Book contents
- Invasive Species
- Invasive Species
- Copyright page
- Contents
- Contributors
- Foreword
- 1 The Allocation of Inspection Resources
- 2 Tools for Designing and Evaluating Post-Border Surveillance Systems
- 3 Control Charts for Biosecurity Monitoring and Surveillance
- 4 Open-Source Intelligence Gathering and Open-Analysis Intelligence for Biosecurity
- 5 Predicting Distributions of Invasive Species
- 6 Mapping Risks and Impacts of Invasive Alien Species with Dynamic Simulation Models
- 7 Models for Understanding Disease Dynamics
- 8 Bayesian Networks for Import Risk Assessment
- 9 Getting the Message Right: Tools for Improving Biosecurity Risk Communication
- 10 Cost–Benefit Analysis for Biosecurity Decisions
- 11 Valuing Protection against Invasive Species Using Contingent Valuation
- 12 Management of Invasive Species: Info-Gap Perspectives
- 13 Decisions with Relative Robustness
- 14 Optimising Resource Allocation
- 15 Value of Information Analysis as a Decision Support Tool for Biosecurity
- 16 Declaring Eradication of an Invasive Species
- 17 Surveillance for Detection of Pests and Diseases: How Sure Can We Be of Their Absence?
- 18 Some Questions to Ask Yourself
- Index
- References
8 - Bayesian Networks for Import Risk Assessment
Published online by Cambridge University Press: 03 July 2017
Book contents
- Invasive Species
- Invasive Species
- Copyright page
- Contents
- Contributors
- Foreword
- 1 The Allocation of Inspection Resources
- 2 Tools for Designing and Evaluating Post-Border Surveillance Systems
- 3 Control Charts for Biosecurity Monitoring and Surveillance
- 4 Open-Source Intelligence Gathering and Open-Analysis Intelligence for Biosecurity
- 5 Predicting Distributions of Invasive Species
- 6 Mapping Risks and Impacts of Invasive Alien Species with Dynamic Simulation Models
- 7 Models for Understanding Disease Dynamics
- 8 Bayesian Networks for Import Risk Assessment
- 9 Getting the Message Right: Tools for Improving Biosecurity Risk Communication
- 10 Cost–Benefit Analysis for Biosecurity Decisions
- 11 Valuing Protection against Invasive Species Using Contingent Valuation
- 12 Management of Invasive Species: Info-Gap Perspectives
- 13 Decisions with Relative Robustness
- 14 Optimising Resource Allocation
- 15 Value of Information Analysis as a Decision Support Tool for Biosecurity
- 16 Declaring Eradication of an Invasive Species
- 17 Surveillance for Detection of Pests and Diseases: How Sure Can We Be of Their Absence?
- 18 Some Questions to Ask Yourself
- Index
- References
- Type
- Chapter
- Information
- Invasive SpeciesRisk Assessment and Management, pp. 181 - 205Publisher: Cambridge University PressPrint publication year: 2017
References
Baker, J. & Stuckey, M. (2009). Prioritising the impact of exotic pest threats using Bayes Net and MCDA methods. Melbourne, Australia: Australian Centre of Excellence for Risk Analysis, The University of Melbourne. Available from www.acera.unimelb.edu.au/materials/endorsed/0707_Final_Report.pdfGoogle Scholar
Baker, R. H. A., Battisti, A., Bremmer, J., et al. (2009). PRATIQUE: A research project to enhance pest risk analysis techniques in the European Union. EPPO Bulletin, 39(1), 87–93.CrossRefGoogle Scholar
Biosecurity Australia (2008). Final import risk analysis report for fresh mango fruit from India. Technical report. Canberra, Australia: Biosecurity Australia. Available from www.agriculture.gov.au/SiteCollectionDocuments/ba/plant/ungroupeddocs/Final_IRA_-_Mangoes_from_India.pdfGoogle Scholar
Boneh, T. (2010). Ontology and Bayesian decision networks for supporting the meteorological forecasting process. Ph.D. thesis, Monash University, 455 pp.Google Scholar
Borsuk, M. E, Stow, C. A. & Reckhow, K. H. (2004). A Bayesian network of eutrophication models for synthesis, prediction and uncertainty analysis. Ecological Modelling, 173(2–3), 219–239.CrossRefGoogle Scholar
Burgman, M., Colyvan, M., Christian, R., et al. (2011). Demonstrating risk analysis capabilities. ACERA Project 0901. Melbourne, Australia: Australian Centre of Excellence for Risk Analysis, The University of Melbourne. Available from http://cebra.unimelb.edu.au/__data/assets/pdf_file/0008/1290491/0901_final-report.pdfGoogle Scholar
Burgman, M. A., Wintle, B. A., Thompson, C. A., et al. (2010). Reconciling uncertain costs and benefits in Bayes nets for invasive species management. Risk Analysis, 30(2), 277–284.Google Scholar
