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Related papers: Majorizing Measures, Codes, and Information

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In this paper, we use a new partial order, called the f-majorization order. The new order includes as special cases the majorization , the reciprocal majorization and the p-larger orders. We provide a comprehensive account of the…

Statistics Theory · Mathematics 2017-04-13 Esmaeil Bashkar , Hamzeh Torabi , Ali Dolati , Felix Belzunce

We studied the mutual information between a stimulus and a large system consisting of stochastic, statistically independent elements that respond to a stimulus. The Mutual Information (MI) of the system saturates exponentially with system…

Statistical Mechanics · Physics 2009-11-07 Kukjin Kang , Haim Sompolinsky

In many contexts it is extremely costly to perform enough high quality experimental measurements to accurately parameterize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that…

Data Analysis, Statistics and Probability · Physics 2017-11-22 Alpha A. Lee , Michael P. Brenner , Lucy J. Colwell

The problem of measuring an unbounded system attribute near a singularity has been discussed. Lenses have been introduced as formal objects to study increasingly precise measurements around the singularity and a specific family of lenses…

General Mathematics · Mathematics 2020-07-13 Swagatam Sen

We extend the discrete majorization theory by working with non-normalized Lorenz curves. Then we prove two generalizations of the Muirhead theorem. These not only use elementary transfers but also local increases. Together these operations…

Discrete Mathematics · Computer Science 2022-12-08 Leo Egghe , Ronald Rousseau

We propose a new interpretation of measures of information and disorder by connecting these concepts to group theory in a new way. Entropy and group theory are connected here by their common relation to sets of permutations. A combinatorial…

Information Theory · Computer Science 2019-11-25 David J. Galas

What does it mean for one quantum process to be more disordered than another? Here we provide a precise answer to this question in terms of a quantum-mechanical generalization of majorization. The framework admits a complete description in…

Quantum Physics · Physics 2018-12-21 Gilad Gour , David Jennings , Francesco Buscemi , Runyao Duan , Iman Marvian

In this contribution I review rigorous formulations of a variety of limitations of measurability in quantum mechanics. To this end I begin with a brief presentation of the conceptual tools of modern measurement theory. I will make precise…

Quantum Physics · Physics 2011-01-04 Paul Busch

Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated…

Neurons and Cognition · Quantitative Biology 2017-02-08 Max Tegmark

In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomography. In such strategies, unknown quantum states are first…

Quantum Physics · Physics 2023-11-22 Casper Gyurik , Riccardo Molteni , Vedran Dunjko

We present a new approach to study measures on ensembles of contours, polymers or other objects interacting by some sort of exclusion condition. For concreteness we develop it here for the case of Peierls contours. Unlike existing methods,…

Probability · Mathematics 2016-08-15 Roberto Fernández , Pablo A. Ferrari , Nancy L. Garcia

Random access codes are an intriguing class of communication tasks that reveal an operational and quantitative difference between classical and quantum information processing. We formulate a natural generalization of random access codes and…

Quantum Physics · Physics 2022-06-22 Teiko Heinosaari , Leevi Leppäjärvi

Nowadays new technologies, and especially artificial intelligence, are more and more established in our society. Big data analysis and machine learning, two sub-fields of artificial intelligence, are at the core of many recent breakthroughs…

Machine Learning · Statistics 2021-06-22 Antonio Sutera

Measurement theory is the cornerstone of science, but no equivalent theory underpins the huge volumes of non-numerical data now being generated. In this study, we show that replacing numbers with alternative mathematical models, such as…

Other Computer Science · Computer Science 2020-12-23 David G. Green , Kerri Morgan , Marc Cheong

The aim of this note is to prove a law of large numbers for local patterns in discrete point processes. We investigate two different situations: a class of point processes on the one dimensional lattice including certain Schur measures, and…

Probability · Mathematics 2022-02-16 Pierre Lazag

Suppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter $p$. By the law of large numbers, the frequency of zeros in the sequence tends to~$p$, and thus we can get better and better…

Logic · Mathematics 2018-10-18 Laurent Bienvenu , Santiago Figueira , Benoit Monin , Alexander Shen

Kifer, Peres, and Weiss proved that there exists $c_0>0,$ such that $\dim \mu\leq 1-c_0$ for any probability measure $\mu$ which makes the digits of the continued fraction expansion i.i.d. random variables. In this paper we prove that…

Dynamical Systems · Mathematics 2018-02-22 Simon Baker , Natalia Jurga

The field of quantum resource theory (QRT) has emerged as an invaluable framework for the examination of small and strongly correlated quantum systems, surpassing the boundaries imposed by traditional statistical treatments. The fundamental…

Quantum Physics · Physics 2023-08-29 Gökhan Torun , Onur Pusuluk , Özgür E. Müstecaplıoğlu

We present a novel derivation of the constraints required to obtain the underlying principles of statistical mechanics using a maximum entropy framework. We derive the mean value constraints by use of the central limit theorem and the…

Statistical Mechanics · Physics 2017-03-30 Diego Granziol , Stephen Roberts

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g.…

Numerical Analysis · Computer Science 2013-03-19 Bojana V. Rosić , Anna Kučerová , Jan Sýkora , Oliver Pajonk , Alexander Litvinenko , Hermann G. Matthies