dc.contributor.author |
Koen, H
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|
dc.contributor.author |
De Villiers, JP
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|
dc.contributor.author |
Pavlin, G
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|
dc.contributor.author |
De Waal, A
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dc.contributor.author |
De Oude, P
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dc.contributor.author |
Mignet, F
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|
dc.date.accessioned |
2014-08-25T10:23:31Z |
|
dc.date.available |
2014-08-25T10:23:31Z |
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dc.date.issued |
2014-07 |
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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
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|
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 -
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en_ZA |