Numerical Solution of Stochastic Differential EquationsThe numerical analysis of stochastic differential equations (SDEs) differs significantly from that of ordinary differential equations. This book provides an easily accessible introduction to SDEs, their applications and the numerical methods to solve such equations. From the reviews: "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." --ZAMP |
Other editions - View all
Numerical Solution of Stochastic Differential Equations Peter E. Kloeden,Eckhard Platen Limited preview - 2011 |
Numerical Solution of Stochastic Differential Equations Peter E. Kloeden,Eckhard Platen Limited preview - 2013 |
Numerical Solution of Stochastic Differential Equations Peter E. Kloeden,Eckhard Platen No preview available - 2010 |
Common terms and phrases
1-dimensional 2.0 strong Taylor a₁ additive noise AW)² Chapter component defined density derivatives deterministic diffusion coefficients discrete approximation discretization error dW₁ dX₁ estimate Euler approximation Euler method Euler scheme Exercise implicit order initial value Ito formula Ito process Ito stochastic Ito-Taylor expansion j₁ Ld(Delta Lemma linear Lyapunov exponent Markov chain matrix Milstein scheme multi-dimensional multi-indices multiple Ito integrals multiple Stratonovich integrals obtain order 1.5 strong order 2.0 weak order ẞ ordinary differential equation parameter PC-Exercise probability random variables Repeat PC-Exercise Results of PC-Exercise sample paths satisfies scalar Section simulations solution standard Wiener process step stochastic differential equation stochastic integrals stochastic process Stratonovich integrals Stratonovich-Taylor strong convergence strong scheme strong Taylor scheme Theorem Tn+1 variance vector W₁ weak scheme weak Taylor scheme Wiener process X₁ Y₂ Yn+1