The title High Dimensional Probability is an attempt to describe the
many trib- utaries of research on Gaussian processes and probability in
Banach spaces that started in the early 1970's. In each of these fields
it is necessary to consider large classes of stochastic processes under
minimal conditions. There are rewards in re- search of this sort. One
can often gain deep insights, even about familiar processes, by
stripping away details that in hindsight turn out to be extraneous. Many
of the problems that motivated researchers in the 1970's were solved.
But the powerful new tools created for their solution, such as
randomization, isoperimetry, concentration of measure, moment and
exponential inequalities, chaining, series representations and
decoupling turned out to be applicable to other important areas of
probability. They led to significant advances in the study of empirical
processes and other topics in theoretical statistics and to a new ap-
proach to the study of aspects of Levy processes and Markov processes in
general. Papers on these topics as well as on the continuing study of
Gaussian processes and probability in Banach are included in this
volume.