### Abstract

Original language | English |
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Title of host publication | Proceedings International Conference on Artificial Neural Networks ICANN'95 |

Editors | F. Fougelman-Soulie, P. Gallinari |

Place of Publication | Paris (FR) |

Publisher | EC2 et Cie |

Pages | 209-214 |

Number of pages | 6 |

Volume | 2 |

ISBN (Print) | 2-910085-19-8 |

Publication status | Published - 1995 |

Event | International Conference on Artificial Neural Networks - Paris Duration: 1 Oct 1995 → … |

### Conference

Conference | International Conference on Artificial Neural Networks |
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City | Paris |

Period | 1/10/95 → … |

### Fingerprint

### Bibliographical note

International Conference on Artificial Neural Networks, Paris (FR), October 2005.### Keywords

- estimating conditional probability densities
- periodic variables
- distribution of wind vector directions
- radar scatterometer data
- remote-sensing satellite

### Cite this

*Proceedings International Conference on Artificial Neural Networks ICANN'95*(Vol. 2, pp. 209-214). Paris (FR): EC2 et Cie.

}

*Proceedings International Conference on Artificial Neural Networks ICANN'95.*vol. 2, EC2 et Cie, Paris (FR), pp. 209-214, International Conference on Artificial Neural Networks, Paris, 1/10/95.

**Modelling conditional probability distributions for periodic variables.** / Nabney, Ian T; Bishop, Christopher M.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Modelling conditional probability distributions for periodic variables

AU - Nabney, Ian T

AU - Bishop, Christopher M.

N1 - International Conference on Artificial Neural Networks, Paris (FR), October 2005.

PY - 1995

Y1 - 1995

N2 - Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

AB - Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

KW - estimating conditional probability densities

KW - periodic variables

KW - distribution of wind vector directions

KW - radar scatterometer data

KW - remote-sensing satellite

M3 - Chapter

SN - 2-910085-19-8

VL - 2

SP - 209

EP - 214

BT - Proceedings International Conference on Artificial Neural Networks ICANN'95

A2 - Fougelman-Soulie, F.

A2 - Gallinari, P.

PB - EC2 et Cie

CY - Paris (FR)

ER -