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Optimizing Epistemic Model Checking Using Conditional Independence (Extended Abstract) [PDF]
Electronic 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
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Extended conditional independence and applications in causal inference [PDF]
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 ...
A P Dawid
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Fuzzy independence and extended conditional probability
Information Sciences, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Edward K Wong
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What matters most to older adults in treatment decision making: A discrete choice experiment. [PDF]
PLoS ONEIntroductionMedical 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
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Extending Hilbert–Schmidt Independence Criterion for Testing Conditional Independence
Entropy, 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
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Adapting Hidden Naive Bayes for Text Classification
Mathematics, 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
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Decision-theoretic foundations for statistical causality
Journal of Causal Inference, 2021 We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions.
Dawid Philip
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CoRR, 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 ...
Pingchuan Ma 0004 +4 more
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Decision-theoretic foundations for statistical causality: Response to Pearl
Journal 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
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Decision-theoretic foundations for statistical causality: Response to Shpitser
Journal of Causal Inference, 2022 I thank Ilya Shpitser for his comments on my article, and discuss the use of models with restricted interventions.
Dawid Philip
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