Modeling with Itô Stochastic Differential Equations

Dynamical systems with random influences occur throughout the physical, biological, and social sciences. By carefully studying a randomly varying system over a small time interval, a discrete stochastic process model can be constructed. Next, letting the time interval shrink to zero, an Ito stochast...

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Detalhes bibliográficos
Autor principal: Allen, Edward James, 19..-
Formato: Livre numérique
Idioma:Anglais
Publicado em: Dordrecht : Springer Netherlands [20..].
Cham : Springer Nature
Edição:1st ed. 2007.
Colecção:Mathematical Modelling: Theory and Applications 22
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Nota: L'impression du document génère 237 p.
Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Modeling with Itô stochastic differential equations, by E. Allen,..., 2007, Dordrecht, Springer, 1 vol. (XII-228 p.), Mathematical modelling, 978-1-4020-5952-0
• Modeling with Itô Stochastic Differential Equations, Texte imprimé, 9789048174874
• Modeling with Itô Stochastic Differential Equations, Texte imprimé, 9789048112944
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520 |a Dynamical systems with random influences occur throughout the physical, biological, and social sciences. By carefully studying a randomly varying system over a small time interval, a discrete stochastic process model can be constructed. Next, letting the time interval shrink to zero, an Ito stochastic differential equation model for the dynamical system is obtained. This modeling procedure is thoroughly explained and illustrated for randomly varying systems in population biology, chemistry, physics, engineering, and finance. Introductory chapters present the fundamental concepts of random variables, stochastic processes, stochastic integration, and stochastic differential equations. These concepts are explained in a Hilbert space setting which unifies and simplifies the presentation. Computer programs, given throughout the text, are useful in solving representative stochastic problems. Analytical and computational exercises are provided in each chapter that complement the material in the text. Modeling with Itô Stochastic Differential Equations is useful for researchers and graduate students. As a textbook for a graduate course, prerequisites include probability theory, differential equations, intermediate analysis, and some knowledge of scientific programming 
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650 |a Variables aléatoires 
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