COMPACTNESS AND LARGE DEVIATIONS
In a reasonable topological space, large deviation estimates essentially deal
with probabilities of events that are asymptotically (exponentially) small,
and in a certain sense, quantify the rate of these decaying probabilities.
In such estimates, upper bounds for such small probabilities often require
compactness of the ambient space, which is often absent in problems arising in
statistical mechanics (for example, distributions of local times of
Brownian motion in the full space R^d). Motivated by such a problem, we
present a robust theory of “translation-invariant compactification”
of probability measures in R^d. Thanks to an inherent shift-invariance of
the underlying problem, we are able to apply this abstract theory painlessly
and solve a long standing problem in statistical mechanics, the mean-field
This talk is based on joint works with S. R. S. Varadhan (New York), as
well as with Erwin Bolthausen(Zurich)and Wolfgang Koenig (Berlin).