Uncertainty Quantification In Population Models

dc.contributor.author	Alsonosi Omar, Almbrok Hussin
dc.date.accessioned	2019-08-20T06:53:53Z
dc.date.available	2019-08-20T06:53:53Z
dc.date.issued	2013-07
dc.description.abstract	Uncertainty in general can be in the form of numeric or non-numeric, where the latter is qualitative and the former quantitative in nature. In numerical quantities, uncertainty can be random in nature, in which case probability theory is appropriate, or it can be as a result of unclear information, whereby fuzzy set theory is useful. Our concern will be on uncertainty in population models described by differential equations and solved numerically. We select the predator-prey model and susceptible- infected-recovered epidemic model to explore the uncertainty in the population models through the initial states. For randomness, the normal distribution is selected to intro- duce the uncertainty in the predator-prey model while we use the Beta distribution to insert the uncertainty in the epidemic model. For the fuzzy approach, we consider a triangular fuzzy number to treat the lack of information in the both models.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/8645
dc.language.iso	en	en_US
dc.publisher	Universiti Sains Malaysia	en_US
dc.subject	Quantification	en_US
dc.subject	Population Models	en_US
dc.title	Uncertainty Quantification In Population Models	en_US
dc.type	Thesis	en_US

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