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Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art…

Machine Learning · Statistics 2018-05-01 Samuel Burkart , Franz J Király

R. Duncan Luce once mentioned in a conversation that he did not consider Kolmogorov's probability theory well-constructed because it treats stochastic independence as a "numerical accident," while it should be treated as a fundamental…

Probability · Mathematics 2016-02-12 Ehtibar Dzhafarov

For an initial-boundary value problem for a parabolic equation in the spatial variable $x=(x_1,.., x_n)$ and time $t$, we consider an inverse problem of determining a coefficient which is independent of one spatial component $x_n$ by extra…

Analysis of PDEs · Mathematics 2020-09-22 Oleg Yu. Imanuvilov , Yavar Kian , Masahiro Yamamoto

This special volume of Statistical Sciences presents some innovative, if not provocative, ideas in the area of reliability, or perhaps more appropriately named, integrated system assessment. In this age of exponential growth in science,…

Methodology · Statistics 2009-09-29 Sallie Keller-McNulty , Alyson Wilson , Christine Anderson-Cook

In this paper, using inverse integral transforms, we derive the exact distribution of the random variable $X$ that is involved in the ratio $Z \stackrel{d}{=} X/(X+Y)$ where $X$ and $Y$ are independent random variables having the same…

Probability · Mathematics 2023-07-14 Roberto Vila , Narayanaswamy Balakrishnan , Marcelo Bourguignon

The proposal and study of dependent prior processes has been a major research focus in the recent Bayesian nonparametric literature. In this paper, we introduce a flexible class of dependent nonparametric priors, investigate their…

Statistics Theory · Mathematics 2014-07-03 Antonio Lijoi , Bernardo Nipoti , Igor Prünster

We introduce the concepts of dependence and independence in a very general framework. We use a concept of rank to study dependence and independence. By means of the rank we identify (total) dependence with inability to create more…

Logic in Computer Science · Computer Science 2021-09-27 Pietro Galliani , Jouko Väänänen

The question of how irreversibility can emerge as a generic phenomena when the underlying mechanical theory is reversible has been a long-standing fundamental problem for both classical and quantum mechanics. We describe a mechanism for the…

Quantum Physics · Physics 2013-09-20 Cozmin Ududec , Nathan Wiebe , Joseph Emerson

We introduce the concept of mutual independence -- correlations shared between distant parties which are independent of the environment. This notion is more general than the standard idea of a secret key -- it is a fully quantum and more…

Quantum Physics · Physics 2009-11-05 Michal Horodecki , Jonathan Oppenheim , Andreas Winter

Variance is a ubiquitous quantity in quantum information theory. Given a basis, we consider the averaged variances of a fixed diagonal observable in a pure state under all possible permutations on the components of the pure state and call…

Quantum Physics · Physics 2022-07-14 Ming-Jing Zhao , Lin Zhang , Shao-Ming Fei

We consider two concepts often discussed as significant features of general relativity (particularly when contrasted with the other forces of the Standard Model): background independence and diffeomorphism invariance. We remind the reader…

General Relativity and Quantum Cosmology · Physics 2015-12-14 Casey Cartwright , Alex Flournoy

Among various approaches in proving gauge independence, models containing an explicit gauge dependence are convenient. The well-known example is the gauge parameter in the covariant gauge fixing which is of course most suitable for the…

High Energy Physics - Theory · Physics 2009-10-30 T. Kashiwa , N. Tanimura

Independence testing plays a central role in statistical and causal inference from observational data. Standard independence tests assume that the data samples are independent and identically distributed (i.i.d.) but that assumption is…

Machine Learning · Statistics 2022-07-04 Ragib Ahsan , Zahra Fatemi , David Arbour , Elena Zheleva

We initiate an investigation how the fundamental concept of independence can be represented effectively in the presence of incomplete information in relational databases. The concepts of possible and certain independence are proposed, and…

Databases · Computer Science 2025-10-10 Miika Hannula , Minna Hirvonen , Juha Kontinen , Sebastian Link

The path integral formulation of Quantum Field Theory implies an infinite set of local, Schwinger-Dyson-like relations. Exact renormalization group equations can be cast as a particular instance of these relations. Furthermore, exact scheme…

High Energy Physics - Theory · Physics 2009-11-07 Jose I. Latorre , Tim R. Morris

In the case of systems composed of identical particles, a typical instance in quantum statistical mechanics, the standard approach to separability and entanglement ought to be reformulated and rephrased in terms of correlations between…

Quantum Physics · Physics 2014-05-21 F. Benatti , R. Floreanini

Statistical mechanics for states with complex eigenvalues, which are described by Gel'fand triplet and represent unstable states like resonances, are discussed on the basis of principle of equal ${\it a priori}$ probability. A new entropy…

Statistical Mechanics · Physics 2016-08-31 T. Kobayashi , T. Shimbori

\noindent In our contribution to this volume we deal with \emph{discrete} symmetries: these are symmetries based upon groups with a discrete set of elements (generally a set of elements that can be enumerated by the positive integers). In…

Quantum Physics · Physics 2007-05-23 S. R. D. French , D. P. Rickles

As a rigorous statistical approach, statistical Taylor expansion extends the conventional Taylor expansion by replacing precise input variables with random variables of known distributions and sample counts to compute the mean, the…

Computation · Statistics 2026-05-19 Chengpu Wang

The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with…

Machine Learning · Computer Science 2023-03-28 Mark Bun , Marco Gaboardi , Max Hopkins , Russell Impagliazzo , Rex Lei , Toniann Pitassi , Satchit Sivakumar , Jessica Sorrell