Abstract
Background. One of the best known results in information theory says that a data sequence x 1,x 2,...,x n produced by independent random draws from a fixed distribution P over a discrete domain can be compressed into a binary sequence, or code whose expected length is at most nH(P)+1 bits, where H(P) = − ∑ i P i logP i is the entropy of P. It is also known that this compression is near optimal as nH(P) is the smallest achievable expected number of code bits.
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Freund, Y., Orlitsky, A., Santhanam, P., Zhang, J. (2003). Universal Coding of Zipf Distributions. In: Schölkopf, B., Warmuth, M.K. (eds) Learning Theory and Kernel Machines. Lecture Notes in Computer Science(), vol 2777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45167-9_57
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DOI: https://doi.org/10.1007/978-3-540-45167-9_57
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