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100%: Anatoly Swishchuk, Jianhong Wu: Evolution of Biological Systems in Random Media: Limit Theorems and Stability: Volume 18 (Mathematical Modelling: Theory and Applications) (ISBN: 9789401715065) 2013, Springer, Erstausgabe, in Englisch, Band: 18, auch als eBook.Nur diese Ausgabe anzeigen…
82%: Anatoly V. Swishchuk; Jianhong Wu; A. V. Svishchuk: Evolution of Biological Systems in Random Media: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications) (ISBN: 9781402015540) 2003, Springer, Niederlande, 2003. Ausgabe, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
![{ EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) } By ( Author ) [ Dec - 2010 ] [ Paperback ]](/c-9789048163984-0.jpg){kind=small}
65%: Anatoly V. Swishchuk, Jianhong Wu: { EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) } By ( Author ) [ Dec - 2010 ] [ Paperback ] (ISBN: 9789048163984) Springer, in Englisch, Band: 18, Taschenbuch.Nur diese Ausgabe anzeigen…
Evolution of Biological Systems in Random Media: Limit Theorems and Stability: Volume 18 (Mathematical Modelling: Theory and Applications)
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Evolution of Biological Systems in Random Media: Limit Theorems and Stability
EN NWISBN: 9781402015540 bzw. 1402015542, in Englisch, Springer Verlag GmbH, neu.
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.
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Evolution of Biological Systems in Random Media, Limit Theorems and Stability (2010)
NL PB NWISBN: 9789048163984 bzw. 9048163986, in Holländisch, Springer, Taschenbuch, neu.
Lieferung aus: Niederlande, 3-4 weken.
bol.com.
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 d... 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. Productinformatie:Taal: Engels;Afmetingen: 12x235x155 mm;Gewicht: 373,00 gram;ISBN10: 9048163986;ISBN13: 9789048163984; Engels | Paperback | 2010.
bol.com.
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 d... 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. Productinformatie:Taal: Engels;Afmetingen: 12x235x155 mm;Gewicht: 373,00 gram;ISBN10: 9048163986;ISBN13: 9789048163984; Engels | Paperback | 2010.
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Symbolbild
Evolution of Biological Systems in Random Media: Limit Theorems and Stability
EN NWISBN: 9781402015540 bzw. 1402015542, in Englisch, Springer, Niederlande, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten, Versandfertig in 1 - 2 Wochen.
Evolution of Biological Systems in Random Media: Limit Theorems and Stability, 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.
Evolution of Biological Systems in Random Media: Limit Theorems and Stability, 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.
4
Symbolbild
Evolution of Biological Systems in Random Media: Limit Theorems and Stability
EN NWISBN: 9781402015540 bzw. 1402015542, in Englisch, Springer, Niederlande, neu.
Lieferung aus: Schweiz, zzgl. Versandkosten, Versandfertig innert 3 Wochen.
Evolution of Biological Systems in Random Media: Limit Theorems and Stability, 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.
Evolution of Biological Systems in Random Media: Limit Theorems and Stability, 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.
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![9789048163984 - Anatoly Swishchuk: { [ EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) ] } By (Author) Dec-07-2010 [ Paperback ]](/c-9789048163984-3.jpg "9789048163984 - Anatoly Swishchuk: { [ EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) ] } By (Author) Dec-07-2010 [ Paperback ]")
{ [ EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) ] } By (Author) Dec-07-2010 [ Paperback ] (2010)
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Evolution of Biological Systems in Random Media: Limit Theorems and Stability: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications): Volume 18 (2013)
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Evolution of Biological Systems in Random Media: Limit Theorems and Stability: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications): Volume 18 (2013)
EN PB NWISBN: 9789048163984 bzw. 9048163986, Band: 18, in Englisch, 240 Seiten, Springer, Taschenbuch, neu.
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Evolution of Biological Systems in Random Media: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications) (2003)
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Evolution of Biological Systems in Random Media: Limit Theorems and Stability (Mathematical Modelling: Theory and Applications) (2003)
EN HC USISBN: 9781402015540 bzw. 1402015542, in Englisch, 218 Seiten, 2003. Ausgabe, Springer, gebundenes Buch, gebraucht.
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![9789048163984 - Anatoly Swishchuk: { EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) } By ( Author ) [ Dec - 2010 ] [ Paperback ]](/c-9789048163984-1.jpg "9789048163984 - Anatoly Swishchuk: { EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) } By ( Author ) [ Dec - 2010 ] [ Paperback ]")
{ EVOLUTION OF BIOLOGICAL SYSTEMS IN RANDOM MEDIA: LIMIT THEOREMS AND STABILITY (SOFTCOVER REPRINT OF THE ORIGI) (MATHEMATICAL MODELLING: THEORY AND APPLICATIONS #18) } By ( Author ) [ Dec - 2010 ] [ Paperback ] (2010)
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Lieferung aus: Deutschland, Versandfertig in 1 - 2 Werktagen.
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Taschenbuch, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2010-12-07, Studio: Springer.
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Taschenbuch, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2010-12-07, Studio: Springer.
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