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====Bayesian Networks with geo-data==== | ====Bayesian Networks with geo-data==== | ||
- | gBay is a toolbox that links BNs to spatial (raster or vector) data. For each pixel (or polygon), the values in the input data are used as evidence in the network, and inference is performed to obtain the posterior probability distribution of the target nodes. Then, the posterior distributions of the target nodes are written into a new spatial file. | ||
+ | Bayesian networks (BNs) are a powerful tool to represent complex socio-ecological systems, as they can take into account both qualitative and quantitative data, while the associated uncertainties are explicitly shown and propagated through the network. Furthermore, | ||
- | To run your own network with spatial data, see [[How to use gBay]] | + | gBay is a toolbox that links BNs to spatial |
+ | gBay supports dynamic BNs – running the network over multiple time steps, where the output of one time step is an input to the next, which can be used to model feedback loops. Advanced users can also modify spatial data directly in gBay, which allows them to take into account spatial interactions such as neighbourhood effects. | ||
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+ | **Contents: ** | ||
+ | - [[How to develop a BN]] | ||
+ | - [[How to use gBay]] | ||
+ | - [[Iterative BNs]] | ||
+ | - [[Intermediate processing]] | ||
+ | - [[Examples]] of BNs for ecosystem services | ||
+ | - [[Case studies]] | ||
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+ | {{ :gbay.png? |http:// | ||
+ | [[http:// | ||
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+ | gBay was developed at [[http:// | ||
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+ | When using gBay, please refer to the following publication: | ||
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+ | Stritih, A., Rabe, S. E., Robaina, O., Grêt-Regamey, | ||
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+ | **Contact**: |
start.1544702361.txt.gz · Last modified: 2023/04/21 15:30 (external edit)