Modeling with Ito Stochastic Differential Equations. Mathematical modelling 22.

Allen, Edward D.:

ISBN 10: 1402059523 / ISBN 13: 9781402059520

Edité par Dordrecht, Springer, 2007

A propos de cet article

Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 60 ALL 9781402059520 Sprache: Englisch Gewicht in Gramm: 550.

N° de réf. du vendeur 2504093

Détails bibliographiques

• Titre: Modeling with Ito Stochastic Differential Equations. Mathematical modelling 22.
• Éditeur: Dordrecht, Springer
• Date de parution: 2007
• Langue: anglais
• ISBN à 10 chiffres: 1402059523
• ISBN à 13 chiffres: 9781402059520
• Reliure: Hardcover
• Catalogues du vendeur:
  • SA MATHEMATIK

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

This book explains a procedure for constructing realistic stochastic differential equation models for randomly varying systems in 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.