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Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other…

Methodology · Statistics 2023-10-24 Robin J. Evans , Vanessa Didelez

The equivalence of the characteristic function approach and the probabilistic approach to monotone and boolean convolutions is proven for non-compactly supported probability measures. A probabilistically motivated definition of the…

Functional Analysis · Mathematics 2021-04-21 Uwe Franz

Let $B$ denote the range of the Brownian motion in $\mathbb{R}^{d}$ ($d\geq3$). For a deterministic Borel measure $\nu$ on $\mathbb{R}^{d}$ we wish to find a random measure $\mu$ such that the support of $\mu$ is contained in $B$ and it is…

Probability · Mathematics 2019-10-17 Ábel Farkas

Quantum mechanics is reformulated using Hartle's definition of the state of an individual physical system and a variant of von Neumann's propositional calculus. An elementary set of quantum postulates lead inductively to the familiar…

Quantum Physics · Physics 2015-06-04 Michael J. Cavagnero

In this paper, we first consider a family of constraints given by straight lines. For a uniform probability distribution, we determine the constrained optimal sets of $n$-points and the corresponding $n$th constrained quantization errors…

Probability · Mathematics 2025-09-26 Pavjeet Singh , S. K. Katiyar , Megha Pandey , Mrinal K. Roychowdhury

We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We…

Machine Learning · Statistics 2017-06-28 Ya'acov Ritov , Yuekai Sun , Ruofei Zhao

We revisit the problem of constructing predictive confidence sets for which we wish to obtain some type of conditional validity. We provide new arguments showing how ``split conformal'' methods achieve near desired coverage levels with high…

Statistics Theory · Mathematics 2025-03-04 John C. Duchi

Techniques for decision making with knowledge of linear constraints on condition probabilities are examined. These constraints arise naturally in many situations: upper and lower condition probabilities are known; an ordering among the…

Artificial Intelligence · Computer Science 2013-04-10 Michael Pittarelli

In the following we revisit the frequency interpretation of probability of Richard von Mises, in order to bring the essential implicit notions in focus. Following von Mises, we argue that probability can only be defined for events that can…

Quantum Physics · Physics 2010-11-30 Louis Vervoort

We consider probability measures on the real line or unit circle with Jacobi or Verblunsky coefficients satisfying an $\ell^p$ condition and a generalized bounded variation condition. This latter condition requires that a sequence can be…

Spectral Theory · Mathematics 2011-12-19 Milivoje Lukic

Quantile is an important risk measure quantifying the stochastic system random behaviors. This paper studies a pooled quantile estimator, which is the sample quantile of detailed simulation outputs after directly pooling independent sample…

Methodology · Statistics 2019-10-15 Qiong Zhang , Bo Wang , Wei Xie

Many key quantities in statistics and probability theory such as the expectation, quantiles, expectiles and many risk measures are law-determined maps from a space of random variables to the reals. We call such a law-determined map, which…

Probability · Mathematics 2026-04-08 Tobias Fissler , Ilya Molchanov

We first define the coarse-graining of probability measures in terms of stochastic kernels. We define when a probability measure is part of another probability measure and say that two probability measures coexist if they are both parts of…

Quantum Physics · Physics 2022-10-14 Stan Gudder

The notion of equality between two observables will play many important roles in foundations of quantum theory. However, the standard probabilistic interpretation based on the conventional Born formula does not give the probability of…

Quantum Physics · Physics 2014-12-31 Masanao Ozawa

Conditional probabilities are a core concept in machine learning. For example, optimal prediction of a label $Y$ given an input $X$ corresponds to maximizing the conditional probability of $Y$ given $X$. A common approach to inference tasks…

Machine Learning · Computer Science 2017-08-09 Yoav Wald , Amir Globerson

While finite non-commutative operator systems lie at the foundation of quantum measurement, they are also tools for understanding geometric iterations as used in the theory of iterated function systems (IFSs) and in wavelet analysis. Key is…

Mathematical Physics · Physics 2009-11-13 Palle E. T. Jorgensen

Concept of entangled probability distribution of several random variables is introduced. These probability distributions describe multimode quantum states in probability representation of quantum mechanics. Example of entangled probability…

Quantum Physics · Physics 2023-02-28 Vladimir N. Chernega , Olga V. Man'ko , Vladimir I. Man'ko

This article addresses an equidistribution problem concerning the zeros of systems of random holomorphic sections of positive line bundles on compact K\"{a}hler manifolds and random polynomials on $\mathbb{C}^{m}$ in the setting of the…

Complex Variables · Mathematics 2026-04-28 Ozan Günyüz

The Principle of Complementarity of Probabilities based on of noncommutative probability is introduced.

Quantum Physics · Physics 2011-05-10 Andrei Khrennikov , Sergei Kozyrev

Characterizations of surjective isometries with respect to the Kuiper distance on three classes of Borel probability measures of $\mathbb{R}$ (or equivalently, probability distribution functions) are presented here. These classes are the…

Functional Analysis · Mathematics 2017-01-24 György Pál Gehér