  The paper studies stochastic integration with respect to Gaussian processes
and fields. It is more convenient to work with a field than a process: by
definition, a field is a collection of stochastic integrals for a class of
deterministic integrands. The problem is then to extend the definition to
random integrands. An orthogonal decomposition of the chaos space of the random
field, combined with the Wick product, leads to the \Ito-Skorokhod integral,
and provides an efficient tool to study the integral, both analytically and
numerically. For a Gaussian process, a natural definition of the integral
follows from a canonical correspondence between random processes and a special
class of random fields. Some examples of the corresponding stochastic
differential equations are also considered.
