9781402015540 - Evolution of Biological Systems in Random Media: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications, Band 18) von Swishchuk, Anatoly; Jia... (7 Ergebnisse)

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Zustand: New. pp. 240 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

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Hardcover. 216 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. 9781402015540 Sprache: Englisch Gewicht in Gramm: 900.

Zustand: New. Containing stochastic non-linear models of biological systems, this book in biomathematics presents results for scalar and vector difference equations in random media with applications to the stochastic biological systems. It also offers an approach to the study of stochastic biological systems in random media such as random evolution approach. Series: Mathematical Modelling: Theory and Applications. Num Pages: 238 pages, biography. BIC Classification: PDE; PS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 14. Weight in Grams: 1140. . 2003. Hardback. . . . . Books ship from the US and Ireland.

Gebunden. Zustand: New. Preface. List of Notations. 1: Random Media. 1.1. Markov Chains. 1.2. Ergodicity and Reducibility of Markov Chains. 1.3. Markov Renewal Processes. 1.4. Semi-Markov Processes. 1.5. Jump Markov Processes. 1.6. Wiener Processes and Diffusion Processes. 1.7. Ma.

Buch. Zustand: Neu. Neuware - The book is devoted to the study of limit theorems and stability of evolving biologieal systems of 'particles' in random environment. Here the term 'particle' is used broadly to include moleculas in the infected individuals considered in epidemie models, species in logistie growth models, age classes of population in demographics models, to name a few. The evolution of these biological systems is usually described by difference or differential equations in a given space X of the following type and dxt/dt = g(Xt, y), here, the vector x describes the state of the considered system, 9 specifies how the system's states are evolved in time (discrete or continuous), and the parameter y describes the change ofthe environment. For example, in the discrete-time logistic growth model or the continuous-time logistic growth model dNt/dt = r(y)Nt(l-Nt/K(y)), N or Nt is the population of the species at time n or t, r(y) is the per capita n birth rate, and K(y) is the carrying capacity of the environment, we naturally have X = R, X == Nn(X == Nt), g(x, y) = r(y)x(l-xl K(y)) , xE X. Note that n t for a predator-prey model and for some epidemie models, we will have that X = 2 3 R and X = R , respectively. In th case of logistic growth models, parameters r(y) and K(y) normaIly depend on some random variable y.