Modeling Conditional Probability Distributions for Periodic Variables

Christopher M. Bishop; Ian T. Nabney

doi:10.1162/neco.1996.8.5.1123

Modeling Conditional Probability Distributions for Periodic Variables

Christopher M. Bishop
, Ian T. Nabney

Research output: Contribution to journal › Article (Academic Journal) › peer-review

8 Citations (Scopus)

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 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.

Original language	English
Pages (from-to)	1123-1133
Number of pages	11
Journal	Neural Computation
Volume	8
Issue number	5
DOIs	https://doi.org/10.1162/neco.1996.8.5.1123
Publication status	Published - 1 Jul 1996

Keywords

conditional probability densities, periodic variables, synthetic data, wind vector, radar scatterometer data, remote-sensing, satellite.

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title = "Modeling 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 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.",

keywords = "conditional probability densities, periodic variables, synthetic data, wind vector, radar scatterometer data, remote-sensing, satellite.",

author = "Bishop, \{Christopher M.\} and Nabney, \{Ian T.\}",

year = "1996",

month = jul,

day = "1",

doi = "10.1162/neco.1996.8.5.1123",

language = "English",

volume = "8",

pages = "1123--1133",

journal = "Neural Computation",

issn = "0899-7667",

publisher = "Massachusetts Institute of Technology (MIT) Press",

number = "5",

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TY - JOUR

T1 - Modeling Conditional Probability Distributions for Periodic Variables

AU - Bishop, Christopher M.

AU - Nabney, Ian T.

PY - 1996/7/1

Y1 - 1996/7/1

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 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.

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 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.

KW - conditional probability densities, periodic variables, synthetic data, wind vector, radar scatterometer data, remote-sensing, satellite.

U2 - 10.1162/neco.1996.8.5.1123

DO - 10.1162/neco.1996.8.5.1123

M3 - Article (Academic Journal)

SN - 0899-7667

VL - 8

SP - 1123

EP - 1133

JO - Neural Computation

JF - Neural Computation

IS - 5

ER -

Modeling Conditional Probability Distributions for Periodic Variables

Abstract

Keywords

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