Probabilistic models of population evolution : scaling limits, genealogies and interactions
Language: English Series: Mathematical biosciences institute lecture series; 1.6 [v.1: Stochastics in biological systems] | / edited by Michael Reed and Richard DurrettPublication details: Switzerland Springer 2016Description: viii,125pISBN:- 9783319303260
- 519.234 P214p
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PK Kelkar Library, IIT Kanpur | General Stacks | 519.234 P214p (Browse shelf(Opens below)) | Available | A183712 |
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519.233 W488pE Limit theorems on large deviations for Markov stochastic processes | 519.234 Al53s Stochastic population and epidemic models | 519.234 At42 Branching processes | 519.234 P214p Probabilistic models of population evolution | 519.24 D34e Extreme value theory | 519.24 H793r Regional frequency analysis | 519.24 IN8b An introduction to stein's method |
This expository book presents the mathematical description of evolutionary models of populations subject to interactions (e.g. competition) within the population. The author includes both models of finite populations, and limiting models as the size of the population tends to infinity. The size of the population is described as a random function of time and of the initial population (the ancestors at time 0). The genealogical tree of such a population is given. Most models imply that the population is bound to go extinct in finite time. It is explained when the interaction is strong enough so that the extinction time remains finite, when the ancestral population at time 0 goes to infinity. The material could be used for teaching stochastic processes, together with their applications. Etienne Pardoux is Professor at Aix-Marseille University, working in the field of Stochastic Analysis, stochastic partial differential equations, and probabilistic models in evolutionary biology and population genetics. He obtained his PhD in 1975 at University of Paris-Sud
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