scholarly journals Classical (Local and Contextual) Probability Model for Bohm–Bell Type Experiments: No-Signaling as Independence of Random Variables

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 157 ◽  
Author(s):  
Andrei Khrennikov ◽  
Alexander Alodjants
Keyword(s):  
Conditional Probability ◽  
Probability Model ◽  
Hidden Variables ◽  
Random Variables ◽  
Experimental Scheme ◽  
Model Following ◽  
No Signaling ◽  
Independence Of Random Variables ◽  
Main Message ◽  
Selection Of

We start with a review on classical probability representations of quantum states and observables. We show that the correlations of the observables involved in the Bohm–Bell type experiments can be expressed as correlations of classical random variables. The main part of the paper is devoted to the conditional probability model with conditioning on the selection of the pairs of experimental settings. From the viewpoint of quantum foundations, this is a local contextual hidden-variables model. Following the recent works of Dzhafarov and collaborators, we apply our conditional probability approach to characterize (no-)signaling. Consideration of the Bohm–Bell experimental scheme in the presence of signaling is important for applications outside quantum mechanics, e.g., in psychology and social science. The main message of this paper (rooted to Ballentine) is that quantum probabilities and more generally probabilities related to the Bohm–Bell type experiments (not only in physics, but also in psychology, sociology, game theory, economics, and finances) can be classically represented as conditional probabilities.

scholarly journals Calculation of Misjudgment Probability for Product Inspection Based on Measurement Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiao-huai Chen ◽  
Yin-bao Cheng ◽  
Han-bin Wang ◽  
Hong-li Li ◽  
Zhen-ying Cheng ◽  
...  
Keyword(s):  
Measurement Uncertainty ◽  
Conditional Probability ◽  
Probability Model ◽  
Probability Models ◽  
Estimation Formula ◽  
Sampling Inspection ◽  
Measurement Results ◽  
Absolute Probability ◽  
Selection Of

It is important to research into the misjudgment probability of product inspection based on measurement uncertainty, which is of great significance to improve the reliability of inspection results. This paper mainly focused on total inspection and sampling inspection methods and regarded the misjudgment probability as the index to provide quantitative misjudgment risk results for both producer and consumer sides. Through the absolute probability and the conditional probability model, the estimation formula of the total inspection misjudgment rate is deduced, respectively, and the calculation methods of qualification determination and misjudgment rate of the full inspection results are studied. According to the total inspection misjudgment rate, the methods of misjudgment rate of sampling inspection and qualification determination of measurement results are researched. The misjudgment rate of measurement results is calculated based on the exhaustive method and the Monte-Carlo simulation. The estimation results show that the misjudgment probabilities calculated by absolute probability models can be used as the basis for the selection of the measurement plan for product inspection. The misjudgment probability calculated by conditional probability models is more directly to reflect the risks for both producer and consumer sides, and it prompts inspectors to make decisions more carefully.

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

Error and Reliability in Stochastic Processes and Psychological Measurement

1972 ◽  
Vol 31 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Donald W. Zimmerman
Keyword(s):  
Stochastic Processes ◽  
Random Error ◽  
Random Sampling ◽  
Probability Model ◽  
Random Variables ◽  
Formal Structure ◽  
Intermediate Structure ◽  
Psychological Measurement ◽  
Joint Distributions ◽  
True Values

The concepts of random error and reliability of measurements that are familiar in traditional theories based on the notions of “true values” and “errors” can be represented by a probability model having a simpler formal structure and fewer special assumptions about random sampling and independence of measurements. In this model formulas that relate observable events are derived from probability axioms and from primitive terms that refer to observable events, without an intermediate structure containing variances and correlations of “true” and “error” components of scores. While more economical in language and formalism, the model at the same time is more general than classical theories and applies to stochastic processes in which joint distributions of many dependent random variables are of interest. In addition, it clarifies some long-standing problems concerning “experimental independence” of measurements and the relation of sampling of individuals to sampling of measurements.

A normal form theorem for Lω1p, with applications

1982 ◽  
Vol 47 (3) ◽  
pp. 605-624 ◽  
Author(s):  
Douglas N. Hoover
Keyword(s):  
Normal Form ◽  
Probability Model ◽  
Random Variables ◽  
Interpolation Theorem ◽  
Complete Theory ◽  
De Finetti’S Theorem ◽  
Form Theorem ◽  
Normal Form Theorem ◽  
De Finetti's Theorem

AbstractWe show that every formula of Lω1P is equivalent to one which is a propositional combination of formulas with only one quantifier. It follows that the complete theory of a probability model is determined by the distribution of a family of random variables induced by the model. We characterize the class of distribution which can arise in such a way. We use these results together with a form of de Finetti’s theorem to prove an almost sure interpolation theorem for Lω1P.

