Fast Variables in Stochastic Population Dynamics

 In this thesis two variants of the fast variable elimination method are developed. They are intuitive, simple to implement and give results which are in very good agreement with those found from numerical simulations. The relative simplicity of the techniques makes them ideal for applying to proble...

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Bibliographic Details
Main Author: Constable, George William Albert
Format: eBook
Language:English
Published: Cham Springer International Publishing 2015, 2015
Edition:1st ed. 2015
Series:Springer Theses, Recognizing Outstanding Ph.D. Research
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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245 0 0 |a Fast Variables in Stochastic Population Dynamics  |h Elektronische Ressource  |c by George William Albert Constable 
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505 0 |a  Introduction -- Technical Background.-  The Conditioning Method.-  The Projection Matrix Method.-  Metapopulation Moran Model Analysis -- Further Developments -- Conclusion 
653 |a Data-driven Science, Modeling and Theory Building 
653 |a Sociophysics 
653 |a Systems biology 
653 |a Econophysics 
653 |a Community & Population Ecology 
653 |a Community ecology, Biotic 
653 |a Biomathematics 
653 |a Biological systems 
653 |a Systems Biology 
653 |a Genetics and Population Dynamics 
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520 |a  In this thesis two variants of the fast variable elimination method are developed. They are intuitive, simple to implement and give results which are in very good agreement with those found from numerical simulations. The relative simplicity of the techniques makes them ideal for applying to problems featuring demographic stochasticity, for experts and non-experts alike.   Within the context of mathematical modelling, fast variable elimination is one of the central tools with which one can simplify a multivariate problem.  When used in the context of of deterministic systems, the theory is quite standard, but when stochastic effects are present, it becomes less straightforward to apply.   While the introductory and background chapters form an excellent primer to the theory of stochastic population dynamics, the techniques developed can be applied to systems exhibiting a separation of timescales in a variety of fields including population genetics, ecology and epidemiology.