# On empirical cumulant generating functions of code lengths for individual sequences

@article{Merhav2017OnEC, title={On empirical cumulant generating functions of code lengths for individual sequences}, author={Neri Merhav}, journal={2017 IEEE International Symposium on Information Theory (ISIT)}, year={2017}, pages={1500-1504} }

We consider the problem of lossless compression of individual sequences using finite-state (FS) machines, from the perspective of the best achievable empirical cumulant generating function (CGF) of the code length, i.e., the normalized logarithm of the empirical average of the exponentiated code length. Since the probabilistic CGF is minimized in terms of the Renyi entropy of the source, one of the motivations of this study is to derive an individual-sequence analogue of the Renyi entropy, in…

## 2 Citations

Improved Bounds on Lossless Source Coding and Guessing Moments via Rényi Measures

- Computer ScienceIEEE Transactions on Information Theory
- 2018

Upper and lower bounds on the optimal guessing moments of a random variable taking values on a finite set when side information may be available are provided, similar to Arikan’s bounds, but expressed in terms of the Arimoto-Rényi conditional entropy.

Fading Channel Coding Based on Entropy and Compressive Sensing

- Computer Science2020 3rd World Symposium on Communication Engineering (WSCE)
- 2020

Channel code length is investigated under Rayleigh and Rician fading assumptions along with additive noise consideration and the inverse problem of identifying the corresponding distributions from the derived channel code lengths and Compressive Sensing based number of samples is addressed.

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