Modelling Conditional Probability Distributions for Periodic Variables

Christopher Bishop; IanT. Nabney

doi:10.1007/978-1-4615-6099-9_17

Modelling Conditional Probability Distributions for Periodic Variables

Christopher Bishop, IanT. Nabney

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

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.

Original language	English
Title of host publication	Mathematics of Neural Networks
Subtitle of host publication	Models, Algorithms and Applications
Editors	StephenW. Ellacott, JohnC. Mason, IainJ. Anderson
Publisher	Springer US
Pages	118-122
Number of pages	5
ISBN (Electronic)	978-1-4615-6099-9
ISBN (Print)	978-1-4613-7794-8
DOIs	https://doi.org/10.1007/978-1-4615-6099-9_17
Publication status	Published - 1997

Publication series

Name	Operations Research/Computer Science Interfaces Series
Publisher	Springer US
Volume	8
ISSN (Print)	1387-666X

Access to Document

10.1007/978-1-4615-6099-9_17

http://link.springer.com/chapter/10.1007%2F978-1-4615-6099-9_17

Cite this

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title = "Modelling Conditional Probability Distributions for Periodic Variables",

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.",

author = "Christopher Bishop and IanT. Nabney",

year = "1997",

doi = "10.1007/978-1-4615-6099-9_17",

language = "English",

isbn = "978-1-4613-7794-8",

series = "Operations Research/Computer Science Interfaces Series",

publisher = "Springer US",

pages = "118--122",

editor = "StephenW. Ellacott and JohnC. Mason and IainJ. Anderson",

booktitle = "Mathematics of Neural Networks",

address = "United States",

}

Modelling Conditional Probability Distributions for Periodic Variables. / Bishop, Christopher; Nabney, IanT.

Mathematics of Neural Networks: Models, Algorithms and Applications. ed. / StephenW. Ellacott; JohnC. Mason; IainJ. Anderson. Springer US, 1997. p. 118-122 (Operations Research/Computer Science Interfaces Series; Vol. 8).

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

TY - CHAP

T1 - Modelling Conditional Probability Distributions for Periodic Variables

AU - Bishop, Christopher

AU - Nabney, IanT.

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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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DO - 10.1007/978-1-4615-6099-9_17

M3 - Chapter

SN - 978-1-4613-7794-8

T3 - Operations Research/Computer Science Interfaces Series

SP - 118

EP - 122

BT - Mathematics of Neural Networks

A2 - Ellacott, StephenW.

A2 - Mason, JohnC.

A2 - Anderson, IainJ.

PB - Springer US

ER -

Modelling Conditional Probability Distributions for Periodic Variables

Abstract

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