Estimating Conditional Probability Densities for Periodic Variables
暂无分享,去创建一个
Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three 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.
[1] K. Mardia. Statistics of Directional Data , 1972 .
[2] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[3] Yong Liu,et al. Robust Parameter Estimation and Model Selection for Neural Network Regression , 1993, NIPS.
[4] Halbert White,et al. Parametric Statistical Estimation with Artificial Neural Networks: A Condensed Discussion , 1994 .
[5] C. Bishop. Mixture density networks , 1994 .