Brownian Occupation Measures, Compactness and Large Deviations
Chiranjib Mukherjee, S. R. S. Varadhan

TL;DR
This paper develops a new compactification of the space of probability measures modulo translation invariance, enabling large deviation principles for occupation measures of Brownian motion without exponential tightness.
Contribution
It introduces a novel compactification of the orbit space of probability measures under translation, facilitating large deviation analysis for non-positively recurrent processes.
Findings
Established a large deviation principle on the new compactified space.
Provided a compactification that respects translation invariance.
Applied the theory to occupation measures of Brownian motion.
Abstract
In proving large deviation estimates, the lower bound for open sets and upper bound for compact sets are essentially local estimates. On the other hand, the upper bound for closed sets is global and compactness of space or an exponential tightness estimate is needed to establish it. In dealing with the occupation measure of the dimensional Brownian motion, which is not positive recurrent, there is no possibility of exponential tightness. The space of probability distributions can be compactified by replacing the usual topology of weak convergence by the vague toplogy, where the space is treated as the dual of continuous functions with compact support. This is essentially the one point compactification of by adding a point at that results in the compactification of by allowing…
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