A Linear Bayesian Updating Model for Probabilistic Spatial Classification
Department of Statistics, Central South University, Changsha 410012, Hunan, China
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Author to whom correspondence should be addressed.
Academic Editor: Palmiro Poltronieri
Challenges 2016, 7(2), 21; https://doi.org/10.3390/challe7020021
Received: 8 September 2016
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Revised: 28 October 2016
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Accepted: 21 November 2016
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Published: 29 November 2016
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.
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Keywords:
expert opinions; linear Bayesian updating; spatial classification; transition probabilities
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MDPI and ACS Style
Huang, X.; Wang, Z. A Linear Bayesian Updating Model for Probabilistic Spatial Classification. Challenges 2016, 7, 21. https://doi.org/10.3390/challe7020021
AMA Style
Huang X, Wang Z. A Linear Bayesian Updating Model for Probabilistic Spatial Classification. Challenges. 2016; 7(2):21. https://doi.org/10.3390/challe7020021
Chicago/Turabian StyleHuang, Xiang, and Zhizhong Wang. 2016. "A Linear Bayesian Updating Model for Probabilistic Spatial Classification" Challenges 7, no. 2: 21. https://doi.org/10.3390/challe7020021
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