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A framework for inferring predictive distributions of rhino poaching events through causal modelling

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dc.contributor.author Koen, H
dc.contributor.author De Villiers, JP
dc.contributor.author Pavlin, G
dc.contributor.author De Waal, A
dc.contributor.author De Oude, P
dc.contributor.author Mignet, F
dc.date.accessioned 2014-08-25T10:23:31Z
dc.date.available 2014-08-25T10:23:31Z
dc.date.issued 2014-07
dc.identifier.citation Koen, H, De Villiers, J.P, Pavlin, G, De Waal, A, De Oude, P and Mignet, F. 2014. A framework for inferring predictive distributions of rhino poaching events through causal modelling. In: FUSION 2014, Salamanca, Spain, 7-10 July 2014 en_US
dc.identifier.uri http://hdl.handle.net/10204/7631
dc.description FUSION 2014, Salamanca, Spain, 7-10 July 2014 en_US
dc.description.abstract Rhino poaching in South Africa is leading to a catastrophic reduction in the rhino population. In this paper a Bayesian network causal model is proposed to model the underlying (causal) relationships that lead to rhino poaching events. The model may be used to fuse a collection of heterogeneous information sources. If a game reserve is partitioned into several geographical areas or cells, the model may perform inference for each of these cells separately, and give a relative predictive distribution of poaching events over the game reserve. After an overview of the current problem definition and a brief overview of similar modelling approaches, the Bayesian network model is presented. The developed Bayesian network based model is an initial attempt at proposing a sensible modelling approach for this problem. Some of the complexities of the approach are discussed, before considering how the model may be validated at a later stage. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Workflow;13073
dc.subject Rhino poaching en_US
dc.subject Modelling approaches en_US
dc.subject Bayesian network models en_US
dc.subject Catastrophic reduction en_US
dc.title A framework for inferring predictive distributions of rhino poaching events through causal modelling en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Koen, H., De Villiers, J., Pavlin, G., De Waal, A., De Oude, P., & Mignet, F. (2014). A framework for inferring predictive distributions of rhino poaching events through causal modelling. http://hdl.handle.net/10204/7631 en_ZA
dc.identifier.chicagocitation Koen, H, JP De Villiers, G Pavlin, A De Waal, P De Oude, and F Mignet. "A framework for inferring predictive distributions of rhino poaching events through causal modelling." (2014): http://hdl.handle.net/10204/7631 en_ZA
dc.identifier.vancouvercitation Koen H, De Villiers J, Pavlin G, De Waal A, De Oude P, Mignet F, A framework for inferring predictive distributions of rhino poaching events through causal modelling; 2014. http://hdl.handle.net/10204/7631 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Koen, H AU - De Villiers, JP AU - Pavlin, G AU - De Waal, A AU - De Oude, P AU - Mignet, F AB - Rhino poaching in South Africa is leading to a catastrophic reduction in the rhino population. In this paper a Bayesian network causal model is proposed to model the underlying (causal) relationships that lead to rhino poaching events. The model may be used to fuse a collection of heterogeneous information sources. If a game reserve is partitioned into several geographical areas or cells, the model may perform inference for each of these cells separately, and give a relative predictive distribution of poaching events over the game reserve. After an overview of the current problem definition and a brief overview of similar modelling approaches, the Bayesian network model is presented. The developed Bayesian network based model is an initial attempt at proposing a sensible modelling approach for this problem. Some of the complexities of the approach are discussed, before considering how the model may be validated at a later stage. DA - 2014-07 DB - ResearchSpace DP - CSIR KW - Rhino poaching KW - Modelling approaches KW - Bayesian network models KW - Catastrophic reduction LK - https://researchspace.csir.co.za PY - 2014 T1 - A framework for inferring predictive distributions of rhino poaching events through causal modelling TI - A framework for inferring predictive distributions of rhino poaching events through causal modelling UR - http://hdl.handle.net/10204/7631 ER - en_ZA


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