Uncertainty Quantification In Population Models
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 informa...
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2013
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my-usm-ep.452642019-08-23T08:11:19Z Uncertainty Quantification In Population Models 2013-07 Omar, Almbrok Hussin Alsonosi QA1 Mathematics (General) 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. 2013-07 Thesis http://eprints.usm.my/45264/ http://eprints.usm.my/45264/1/Almbrok%20Hussin%20Alsonosi%20Omar24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Matematik |
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Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
QA1 Mathematics (General) |
| spellingShingle |
QA1 Mathematics (General) Omar, Almbrok Hussin Alsonosi Uncertainty Quantification In Population Models |
| description |
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. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Omar, Almbrok Hussin Alsonosi |
| author_facet |
Omar, Almbrok Hussin Alsonosi |
| author_sort |
Omar, Almbrok Hussin Alsonosi |
| title |
Uncertainty Quantification In Population Models |
| title_short |
Uncertainty Quantification In Population Models |
| title_full |
Uncertainty Quantification In Population Models |
| title_fullStr |
Uncertainty Quantification In Population Models |
| title_full_unstemmed |
Uncertainty Quantification In Population Models |
| title_sort |
uncertainty quantification in population models |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Matematik |
| publishDate |
2013 |
| url |
http://eprints.usm.my/45264/1/Almbrok%20Hussin%20Alsonosi%20Omar24.pdf |
| _version_ |
1747821479837302784 |