Search results for "Joint probability distribution"

showing 2 items of 52 documents

The Joint Distribution Criterion and the Distance Tests for Selective Probabilistic Causality

2010

A general definition and a criterion (a necessary and sufficient condition) are formulated for an arbitrary set of external factors to selectively influence a corresponding set of random entities (generalized random variables, with values in arbitrary observation spaces), jointly distributed at every treatment (a set of factor values containing precisely one value of each factor). The random entities are selectively influenced by the corresponding factors if and only if the following condition, called the joint distribution criterion, is satisfied : there is a jointly distributed set of random entities, one entity for every value of every factor, such that every subset of this set that corr…

selective influenceComputer scienceGeneralizationlcsh:BF1-990Value (computer science)systems of random variablescomputer.software_genre050105 experimental psychologyCausality (physics)Set (abstract data type)03 medical and health sciences0302 clinical medicineJoint probability distributionHypothesis and TheoryPsychology0501 psychology and cognitive sciencesstochastically unrelatedGeneral PsychologyDiscrete mathematics05 social sciencesProbabilistic logicexternal factorsstochastic dependencejoint distributionlcsh:PsychologyProbabilistic causalitySum of normally distributed random variablesData miningcomputerRandom variable030217 neurology & neurosurgeryFrontiers in Psychology
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Context–content systems of random variables : The Contextuality-by-Default theory

2016

Abstract This paper provides a systematic yet accessible presentation of the Contextuality-by-Default theory. The consideration is confined to finite systems of categorical random variables, which allows us to focus on the basics of the theory without using full-scale measure-theoretic language. Contextuality-by-Default is a theory of random variables identified by their contents and their contexts, so that two variables have a joint distribution if and only if they share a context. Intuitively, the content of a random variable is the entity the random variable measures or responds to, while the context is formed by the conditions under which these measurements or responses are obtained. A …

ta113Theoretical computer scienceComputer scienceApplied Mathematicscouplings05 social sciencesta111Probabilistic logicContext (language use)01 natural sciencesMeasure (mathematics)050105 experimental psychologyconnectednessKochen–Specker theoremrandom variablesJoint probability distribution0103 physical sciences0501 psychology and cognitive sciencescontextualityNegative number010306 general physicsCategorical variableRandom variableGeneral PsychologyJournal of Mathematical Psychology
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