Directional Data. Academic Press, London (1972).
G J McLachlan and K E Basford, Mixture models: Inference and Applications to Clustering. Marcel Dekker, New York (1988).
S Thiria, C Mejia, F Badran, and M Crepon, A neural network approach for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data, Journal of Geophysical Research, Vol. 98(C12) (1993), pp22827–22841.
D M Titterington, A F M Smith, and U E Makov, Statistical Analysis of Finite Mixture Distributions, John Wiley, Chichester (1985).
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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
Publisher Name: Springer, Boston, MA
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