Probability Spaces and the Theory of Error of Measurement

1971 ◽  
Vol 28 (1) ◽  
pp. 291-301 ◽  
Author(s):  
Donald W. Zimmerman
Keyword(s):  
Probability Model ◽  
Random Variables ◽  
Measurement Procedure ◽  
Psychological Tests ◽  
Random Variability ◽  
Probability Spaces ◽  
True Values ◽  
Suitable Product ◽  
Error Of Measurement

A model of variability in measurement, which is sufficiently general for a variety of applications and which includes the main content of traditional theories of error of measurement and psychological tests, can be derived from the axioms of probability, without introducing “true values” and “errors.” Beginning with probability spaces (Ω, P1) and (φ, P2), the set Ω representing the outcomes of a measurement procedure and the set * representing individuals or experimental objects, it is possible to construct suitable product probability spaces and collections of random variables which can yield all results needed to describe random variability and reliability. This paper attempts to fill gaps in the mathematical derivations in many classical theories and at the same time to overcome limitations in the language of “true values” and “errors” by presenting explicitly the essential constructions required for a general probability model.

An Improved Model-Free Sampling Technique Based on Bootstrap Methods

2016 ◽  
Author(s):  
Xinshui Yu ◽  
Zhaohui Yang ◽  
Kunling Song ◽  
Tianxiang Yu ◽  
Bozhi Guo
Keyword(s):  
Random Variables ◽  
Sampling Technique ◽  
Improved Method ◽  
Initial Sample ◽  
Bootstrap Methods ◽  
Local Characteristics ◽  
Model Free ◽  
Improved Model ◽  
Center Distance ◽  
Selection Of

The distribution and parameters of the random variables is an important part of conventional reliability analysis methods, such as Monte Carlo method, which should be known fist before using these methods, but it is often hard or impossible to obtain. Model-free sampling technique puts forward a method to get the distribution of the random variables, but the accuracy of the extended sample generated by it is not enough. This paper presented an improved model-free sampling technique, which is based on Bootstrap methods, to increase the accuracy of the extended sample and decrease the iteration times. In this improved model-free sampling technique, the method of the selection of initial sample points and the generation of iterative sample is improved. Meanwhile, a center distance criterion, which considers the local characteristics of the extended sample, is added to the generating criterion of dissimilarity measure. The effectiveness of this improved method is illustrated through some numerical examples.

Optimal selection of stochastic intervals under a sum constraint

1987 ◽  
Vol 19 (2) ◽  
pp. 454-473 ◽  
Author(s):  
E. G. Coffman ◽  
L. Flatto ◽  
R. R. Weber
Keyword(s):  
Decision Rule ◽  
Selection Process ◽  
Random Variables ◽  
Optimal Threshold ◽  
Optimal Selection ◽  
Expected Number ◽  
Common Distribution ◽  
The Common ◽  
Image Position ◽  
Selection Of

We model a selection process arising in certain storage problems. A sequence (X1, · ··, Xn) of non-negative, independent and identically distributed random variables is given. F(x) denotes the common distribution of the Xi′s. With F(x) given we seek a decision rule for selecting a maximum number of the Xi′s subject to the following constraints: (1) the sum of the elements selected must not exceed a given constant c > 0, and (2) the Xi′s must be inspected in strict sequence with the decision to accept or reject an element being final at the time it is inspected.We prove first that there exists such a rule of threshold type, i.e. the ith element inspected is accepted if and only if it is no larger than a threshold which depends only on i and the sum of the elements already accepted. Next, we prove that if F(x) ~ Axα as x → 0 for some A, α> 0, then for fixed c the expected number, En(c), selected by an optimal threshold is characterized by Asymptotics as c → ∞and n → ∞with c/n held fixed are derived, and connections with several closely related, well-known problems are brought out and discussed.

scholarly journals Nonrecurrence and Bell-like inequalities

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 762-768
Author(s):  
Douglas G. Danforth
Keyword(s):  
Infinite Number ◽  
General Class ◽  
Hidden Variables ◽  
Random Variables ◽  
Random Variable ◽  
Bell Inequalities ◽  
Usual Sense ◽  
Real Random Variable ◽  
Action At A Distance ◽  
The Continuum

AbstractThe general class,Λ, of Bell hidden variables is composed of two subclassesΛRandΛNsuch thatΛR⋃ΛN=ΛandΛR∩ΛN= {}. The classΛNis very large and contains random variables whose domain is the continuum, the reals. There are an uncountable infinite number of reals. Every instance of a real random variable is unique. The probability of two instances being equal is zero, exactly zero.ΛNinduces sample independence. All correlations are context dependent but not in the usual sense. There is no “spooky action at a distance”. Random variables, belonging toΛN, are independent from one experiment to the next. The existence of the classΛNmakes it impossible to derive any of the standard Bell inequalities used to define quantum entanglement.

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