Results 21 to 30 of about 1,301,577 (294)
Sound Over-Approximation of Probabilities
Acta Cybernetica, 2020 Safety analysis of high confidence systems requires guaranteed bounds on the probability of events of interest. Establishing the correctness of algorithms that compute such bounds is challenging. We address this problem in three steps. First, we use monadic transition systems (MTS) in the category of sets as a general framework for modeling discrete ...
Eugenio Moggi +2 more
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Безопасность информационных технологий, 2021 ARX stochastic algorithms are a promising solution in the development of unpredictable pseudo- random number generators for low-resource systems. The ease of implementation of their round operations, as well as their high energy efficiency, make the ...
Alexander A. Kozlov, Mikhail A. Ivanov
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Probabilistic Output Analyses for Deterministic Programs — Reusing Existing Non-probabilistic Analyses [PDF]
Electronic Proceedings in Theoretical Computer Science, 2020 We consider reusing established non-probabilistic output analyses (either forward or backwards) that yield over-approximations of a program's pre-image or image relation, e.g., interval analyses. We assume a probability measure over the program input and
Maja Hanne Kirkeby
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On the implementation of local probability matching priors for interest parameters [PDF]
, 2005 Probability matching priors are priors for which the posterior probabilities of certain specified sets are exactly or approximately equal to their coverage probabilities.
Sweeting, TJ, Trevor J. Sweeting
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Exact Probability Distribution versus Entropy
Entropy, 2014 The problem addressed concerns the determination of the average number of successive attempts of guessing a word of a certain length consisting of letters with given probabilities of occurrence.
Kerstin Andersson
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Explicitly Invertible Approximations of the Gaussian Q-Function: A Survey
IEEE Open Journal of the Communications Society, 2023 Communications and information theory use the Gaussian $Q$ -function, a positive and decreasing function, across the literature. Its approximations were created to simplify mathematical study of the Gaussian $Q$ -function expressions. This is important
Alessandro Soranzo +4 more
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Scarce Sample-Based Reliability Estimation and Optimization Using Importance Sampling
Mathematical and Computational Applications, 2022 Importance sampling is a variance reduction technique that is used to improve the efficiency of Monte Carlo estimation. Importance sampling uses the trick of sampling from a distribution, which is located around the zone of interest of the primary ...
Kiran Pannerselvam +2 more
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AppliedMath, 2022 We consider the inhomogeneous Wiener–Hopf equation whose kernel is a nonarithmetic probability distribution with positive mean. The inhomogeneous term behaves like a submultiplicative function.
Mikhail Sgibnev
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Depth-Bounded Approximations of Probability [PDF]
, 2020 We introduce measures of uncertainty that are based on Depth-Bounded Logics [4] and resemble belief functions. We show that our measures can be seen as approximation of classical probability measures over classical logic, and that a variant of the PSAT [10] problem for them is solvable in polynomial time.
Baldi, Paolo +2 more
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Parallel probability density approximation [PDF]
Behavior Research Methods, 2019 Probability density approximation (PDA) is a nonparametric method of calculating probability densities. When integrated into Bayesian estimation, it allows researchers to fit psychological processes for which analytic probability functions are unavailable, significantly expanding the scope of theories that can be quantitatively tested. PDA is, however,
Yi-Shin, Lin +2 more
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