Stochastic differential equations (SDE) play an important role in a range of application areas, including biology, physics, chemistry, epidemiology, mechanics, microelectronics, economics, and finance . The book presents many new results on high-order methods for strong sample path approximations and for weak functional approximations, including implicit, predictor-corrector, extra-polation and variance-reduction methods. This chapter is an introduction and survey of numerical solution methods for stochastic differential equations. book series Besides serving as a basic text on such methods, the book offers the reader ready access to a large number of potential research problems in a field that is just beginning to expand rapidly and is widely applicable. Kleden, P.E, Platen, E. Numerical solution of stochastic differential equations. Kloeden, Peter E., Platen, Eckhard. The stochastic Taylor expansion provides the basis for the discrete time numerical methods for differential equations. It assumes of the reader an undergraduate background in mathematical methods typical of engineers and physicists, though many chapters begin with a descriptive summary. The stochastic Taylor expansion provides the basis for the discrete time numerical methods for differential equations. Springer is part of, Stochastic Modelling and Applied Probability, Please be advised Covid-19 shipping restrictions apply. The numerical analysis of stochastic differential equations differs significantly from that of ordinary differential equations due to peculiarities of stochastic calculus. ...you'll find more products in the shopping cart. CYBER DEAL: 50% off all Springer eBooks | Get this offer! It is a simple generalization of the Euler method for ordinary differential equations to stochastic differential equations. (gross), © 2020 Springer Nature Switzerland AG. Not logged in The book presents many new results on high-order methods for strong sample path approximations and for weak functional approximations, including implicit, predictor-corrector, extra-polation and variance-reduction methods. Authors: price for Vietnam This book provides an introduction to stochastic calculus and stochastic differential equations, in both theory and applications, emphasising the numerical methods needed to solve such equations. ISBN 3-540-54062-8 Springer-Verlag Berlin Heidelberg New Yor k ISBN 0-387-54062-8 Springer-Verlag New York Berlin Heidelber g Library of Congress Cataloging-in-Publication Data. This service is more advanced with JavaScript available, Part of the The solutions of SDEs are of a different character compared with the solutions of classical ordinary and partial differential equations in the sense that the solutions of SDEs are stochastic processes. © 2020 Springer Nature Switzerland AG. It assumes of the reader an undergraduate background in mathematical methods typical of engineers and physicists, though many chapters begin with a descriptive summary. "... the authors draw upon their own research and experiences in obviously many disciplines... considerable time has obviously been spent writing this in the simplest language possible. Part of Springer Nature. 185.207.228.65. School of Mathematical Sciences and School of Finance & Economics, https://doi.org/10.1007/978-3-662-12616-5, Probability Theory and Stochastic Processes, Modelling with Stochastic Differential Equations, Applications of Stochastic Differential Equations, Time Discrete Approximation of Deterministic Differential Equations, Introduction to Stochastic Time Discrete Approximation, Selected Applications of Strong Approximations, Explicit and Implicit Weak Approximations, Selected Applications of Weak Approximations. The book presents many new results on high-order methods for strong sample path approximations and for weak functional approximations, including implicit, predictor-corrector, extra-polation and variance-reduction methods. - (Applications of mathematics; 23) "Second corrected printing" - T. p. verso. enable JavaScript in your browser. Not affiliated This was not an easy task... Their exposition stresses clarity, not formality - a very welcome approach." The book is also accessible to others who only require numerical recipes. Thus it is a nontrivial matter to measure the efficiency of a given algorithm for finding numerical solutions. Springer, New York 1991 Springer, New York 1991 Google Scholar The book is also accessible to others who only require numerical recipes. Kloeden, Peter E. Numerical solution of stochastic differential equations/Peter E. Kloeden, Eckhard Platen. In Itô calculus, the Euler–Maruyama method (also called the Euler method) is a method for the approximate numerical solution of a stochastic differential equation (SDE). It is named after Leonhard Euler and Gisiro Maruyama. Please review prior to ordering, ebooks can be used on all reading devices, Institutional customers should get in touch with their account manager, Usually ready to be dispatched within 3 to 5 business days, if in stock. The book presents many new results on high-order methods for strong sample path approximations and for weak functional approximations, including implicit, predictor-corrector, extra-polation and variance-reduction methods. To help the reader to develop an intuitive understanding of the underlying mathematics and hand-on numerical skills, exercises and over 100 PC-Exercises are included.

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