MATH 286: STOCHASTIC DIFFERENTIAL EQUATIONS (FALL 1998)
Professor R. J. Williams
Lecture time: MW, 4.40-6 p.m., AP&M 6218.
First class meeting: Friday, September 25, 1998.
Stochastic differential equations arise in modelling a variety of
random dynamic phenomena in the physical, biological,
engineering and social sciences. Solutions of these
equations are often diffusion processes and hence are connected to the
subject of partial differential equations.
This course will present the basic theory of stochastic differential equations
and provide examples of its application.
1. A review of the relevant stochastic process and martingale theory.
2. Stochastic calculus including Ito's formula.
3. Existence and uniqueness for stochastic differential equations, strong
These will complement the theoretical material presented in the course.
Students may wish
to enrol in Math 161 in the Fall of 1998, which will include an Introduction to Mathematica.
Chung, K. L., and Williams, R. J., Introduction to Stochastic
Integration, Second Edition, Birkhauser, 1990.
Karatzas, I. and Shreve, S., Brownian motion and stochastic
calculus, 2nd edition, Springer.
Oksendal, B., Stochastic Differential Equations,
Springer, 4th edition.
Protter, P., Stochastic Integration and Differential
Rogers, L. C. G., and Williams, D., Diffusions, Markov Processes,
and Martingales, Wiley, Volume 1: 1994, Volume 2: 1987.
Jacod, J., and Shiryaev, A. N., Limit theorems for
stochastic processes, Springer-Verlag, 1987.
Numerical solution of SDEs
Talay, D., and Tubaro, L., Expansion of the global error
for numerical schemes solving stochastic differential equations,
Stochastic Analysis and Applications, 8 (1990), 94-120.
Kloeden, P. E., and Platen, E., Numerical
solution of stochastic differential equations,
Kloeden, P. E., Platen, E., and Schurz, H.,
Numerical solution of SDEs through computer experiments,
Bouleau, N., and Lepingle, D., Numerical Methods for Stochastic
Processes, Wiley, 1994.
Gaines, J. G., and Lyons, T. J., Variable step
size control in the numerical solution of
stochastic differential equations, SIAM J. Applied
Math., to appear.
Calculus Software developed by Wilfrid Kendall, University of Warwick. This
runs under Mathematica for example.
C programs for the numerical solution of
stochastic differential equations,
provided by Jessica Gaines, Edinburgh University, U.K.
Math 280AB (Probability) or
equivalent, or consent of instructor.
Please direct any questions to
Professor Ruth J. Williams, email: