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Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the…

Machine Learning · Computer Science 2021-02-08 Samir Wadhwa , Roy Dong

Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most current CIT approaches assume that all variables are numerical…

Machine Learning · Computer Science 2023-11-07 Oana-Iuliana Popescu , Andreas Gerhardus , Jakob Runge

Contextuality is a non-classical behaviour that can be exhibited by quantum systems. It is increasingly studied for its relationship to quantum-over-classical advantages in informatic tasks. To date, it has largely been studied in…

Quantum Physics · Physics 2022-04-20 Rui Soares Barbosa , Tom Douce , Pierre-Emmanuel Emeriau , Elham Kashefi , Shane Mansfield

The Bell theorem is explored in terms of a trade-off relation between underlying assumptions within the hidden variable model framework. In this paper, recognizing the incorporation of hidden variables as one of the fundamental assumptions,…

Quantum Physics · Physics 2025-03-19 Ryo Takakura , Kei Morisue , Issei Watanabe , Gen Kimura

We propose a test of the conditional independence of random variables $X$ and~$Y$ given~$Z$ under the additional assumption that $X$ is stochastically nondecreasing in~$Z$. The well-documented hardness of testing conditional independence…

Methodology · Statistics 2026-04-24 Rohan Hore , Jake A. Soloff , Rina Foygel Barber , Richard J. Samworth

Quantum cognition often explains order effects, contextuality, and violations of the law of total probability by replacing classical probability with quantum probability on a fixed event structure. This paper proposes a different…

Artificial Intelligence · Computer Science 2026-05-26 Song-Ju Kim

Motivated by conditional independence testing, an essential step in constraint-based causal discovery algorithms, we study the nonparametric Von Mises estimator for the entropy of multivariate distributions built on a kernel density…

Machine Learning · Computer Science 2023-10-23 Fateme Jamshidi , Luca Ganassali , Negar Kiyavash

Beginning with the Bell theorem, cyclic systems of dichotomous random variables have been the object of many foundational findings in quantum mechanics. Here, we ask the question: if one chooses a cyclic system "at random" (uniformly within…

Quantum Physics · Physics 2021-04-14 Ehtibar N. Dzhafarov , Janne V. Kujala , Víctor H. Cervantes

Nonlocal nature apparently shown in entanglement is one of the most striking features of quantum theory. We examine the locality assumption in Bell-type proofs for entangled qubits, i.e. the outcome of a qubit at one end is independent of…

Quantum Physics · Physics 2009-03-23 Daegene Song

This Comment argues that two assumptions, which are presented as basic assumptions of Bell's theorem in [J. Handsteiner et al., Phys. Rev. Lett. 118, 060401 (2017)] and elsewhere, in fact follow from more basic premises. Measurement…

Quantum Physics · Physics 2017-05-12 Nathan Argaman

We introduce the concept of fidelity for dynamical maps in an open quantum system scenario. We derive an inequality linking this quantity to the distinguishability of the inducing environmental states. Our inequality imposes constraints on…

Quantum Physics · Physics 2017-05-10 Mikko Tukiainen , Henri Lyyra , Gniewomir Sarbicki , Sabrina Maniscalco

Local realism in recent experiments is excluded on condition of freedom or randomness of choice combined with no signaling between observers by implementations of simple quantum models. Both no-signaling and the underlying quantum model can…

Quantum Physics · Physics 2017-04-24 Adam Bednorz

Variable importance measures (VIMs) aim to quantify the contribution of each input covariate to the predictability of a given output. With the growing interest in explainable AI, numerous VIMs have been proposed, many of which are heuristic…

Methodology · Statistics 2025-09-23 Angel Reyero-Lobo , Pierre Neuvial , Bertrand Thirion

An extension of the latent class model is presented for clustering categorical data by relaxing the classical "class conditional independence assumption" of variables. This model consists in grouping the variables into inter-independent and…

Computation · Statistics 2015-10-01 Matthieu Marbac , Christophe Biernacki , Vincent Vandewalle

The problem of identifying measurement scenarios capable of revealing state-independent contextuality in a given Hilbert space dimension is considered. We begin by showing that for any given dimension $d$ and any measurement scenario…

Quantum Physics · Physics 2014-02-05 Ravishankar Ramanathan , Pawel Horodecki

In this paper we study covariance estimation with missing data. We consider missing data mechanisms that can be independent of the data, or have a time varying dependency. Additionally, observed variables may have arbitrary (non uniform)…

Statistics Theory · Mathematics 2021-06-17 Eduardo Pavez , Antonio Ortega

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process…

Machine Learning · Statistics 2016-09-14 Nguyen Tran Quang , Alexander Jung

We discuss quantum non-locality and contextuality using the notion of transition sets. This approach provides a way to obtain a direct logical contradiction with locality/non-contextuality in the EPRB gedanken experiment as well as a clear…

Quantum Physics · Physics 2007-11-19 Hans Westman

Contextuality is a particular quantum phenomenon that has no analogue in classical probability theory. Given two independent systems, a natural question is how to represent such a situation as a single test space. In other words, how…

Quantum Physics · Physics 2018-12-24 Ana Belén Sainz , Elie Wolfe

Identifying dependency between two random variables is a fundamental problem. The clear interpretability and ability of a procedure to provide information on the form of possible dependence is particularly important when exploring…

Methodology · Statistics 2026-04-27 Bogdan Ćmiel , Teresa Ledwina
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