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Prediction of categorical spatial data via Bayesian updating / Xiang Huang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1426 - 1449 Titre : Prediction of categorical spatial data via Bayesian updating Type de document : Article/Communication Auteurs : Xiang Huang, Auteur ; Zhizhong Wang, Auteur ; Jianhua Guo, Auteur Année de publication : 2016 Article en page(s) : pp 1426 - 1449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse spatiale [Termes IGN] classification dirigée [Termes IGN] mise à jour automatique [Termes IGN] système expert Résumé : (Auteur) This study introduces a transition probability-based Bayesian updating (BU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to provide a spatial categorical variable prediction method which has a solid theoretical foundation and yields relatively higher classification accuracy compared with conventional ones. The basic idea is to first build a linear Bayesian updating (LBU) model that corresponds to an application of Bayes’ theorem. Since the linear opinion pool is intrinsically suboptimal and underconfident, the beta-transformed Bayesian updating (BBU) model is proposed to overcome this limitation. Another type of BU approach, conditional independent Bayesian updating (CIBU), is derived based on conditional independent experts. It is shown that traditional Markovian-type categorical prediction (MCP) is equivalent to a particular CIBU model with specific parameters. As three variants of the BU method, these techniques are illustrated in synthetic and real-world case studies, comparison results with both the LBU and MCP favor the BBU model. Numéro de notice : A2016-310 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1133819 En ligne : http://dx.doi.org/10.1080/13658816.2015.1133819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80910 [article]

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