Advantages and challenges of Bayesian networks in environmental modelling
By Laura Uusitalo. 2007
1,094 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/3a3ea50d6e5cafc760ca3d3f6094a873a2774c2f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Leonel Lara-Estrada
- Luis Enrique Sucar
- Peng Zeng
- Yongtae Park
- Shuchen Li
- Chao Yuan
- Yang Sun
- Eunchan Lee
In Signal Processing
Citations in the corpus
- Prediction of Slope Stability using Naive Bayes Classifier (2018, background): “This is because NBCs are particularly useful for handling incomplete data (Uusitalo, 2007) and could yield good predictions even with small data sizes (Kontkanen et al., 1997), making them quite suitable for analyses with limited (or incomplete) geotechnical data.”
- The role of expert opinion in environmental modelling (2012, background): “Reviews of probabilistic networks in environmental 22 modelling can be found in Marcot et al. (2006) and Uusitalo (2007), with focus on ecological 23 applications, and Varis and Kuikka (1999), with focus on management applications.”
- Metrics for evaluating performance and uncertainty of Bayesian network models (2012, neutral): “As their popularity increases, it becomes more mportant to ensure rigor in their application to real-world probems (Uusitalo, 2007).”
- Large engineering project risk management using a Bayesian belief network (2009, methodology): “However, with a Bayesian belief network it is possible to apply a feedback loop for risk management (even if a Bayesian belief network has no feedback loop itself (Uusitalo, 2007)), construct a cause–consequence relation visually, and provide conditional probabilistic estimations of risks (Fan & Yu,”
- Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks (2018, background): “Instead of a single, certain value, the output is then the most probable value of the variable of interest with the uncertainty attached [11,14,18].”