This book provides an easily accessible, computationally-oriented introduction into the numerical solution of stochastic differential equations using computer experiments. It develops in the reader an ability to apply numerical methods solving stochastic differential equations. It also creates an intuitive understanding of the necessary theoretical background. Software containing programs for over 100 problems is available online.
Numerical Solution of SDE Through Computer Experiments
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This is a computer experimental introduction to the numerical solution of stochastic differential equations. A downloadable software software containing programs for over 100 problems is provided at one of the following homepages: http://www.math.uni-frankfurt.de/numerik/kloeden/ http://www.business.uts.edu.au/finance/staff/eckard.html http://www.math.siu.edu/schurz/SOFTWARE/ to enable the reader to develop an intuitive understanding of the issues involved. Applications include stochastic dynamical systems, filtering, parametric estimation and finance modeling. The book is intended for readers without specialist stochastic background who want to apply such numerical methods to stochastic differential equations that arise in their own field. It can also be used as an introductory textbook for upper-level undergraduate or graduate students in engineering, physics and economics.
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Inside This Book
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First Sentence:
In everyday life we meet many situations where randomness plays a crucial role. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
current time step size, order weak schemes, necessary computer time, weak convergence criterion, bilinear order, equidistant step size, shuffle vector, noisy limit cycle, global discretization error, equidistant time steps, pathwise approximation, same sample paths, multiple stochastic integrals, strong scheme, plot loge, subinterval points, discrete time approximations, corrected drift, given stochastic differential equation, absolute error criterion, linear stochastic differential equation, weak approximations, order extrapolation, starting routine, midpoint method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Polar Marsaglia, Repeat Exercise, Central Limit Theorem, Generation of the Euler, Law of Large Numbers New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In everyday life we meet many situations where randomness plays a crucial role. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
current time step size, order weak schemes, necessary computer time, weak convergence criterion, bilinear order, equidistant step size, shuffle vector, noisy limit cycle, global discretization error, equidistant time steps, pathwise approximation, same sample paths, multiple stochastic integrals, strong scheme, plot loge, subinterval points, discrete time approximations, corrected drift, given stochastic differential equation, absolute error criterion, linear stochastic differential equation, weak approximations, order extrapolation, starting routine, midpoint method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Polar Marsaglia, Repeat Exercise, Central Limit Theorem, Generation of the Euler, Law of Large Numbers New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 96
books:
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cites this book:
- Stochastic Differential Equations (Siam-Ams Proceedings Ser .: No 6) by N. Y.) Symposium in Applied Mathematics (1972 New York on 4 pages
- Stochastic Differential Equations 1976: Kyoto: International Symposium Proceedings by Kiyosi Ito on page 69, page 72, and page 75
- Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) by Peter E. Kloeden in Front Matter, and Back Matter
- Theory and Numerics of Differential Equations by James F. Blowey in Back Matter
- Nonstandard Analysis in Practice (Universitext) by Francine Diener in Back Matter
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