Mathematicians often face the question to which extent mathematical
models describe processes of the real world. These models are derived
from experimental data, hence they describe real phenomena only
approximately. Thus a mathematical approach must begin with choosing
properties which are not very sensitive to small changes in the model,
and so may be viewed as properties of the real process. In particular,
this concerns real processes which can be described by means of ordinary
differential equations. By this reason different notions of stability
played an important role in the qualitative theory of ordinary
differential equations commonly known nowdays as the theory of dynamical
systems. Since physical processes are usually affected by an enormous
number of small external fluctuations whose resulting action would be
natural to consider as random, the stability of dynamical systems with
respect to random perturbations comes into the picture. There are
differences between the study of stability properties of single
trajectories, i. e., the Lyapunov stability, and the global stability of
dynamical systems. The stochastic Lyapunov stability was dealt with in
Hasminskii [Has]. In this book we are concerned mainly with questions
of global stability in the presence of noise which can be described as
recovering parameters of dynamical systems from the study of their
random perturbations. The parameters which is possible to obtain in this
way can be considered as stable under random perturbations, and so
having physical sense. -1- Our set up is the following.