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--- start [2018/12/20 15:20] – [Bayesian Networks with geo-data] stritiha
+++ start [2023/04/21 15:30] (current) – external edit 127.0.0.1
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 ====Bayesian Networks with geo-data====
 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, relationships between variables in the BN are represented graphically, creating a transparent model structure that can facilitate communication with stakeholders. Because of these advantages, BNs are increasingly used to model land use change and ecosystem services.
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 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.
 **Contents: **
   - [[How to develop a BN]]
   - [[How to use gBay]]
-  - [[Dynamic BNs]]
+  - [[Iterative BNs]]
   - [[Intermediate processing]]
   - [[Examples]] of BNs for ecosystem services
   - [[Case studies]]
+{{ :gbay.png? |http://gbay.ethz.ch}}
+[[http://gbay.ethz.ch]]
+gBay was developed at [[http://www.plus.ethz.ch/|ETH-PLUS]] and is partially supported by the [[http://www.ecopotential-project.eu/|ECOPOTENTIAL project]] (funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 641762).
+When using gBay, please refer to the following publication:
+Stritih, A., Rabe, S. E., Robaina, O., Grêt-Regamey, A. & Celio, E. 2020. An online platform for spatial and iterative modelling with Bayesian Networks. Environmental Modelling and Software, 127. https://doi.org/10.1016/j.envsoft.2020.104658
-gBay was developed at [[http://www.plus.ethz.ch/|ETH-PLUS]] and is partially supported by the [[http://www.ecopotential-project.eu/|ECOPOTENTIAL project]] (funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 641762).
+**Contact**: Ben Black [[bblack@ethz.ch]]