A Linear Bayesian Updating Model for Probabilistic Spatial Classification

A Linear Bayesian Updating Model for Probabilistic Spatial Classification

Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the the...

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Main Authors: Xiang Huang, Zhizhong Wang
Format: Article
Language:English
Published: MDPI AG 2016-11-01
Series:Challenges
Subjects:
expert opinions
linear Bayesian updating
spatial classification
transition probabilities
Online Access:http://www.mdpi.com/2078-1547/7/2/21
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spelling doaj-9bcad70b3f1e400981a795fb4b32f1e02020-11-24T21:07:56ZengMDPI AGChallenges2078-15472016-11-01722110.3390/challe7020021challe7020021A Linear Bayesian Updating Model for Probabilistic Spatial ClassificationXiang Huang0Zhizhong Wang1Department of Statistics, Central South University, Changsha 410012, Hunan, ChinaDepartment of Statistics, Central South University, Changsha 410012, Hunan, ChinaCategorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classification through an expert system. The main purpose of this paper is to present the solid theoretical foundations of the LBU approach. Since the LBU idea is originated from aggregating expert opinions and is not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, such as remote sensing images or area-class maps.http://www.mdpi.com/2078-1547/7/2/21expert opinionslinear Bayesian updatingspatial classificationtransition probabilities
collection DOAJ
language English
format Article
sources DOAJ
author Xiang Huang
Zhizhong Wang
spellingShingle Xiang Huang
Zhizhong Wang
A Linear Bayesian Updating Model for Probabilistic Spatial Classification
Challenges
expert opinions
linear Bayesian updating
spatial classification
transition probabilities
author_facet Xiang Huang
Zhizhong Wang
author_sort Xiang Huang
title A Linear Bayesian Updating Model for Probabilistic Spatial Classification
title_short A Linear Bayesian Updating Model for Probabilistic Spatial Classification
title_full A Linear Bayesian Updating Model for Probabilistic Spatial Classification
title_fullStr A Linear Bayesian Updating Model for Probabilistic Spatial Classification
title_full_unstemmed A Linear Bayesian Updating Model for Probabilistic Spatial Classification
title_sort linear bayesian updating model for probabilistic spatial classification
publisher MDPI AG
series Challenges
issn 2078-1547
publishDate 2016-11-01
description Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classification through an expert system. The main purpose of this paper is to present the solid theoretical foundations of the LBU approach. Since the LBU idea is originated from aggregating expert opinions and is not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, such as remote sensing images or area-class maps.
topic expert opinions
linear Bayesian updating
spatial classification
transition probabilities
url http://www.mdpi.com/2078-1547/7/2/21
work_keys_str_mv AT xianghuang alinearbayesianupdatingmodelforprobabilisticspatialclassification
AT zhizhongwang alinearbayesianupdatingmodelforprobabilisticspatialclassification
AT xianghuang linearbayesianupdatingmodelforprobabilisticspatialclassification
AT zhizhongwang linearbayesianupdatingmodelforprobabilisticspatialclassification
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