Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
By Carmel A. Pollino, Owen Woodberry, Ann E. Nicholson, Kevin B. Korb, Barry T. Hart. Environmental Modelling & Software, 2007
486 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/231ae2d5ddc28536867a2847ba138827493d3690
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
- Rob D'hondt
- Gabriela Medina-Oliva
- Benoît Iung
- Deborah Blackman
- Helen Ross
- Guy Engelen
- A. Guerrero
- Joris Aertsens
In Nature and Landscape Conservation
- David C. Douglas
- Jeffrey F. Bromaghin
- Steven C. Amstrup
- T. Atwood
- K. Rode
- G. Durner
- Bruce G. Marcot
- Anca M. Hanea
Citations in the corpus
- A review of Bayesian belief networks in ecosystem service modelling (2013, background): “Using observable input variables will often increase the perceived reliability of the models (Pollino et al., 2007b; Borsuk et al., 2004).”
- Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas (2012, methodology): “One aspect to be developed is formalizing some methods for the sensibility analysis of a model in order to investigate its robustness according to the problem studied (Pollino, 2007).”
- Forecasting the relative influence of environmental and anthropogenic stressors on polar bears (2016, methodology): “To the extent practicable, we represented uncertainty in the estimation of probabilities associated with individual nodes (Marcot et al. 2006, Marcot 2007, Pollino et al. 2007).”
- Mental models for conservation research and practice (2019, background): “The networks are used to calculate how probable an event is, and can be confirmed or adapted on the basis of empirical observations or external interventions on particular system variables that can be collected over time (e.g., Pollino et al., 2007).”