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The entropic region is formed by the collection of the Shannon entropies of all subvectors of finitely many jointly distributed discrete random variables. For four or more variables, the structure of the entropic region is mostly unknown.…

Information Theory · Computer Science 2026-03-04 E. P. Csirmaz , L. Csirmaz

In this paper, we investigate the asymptotic stability of finite-dimensional stochastic integrable Hamiltonian systems via information entropy. Specifically, we establish the asymptotic vanishing of Shannon entropy difference (with…

Dynamical Systems · Mathematics 2025-10-28 Chen Wang , Yong Li

We determine the phase diagram of DNA with inter- and intra-strand native-pair interactions that mimic the compaction of DNA. We show that DNA takes an overall sheet-like structure in the region where an incipient transition to a compact…

Soft Condensed Matter · Physics 2024-04-15 Garima Mishra , Somendra M. Bhattacharjee

The Shannon Noiseless coding theorem (the data-compression principle) asserts that for an information source with an alphabet $\mathcal X=\{0,\ldots ,\ell -1\}$ and an asymptotic equipartition property, one can reduce the number of stored…

Information Theory · Computer Science 2016-04-26 Yuri Suhov , Izabella Stuhl

We investigate Eigen's model for the evolution of the genetic code of microorganisms using a novel method based on population dynamics analysis. This model, for a given number of offspring, determines long-term survival as a function of the…

Populations and Evolution · Quantitative Biology 2024-06-21 Hermano Velten , Carlos Felipe Pinheiro , Alcides Castro e Silva

Shannon and Renyi entropies are quantitative measures of uncertainty in a data set. They are developed by Renyi in the context of entropy theory. These measures have been studied in the case of the multivariate t-distributions. We extend…

Statistics Theory · Mathematics 2019-01-31 Salah H. Abid , Uday J. Quaez

We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression values and protein interaction networks. The entropy measure estimates the parameter…

Molecular Networks · Quantitative Biology 2013-05-24 G. Menichetti , G. Bianconi , E. Giampieri , G. Castellani , D. Remondini

Information theory provides tools to predict the performance of a learning algorithm on a given dataset. For instance, the accuracy of learning an unknown parameter can be upper bounded by reducing the learning task to hypothesis testing…

Quantum Physics · Physics 2026-04-21 Evan Peters

Measuring the complexity of tree structures can be beneficial in areas that use tree data structures for storage, communication, and processing purposes. This complexity can then be used to compress tree data structures to their…

Information Theory · Computer Science 2023-09-19 Amirmohammad Farzaneh , Mihai-Alin Badiu , Justin P. Coon

We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is identical to its guessing number and can be bounded from below…

Combinatorics · Mathematics 2007-11-28 Soren Riis

The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair Entropy-Statistical Complexity for a large class…

We prove a variety of new and refined uniform continuity bounds for entropies of both classical random variables on an infinite state space and of quantum states of infinite-dimensional systems. We obtain the first tight continuity estimate…

Quantum Physics · Physics 2024-11-20 Simon Becker , Nilanjana Datta , Michael G. Jabbour

We discuss the entropy of a circular polymer under a topological constraint. We call it the {\it topological entropy} of the polymer, in short. A ring polymer does not change its topology (knot type) under any thermal fluctuations. Through…

Statistical Mechanics · Physics 2009-11-07 Miyuki K. Shimamura , Tetso Deguchi

Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their samples,…

Machine Learning · Computer Science 2023-05-16 Gecia Bravo-Hermsdorff , Róbert Busa-Fekete , Mohammad Ghavamzadeh , Andres Muñoz Medina , Umar Syed

Tandem duplication is the process of inserting a copy of a segment of DNA adjacent to the original position. Motivated by applications that store data in living organisms, Jain et al. (2017) proposed the study of codes that correct tandem…

Information Theory · Computer Science 2018-01-09 Yeow Meng Chee , Johan Chrisnata , Han Mao Kiah , Tuan Thanh Nguyen

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible constrained to match empirical data, for instance, feature expectations. We seek to generalize…

Information Theory · Computer Science 2022-05-30 Kenneth Bogert

We consider the entropy of sums of independent discrete random variables, in analogy with Shannon's Entropy Power Inequality, where equality holds for normals. In our case, infinite divisibility suggests that equality should hold for…

Information Theory · Computer Science 2010-10-21 Oliver Johnson , Yaming Yu

Experiments have revealed correlation-driven behavior of DNA in charged solutions, including charge inversion and condensation. This paper presents calculations of a lattice-gas model of charge inversion for the adsorption of charged dimers…

Soft Condensed Matter · Physics 2019-02-15 Matthew D. Sievert , Marilyn F. Bishop , Tom McMullen

Entropic measures of complexity are able to quantify the information encoded in complex network structures. Several entropic measures have been proposed in this respect. Here we study the relation between the Shannon entropy and the Von…

Disordered Systems and Neural Networks · Physics 2011-09-30 Kartik Anand , Ginestra Bianconi , Simone Severini

Landauer's Principle states that the energy cost of information processing must exceed the product of the temperature and the change in Shannon entropy of the information-bearing degrees of freedom. However, this lower bound is achievable…

Statistical Mechanics · Physics 2019-01-01 A. B. Boyd , A. Patra , C. Jarzynski , J. P. Crutchfield