Coupé, V. M. H., van der Gaag, L. C. & Habbema, J. D. F. (2000). Sensitivity analysis: An aid for belief-network quantification. Knowledge Engineering Review, 15(3), 1–18.Google Scholar
Dambacher, J. M., Shenton, W., Hayes, K. R., Hart, B. T. & Barry, S. (2008). Qualitative modelling and Bayesian network analysis for risk-based biosecurity decision making in complex systems. Melbourne, Australia: Australian Centre of Excellence for Risk Analysis, The University of Melbourne. Available from www.acera.unimelb.edu.au/materials/endorsed/0601.pdfGoogle Scholar
Druzdel, M. J. & Van Der Gaag, L. C. (2000). Building probabilistic networks: “Where do the numbers come from?” IEEE Transactions on Knowledge and Data Engineering, 12(4), 481–486.CrossRefGoogle Scholar
Holt, J., Leach, A. W., Knight, J. D., et al. (2012). Tools for visualizing and integrating pest risk assessment ratings and uncertainties. EPPO Bulletin, 42(1), 35–41.Google Scholar
Hood, G. (2009). Risk of introduction to Australia of giant African snail via shipping containers. Canberra, Australia: Bureau of Rural Sciences.Google Scholar
Hood, G. M., Barry, S. C. & Martin, P. A. J. (2009). Alternative methods for computing the sensitivity of complex surveillance systems. Risk Analysis, 29(12), 1686–1698.Google Scholar
Horton, B. J., Evans, D. L., James, P. J. & Campbell, N. J. (2009). Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing. Animal Production Science, 49(1), 48–55.Google Scholar
Hosack, G. R., Hayes, K. R. & Dambacher, J. M. (2008). Assessing model structure uncertainty through an analysis of system feedback and Bayesian networks. Ecological Applications, 18(4), 1070–1082.Google Scholar
Howard, R. & Matheson, J. (1981). Influence diagrams. In R. Howard and J. Matheson (eds.), Readings in Decision Analysis (pp. 763–771). Menlo Park, CA: Strategic Decisions Group.Google Scholar
Korb, K. B. & Nicholson, A. E. (2011). Bayesian artificial intelligence, 2nd ed. Boca Raton, FL: CRC/Chapman Hall.Google Scholar
Kuhnert, P. M. & Hayes, K. R. (2009). How believable is your BBN? In 18th World IMACS/MODSIM Congress, Cairns, Australia.Google Scholar
Laskey, K. B. & Mahoney, S. M. (2000). Network engineering for agile belief network models. IEEE Transactions on Knowledge and Data Engineering, 12(4), 487–498.CrossRefGoogle Scholar
Martin, T. G., Burgman, M. A., Fidler, F., et al. (2012). Eliciting expert knowledge in conservation science. Conservation Biology, 26, 29–38.Google Scholar
Mengersen, K., Quinlan, M. M., Whittle, P. J. L., et al. (2012). Beyond compliance: Project on an integrated systems approach for pest risk management in South East Asia. EPPO Bulletin, 42(1), 109–116.Google Scholar
Murphy, B., Jansen, C., Murray, J. & De Barro, P. (2010). Risk analysis on the Australian release of Aedes aegypti (L.) (Diptera: Culicidae) containing Wolbachia. Indooroopilly, Australia: CSIRO Entomology.Google Scholar
Norsys (2012). Netica. Vancouver, Canada: Norsys Software Corp. Available from www.norsys.com/Google Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.Google Scholar
Peterson, D. P., Rieman, B. E., Dunham, J. B., Fausch, K. D. & Young, M. K. (2008). Analysis of trade-offs between threats of invasion by nonnative brook trout (Salvelinus fontinalis) and intentional isolation for native westslope cutthroat trout (Oncorhynchus clarkii lewisi). Canadian Journal of Fisheries and Aquatic Sciences, 65(4), 557–573.CrossRefGoogle Scholar
Renken, H. & Mumby, P. J. (2009). Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach. Ecological Modelling, 220(9–10), 1305–1314.CrossRefGoogle Scholar
Russell, S. J. & Norvig, P. (2010). Artificial intelligence: A modern approach, 3rd ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Speirs-Bridge, A., Fidler, F., McBride, M., Flander, L., Cumming, G., & Burgman, M. (2010). Reducing overconfidence in the interval judgments of experts. Risk Analysis, 30, 512–523.CrossRefGoogle Scholar
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3–4), 312–318.Google Scholar
Varis, O. & Kuikka, S. (1999). Learning Bayesian decision analysis by doing: Lessons from environmental and natural resources management. Ecological Modelling, 119(2–3), 177–195.Google Scholar
Walshe, T. & Burgman, M. (2010). A framework for assessing and managing risks posed by emerging diseases. Risk Analysis, 30(2), 236–249.Google Scholar
Wintle, B. C. & Nicholson, A. E. (2014). Exploring risk judgments in a trade dispute using Bayesian networks. Risk Analysis, 34(6), 1095–1111.Google Scholar