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Related papers: Negative Probability

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Measurable quantities that have positive values in classical dynamical systems need not to be positive in quantum theory. For example, consider a free quantum mechanical particle in one dimension. There are quantum states in which the…

Quantum Physics · Physics 2017-12-12 Daniela Cadamuro

How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches…

Information Retrieval · Computer Science 2022-07-12 Bin Liu , Bang Wang

The conditions for observation of the particle coordinates, required by logic of the Special Relativity and filtering the quantum field effects, are described. A general relation between the corresponding density of probability and the wave…

Quantum Physics · Physics 2008-07-03 V. F. Krotov

Although the specification of bivariate probability models using a collection of assumed conditional distributions is not a novel concept, it has received considerable attention in the last decade. In this study, a bivariate…

Methodology · Statistics 2025-03-20 Indranil Ghosh , Mina Norouzirad , Filipe J. Marques

In this article, we discuss a bivariate distribution whose conditionals are univariate binomial distributions and the marginals are not binomial that exhibits negative correlation. Some useful structural properties of this distribution…

Methodology · Statistics 2023-01-10 Indranil Ghosh , Filipe Marques , Subrata Chakraborty

Nested Sampling is a method for computing the Bayesian evidence, also called the marginal likelihood, which is the integral of the likelihood with respect to the prior. More generally, it is a numerical probabilistic quadrature rule. The…

Computation · Statistics 2023-10-09 Jonas Latz , Doris Schneider , Philipp Wacker

Quantum theory does not provide a unique definition for the joint probability of two non-commuting observables, which is the next important question after the Born's probability for a single observable. Instead, various definitions were…

Quantum Physics · Physics 2018-04-04 Armen E. Allahverdyan , Arshag Danageozian

The subjective and the objective aspects of probabilities are incorporated in a simple duality axiom inspired by observer participation in quantum theory. Transcending the classical notion of probabilities, it is proposed and demonstrated…

Quantum Physics · Physics 2013-03-20 Rajat Kumar Pradhan

Two notions of nonclassicality that have been investigated intensively are: (i) negativity, that is, the need to posit negative values when representing quantum states by quasiprobability distributions such as the Wigner representation, and…

Quantum Physics · Physics 2008-07-07 Robert W. Spekkens

The left-to-right motion of a free quantum Gaussian wave packet can be accompanied by the right-to-left flow of the probability density, the effect recently studied by Villanueva [Am. J. Phys. 88, 325 (2020)]. Using the Wigner…

Quantum Physics · Physics 2020-10-27 Arseni Goussev

The products of weak values of quantum observables are shown to be of value in deriving quantum uncertainty and complementarity relations, for both weak and strong measurement statistics. First, a 'product representation formula' allows the…

Quantum Physics · Physics 2016-06-08 Michael J. W. Hall , Arun Kumar Pati , Junde Wu

In this work, we elaborate on a measure-theoretic approach to negative probabilities. We study a natural notion of contextuality measure and characterize its main properties. Then, we apply this measure to relevant examples of quantum…

Quantum Physics · Physics 2023-02-02 Elisa Monchietti , César Massri , J. Acacio de Barros , Federico Holik

Starting from considerations about meaning and subsequent use of asymmetric uncertainty intervals of experimental results, we review the issue of uncertainty propagation. We show that, using a probabilistic approach (the so-called Bayesian…

High Energy Physics - Experiment · Physics 2007-05-23 G. D'Agostini , M. Raso

It is common to model random errors in a classical measurement by the normal (Gaussian) distribution, because of the central limit theorem. In the quantum theory, the analogous hypothesis is that the matrix elements of the error in an…

Quantum Physics · Physics 2009-11-10 S. G. Rajeev

We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution $P_0$, which may not be in the support of the prior, we show that the posterior…

Statistics Theory · Mathematics 2007-06-13 B. J. K. Kleijn , A. W. van der Vaart

In this article, we introduce mixture representations for likelihood ratio ordered distributions. Essentially, the ratio of two probability densities, or mass functions, is monotone if and only if one can be expressed as a mixture of…

Methodology · Statistics 2023-10-30 Michael Jauch , Andrés F. Barrientos , Víctor Peña , David S. Matteson

Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and…

Theoretical Economics · Economics 2020-05-04 Ole Peters , Alexander Adamou , Mark Kirstein , Yonatan Berman

This brief paper develops a probability density that models processes for which the physical mechanism is unknown. It has desirable properties which are not realized by densities derived from Gaussian process or other classic methods. In…

General Physics · Physics 2011-04-21 Steven C. Gustafson , Adam C. Hillier

In this paper, we examined the connection between quantum systems' indistinguishability and signed (or negative) probabilities. We do so by first introducing a measure-theoretic definition of signed probabilities inspired by research in…

Quantum Physics · Physics 2020-07-30 J. Acacio de Barros , Federico Holik

We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks that lead to a node…

Artificial Intelligence · Computer Science 2012-07-19 Lenhart Schubert
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