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Modal Specifications for Probabilistic Timed Systems [PDF]
Electronic Proceedings in Theoretical Computer Science, 2013 Modal automata are a classic formal model for component-based systems that comes equipped with a rich specification theory supporting abstraction, refinement and compositional reasoning.
Tingting Han +3 more
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A Probabilistic Analysis of Causation [PDF]
, 2009 The starting point in the development of probabilistic analyses of token causation has usually been the naive intuition that, in some relevant sense, a cause raises the probability of its effect.
Glynn, Luke, Luke Glynn, Glynn, L
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BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks
Journal of Statistical Software, 2020 The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous).
Han Yu +2 more
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Mapping Validation by Probabilistic Reasoning [PDF]
, 2008 In the semantic web environment, where several independent ontologies are used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived from the semantics of the ontologies that are compared.
Silvana Castano +4 more
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Probabilistic Couplings for Probabilistic Reasoning
CoRR, 2017 This thesis explores proofs by coupling from the perspective of formal verification. Long employed in probability theory and theoretical computer science, these proofs construct couplings between the output distributions of two probabilistic processes. Couplings can imply various guarantees comparing two runs of a probabilistic computation.
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Model fitting data from syllogistic reasoning experiments
Data in Brief, 2016 The data presented in this article are related to the research article entitled “Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics” (M. Hattori, 2016) [1].
Masasi Hattori
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ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition
IEEE Access, 2020 The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from ...
Claudio Bettini +3 more
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A Logic For Inductive Probabilistic Reasoning [PDF]
Synthese, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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IS CAUSAL REASONING HARDER THAN PROBABILISTIC REASONING?
The Review of Symbolic Logic, 2022 AbstractMany tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages.
Milan Mossé +2 more
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Zero-shot visual reasoning through probabilistic analogical mapping
Nature Communications, 2023 Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct
Taylor Webb +4 more
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