Definition
A Bayesian network (BN) is a graphical-mathematical construct used to probabilistically model processes which include interdependent variables, decisions affecting those variables, and costs associated with the decisions and states of the variables. BNs are inherently system representations and, as such, are often used to model environmental processes. Because of this, there is a natural connection between certain BNs and GIS. BNs are represented as a directed acyclic graph structure with nodes (representing variables, costs, and decisions) and arcs (directed lines representing conditionally probabilistic dependencies between the nodes). A BN can be used for prediction or analysis of real-world problems and complex natural systems where statistical correlations can be found between variables or approximated using expert opinion....
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References
Ames DP, Neilson BT, Stevens DK, Lall U (2005) Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study. J Hydroinform 7:267–282. IWA Publishing
Borsuk ME, Reckhow KH (2000) Summary description of the Neuse estuary Bayesian ecological response network (Neu-BERN). http://www2.ncsu.edu/ncsu/CIL/WRRI/neuseltm.html. 26 Dec 2001
Haas TC (1998) Modeling waterbody eutrophication with a Bayesian belief network. Working paper, School of Business Administration, University of Wisconsin, Milwaukee
Heckerman D (1997) Bayesian networks for data mining. Data Mining Knowl Discov 1:79–119. MapWindow Open Source Team (2007). MapWindow GIS 4.3 Open Source Software. Accessed 06 Feb 2007 at the MapWindow Website: http://www.mapwindow.org/
Kuikka S, Varis O (1997) Uncertainties of climate change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environ Res 2:109–128
Lee DC, Bradshaw GA (1998) Making monitoring work for managers: thoughts on a conceptual framework for improved monitoring within broad-scale ecosystem management. http://icebmp.gov/spatial/lee_monitor/preface.html (26 Dec 2001)
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco
Shachter R, Peot M (1992) Decision making using probabilistic inference methods. In: Proceedings of the eighth conference on uncertainty in artificial intelligence, Stanford, pp 275–283
Stassopoulou A, Petrou M, Kittler J (1998) Application of a Bayesian network in a GIS based decision making system. Int J Geograph Inf Sci 12(1):23–45
Taylor KJ (2003) Bayesian belief networks: a conceptual approach to assessing risk to habitat. Utah State University, Logan
Varis O, Kuikka S (1996) An influence diagram approach to Baltic salmon management. In: Proceedings of the conference on decision analysis for public policy in Europe, INFORMS decision analysis society, Atlanta
Walker A, Pham B, Maeder A (2004) A Bayesian framework for automated dataset retrieval. In: Geographic information systems. 10th International Multimedia Modelling Conference (MMM), Brisbane, p 138
Walker A, Pham B, Moody M (2005) Spatial Bayesian learning algorithms for geographic information retrieval. In: Proceedings 13th annual ACM international workshop on geographic information systems, Bremen, pp 105–114
Recommended Reading
Ames DP (2002) Bayesian decision networks for watershed management. Utah State University, Logan
Norsys Software Corp (2006) Netica Bayesian belief network software. Acquired from http://www.norsys.com/
Stassopoulou A, Caelli T (2000) Building detection using Bayesian networks. Int J Pattern Recognit Artif Intell 14(6):715–733
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Ames, D.P., Anselmo, A. (2017). Bayesian Network Integration with GIS. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_95
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DOI: https://doi.org/10.1007/978-3-319-17885-1_95
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