Search results for "Sum of normally distributed random variables"

showing 3 items of 3 documents

Embedding Quantum into Classical: Contextualization vs Conditionalization

2014

We compare two approaches to embedding joint distributions of random variables recorded under different conditions (such as spins of entangled particles for different settings) into the framework of classical, Kolmogorovian probability theory. In the contextualization approach each random variable is "automatically" labeled by all conditions under which it is recorded, and the random variables across a set of mutually exclusive conditions are probabilistically coupled (imposed a joint distribution upon). Analysis of all possible probabilistic couplings for a given set of random variables allows one to characterize various relations between their separate distributions (such as Bell-type ine…

Multivariate random variableFOS: Physical scienceslcsh:MedicineStability (probability)Joint probability distributionFOS: MathematicsMixture distributionStatistical physicslcsh:ScienceInverse distributionQuantum MechanicsProbabilityPhysicsta113Quantum PhysicsMultidisciplinaryModels StatisticalPhysicsProbability (math.PR)lcsh:RRandom Variables60A99 81P13Probability TheoryProbability DistributionAlgebra of random variablesEvents (Probability Theory)Sum of normally distributed random variablesPhysical SciencesQuantum Theorylcsh:QMarginal distributionQuantum EntanglementQuantum Physics (quant-ph)Mathematics - ProbabilityMathematicsResearch ArticlePlos One
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On (n-l)-wise and joint independence and normality of n Random variables: an example

1981

An example is given of a vector of n random variables such that any (n-1)-dimensional subvector consists of n-1 independent standard normal variables. The whole vector however is neither independent nor normal.

Statistics and ProbabilityPairwise independenceCombinatoricsExchangeable random variablesIndependent and identically distributed random variablesStandard normal deviateMultivariate random variableSum of normally distributed random variablesStatisticsMarginal distributionCentral limit theoremMathematicsCommunications in Statistics - Theory and Methods
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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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