In the language of large deviations theory, Sanov's theorem identifies the rate function for large deviations of the empirical measure of a sequence of i.i.d. random variables.
In this lecture, we will look at information-theoretic tools to bound probability of large deviations (and as a consequence concentration inequalities) via ...
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A sweeping generalization of Sanov's theorem was achieved by Donsker and Varadhan. To explain their result, let us set E = EZ to denote the space of sequences x ...
Large deviations theory formalizes the heuristic ideas of concentration of measures and widely generalizes the notion of convergence of probability measures.
It is a consequence of the following rigorous reformulation of Boltzmann's discovery, known as Sanov's Theorem, which expresses the large deviation principle.
A basic result of large deviations theory is Sanov's theorem, which states that the sequence of em- pirical measures of independent and identically ...
Jan 30, 2024 · In this paper, we prove a Sanov-type large deviation principle for the sequence of empirical measures of vectors chosen uniformly at random ...
In this lecture, we will look at information-theoretic tools to bound probability of large deviations and hypothesis testing error via Sanov's Theorem. Before ...
Mar 9, 2018 · Basically, the idea for the upper bound is to use the Markov inequality at the exponential scale, whereas the idea of the lower bound is to tilt ...
Aug 2, 2012 · Abstract:A basic result of large deviations theory is Sanov's theorem, which states that the sequence of empirical measures of independent ...
