  Uncertainties are abundant in complex systems. Mathematical models for these
systems thus contain random effects or noises. The models are often in the form
of stochastic differential equations, with some parameters to be determined by
observations. The stochastic differential equations may be driven by Brownian
motion, fractional Brownian motion or L\'evy motion.
  After a brief overview of recent advances in estimating parameters in
stochastic differential equations, various numerical algorithms for computing
parameters are implemented. The numerical simulation results are shown to be
consistent with theoretical analysis. Moreover, for fractional Brownian motion
and $\alpha-$stable L\'evy motion, several algorithms are reviewed and
implemented to numerically estimate the Hurst parameter $H$ and characteristic
exponent $\alpha$.
