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Optimizing Epistemic Model Checking Using Conditional Independence (Extended Abstract) [PDF]

open access: greenElectronic Proceedings in Theoretical Computer Science, 2017
This paper shows that conditional independence reasoning can be applied to optimize epistemic model checking, in which one verifies that a model for a number of agents operating with imperfect information satisfies a formula expressed in a modal multi-
Ron van der Meyden
doaj   +6 more sources

Extended conditional independence and applications in causal inference [PDF]

open access: greenThe Annals of Statistics, 2017
The goal of this paper is to integrate the notions of stochastic conditional independence and variation conditional independence under a more general notion of extended conditional independence. We show that under appropriate assumptions the calculus that applies for the two cases separately (axioms of a separoid) still applies for the extended case ...
Constantinou, Panayiota   +1 more
core   +9 more sources

Fuzzy independence and extended conditional probability

open access: hybridInformation Sciences, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hoffman, Mark E.   +2 more
semanticscholar   +3 more sources

Extending Hilbert–Schmidt Independence Criterion for Testing Conditional Independence [PDF]

open access: goldEntropy, 2023
The Conditional Independence (CI) test is a fundamental problem in statistics. Many nonparametric CI tests have been developed, but a common challenge exists: the current methods perform poorly with a high-dimensional conditioning set. In this paper, we considered a nonparametric CI test using a kernel-based test statistic, which can be viewed as an ...
Bingyuan Zhang, Joe Suzuki
openaire   +4 more sources

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version) [PDF]

open access: green, 2023
Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable ...
Ma, Pingchuan   +4 more
openaire   +3 more sources

What matters most to older adults in treatment decision making: A discrete choice experiment. [PDF]

open access: yesPLoS ONE
IntroductionMedical decision making is often guided bydisease-specific outcomes such as life extension or survival. Especially for older adults other outcomes like maintaining independence can be equally vital or more important. Enhanced insight into the
Vera C Hanewinkel   +8 more
doaj   +2 more sources

Comparison of Parametric Rate Models for Gap Times Between Recurrent Events

open access: yesMathematics
Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the ...
Ivo Sousa-Ferreira   +2 more
doaj   +2 more sources

Empirical Projected Copula Process and Conditional Independence An Extended Version

open access: green, 2013
La dépendence conditionnelle est exprimée comme une projection dans l'espace copule trivarié. La copule projections, la copule empirique et le processus associés sont définis. La convergence faible du processus vers un processus gaussien centré est obtenu sous des hypothèses faibles portant sur les dérivés des copules.
Lorenzo Frattarolo, Dominique Guegan
openaire   +3 more sources

Adapting Hidden Naive Bayes for Text Classification

open access: yesMathematics, 2021
Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, reduces
Shengfeng Gan   +4 more
doaj   +1 more source

Decision-theoretic foundations for statistical causality: Response to Pearl

open access: yesJournal of Causal Inference, 2022
I thank Judea Pearl for his discussion of my paper and respond to the points he raises. In particular, his attachment to unaugmented directed acyclic graphs has led to a misapprehension of my own proposals. I also discuss the possibilities for developing
Dawid Philip
doaj   +1 more source

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