The large deviation principle is usually obtained by transferring Sanov's theorem, which gives the large de- viation principle for the empirical measures of independent and identically distributed samples, through an absolutely continuous change of measure.
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. ...
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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.
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 ...
It is a consequence of the following rigorous reformulation of Boltzmann's discovery, known as Sanov's Theorem, which expresses the large deviation principle.
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 ...