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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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Main Author: Omar, Almbrok Hussin Alsonosi
Format: Thesis
Language:English
Published: 2013
Subjects:
QA1 Mathematics (General)
Online Access:http://eprints.usm.my/45264/1/Almbrok%20Hussin%20Alsonosi%20Omar24.pdf
http://eprints.usm.my/45264/
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spelling my.usm.eprints.45264 http://eprints.usm.my/45264/ Uncertainty Quantification In Population Models 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 NonPeerReviewed application/pdf en http://eprints.usm.my/45264/1/Almbrok%20Hussin%20Alsonosi%20Omar24.pdf Omar, Almbrok Hussin Alsonosi (2013) Uncertainty Quantification In Population Models. Masters thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
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
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
publishDate 2013
url http://eprints.usm.my/45264/1/Almbrok%20Hussin%20Alsonosi%20Omar24.pdf
http://eprints.usm.my/45264/
_version_ 1643711236051828736
score 13.211869

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