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- W4299491246 abstract "Large deviation theory is a branch of probability theory that is devoted to a study of the rate at which empirical estimates of various quantities converge to their true values. The object of study in this paper is the rate at which estimates of the doublet frequencies of a Markov chain over a finite alphabet converge to their true values. In case the Markov process is actually an i.i.d. process, the rate function turns out to be the relative entropy (or Kullback-Leibler divergence) between the true and the estimated probability vectors. This result is a special case of a very general result known as Sanov's theorem and dates back to 1957. Moreover, since the introduction of the method of types by Csisz'{a}r and his co-workers during the 1980s, the proof of this version of Sanov's theorem has been elementary, using some combinatorial arguments. However, when the i.i.d. process is replaced by a Markov process, the available proofs are far more complex. The main objective of this paper is therefore to present a first-principles derivation of the LDP for finite state Markov chains, using only simple combinatorial arguments (e.g. the method of types), thus gathering in one place various arguments and estimates that are scattered over the literature." @default.
- W4299491246 created "2022-10-02" @default.
- W4299491246 creator A5089027398 @default.
- W4299491246 date "2013-09-14" @default.
- W4299491246 modified "2023-10-18" @default.
- W4299491246 title "An Elementary Derivation of the Large Deviation Rate Function for Finite State Markov Chains" @default.
- W4299491246 doi "https://doi.org/10.48550/arxiv.1309.3663" @default.
- W4299491246 hasPublicationYear "2013" @default.
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