Search results for "Probability."

showing 10 items of 3396 documents

On a representation theorem for finitely exchangeable random vectors

2016

A random vector $X=(X_1,\ldots,X_n)$ with the $X_i$ taking values in an arbitrary measurable space $(S, \mathscr{S})$ is exchangeable if its law is the same as that of $(X_{\sigma(1)}, \ldots, X_{\sigma(n)})$ for any permutation $\sigma$. We give an alternative and shorter proof of the representation result (Jaynes \cite{Jay86} and Kerns and Sz\'ekely \cite{KS06}) stating that the law of $X$ is a mixture of product probability measures with respect to a signed mixing measure. The result is "finitistic" in nature meaning that it is a matter of linear algebra for finite $S$. The passing from finite $S$ to an arbitrary one may pose some measure-theoretic difficulties which are avoided by our p…

Discrete mathematicsRepresentation theoremMultivariate random variableApplied MathematicsSigned measureProbability (math.PR)010102 general mathematicsSpace (mathematics)01 natural sciencesMeasure (mathematics)60G09 (Primary) 60G55 62E99 (Secondary)010104 statistics & probabilityHomogeneous polynomialFOS: Mathematics0101 mathematicsMathematics - ProbabilityAnalysisMixing (physics)MathematicsProbability measureJournal of Mathematical Analysis and Applications
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Probability Propagation in Selected Aristotelian Syllogisms

2019

This paper continues our work on a coherence-based probability semantics for Aristotelian syllogisms (Gilio, Pfeifer, and Sanfilippo, 2016; Pfeifer and Sanfilippo, 2018) by studying Figure III under coherence. We interpret the syllogistic sentence types by suitable conditional probability assessments. Since the probabilistic inference of $P|S$ from the premise set ${P|M, S|M}$ is not informative, we add $p(M|(S ee M))>0$ as a probabilistic constraint (i.e., an ``existential import assumption'') to obtain probabilistic informativeness. We show how to propagate the assigned premise probabilities to the conclusion. Thereby, we give a probabilistic meaning to all syllogisms of Figure~III. We…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica Matematica05 social sciencesProbabilistic logicSyllogismConditional probability02 engineering and technologyCoherence (statistics)Settore MAT/01 - Logica MatematicaImprecise probabilityAristotelian syllogismFigure III050105 experimental psychologyConstraint (information theory)Premise0202 electrical engineering electronic engineering information engineeringImprecise probability020201 artificial intelligence & image processing0501 psychology and cognitive sciencesConditional eventDefault reasoningCoherenceSentenceMathematics
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Conjunction and Disjunction Among Conditional Events

2017

We generalize, in the setting of coherence, the notions of conjunction and disjunction of two conditional events to the case of n conditional events. Given a prevision assessment on the conjunction of two conditional events, we study the set of coherent extensions for the probabilities of the two conditional events. Then, we introduce by a progressive procedure the notions of conjunction and disjunction for n conditional events. Moreover, by defining the negation of conjunction and of disjunction, we show that De Morgan’s Laws still hold. We also show that the associative and commutative properties are satisfied. Finally, we examine in detail the conjunction for a family \(\mathcal F\) of t…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaComputer scienceConditional events · Conditional random quantities · Con- junction · Disjunction · Negation · Quasi conjunction · Coherent previ- sion assessments · Coherent extensions · De Morgan’s Laws02 engineering and technologyCoherence (philosophical gambling strategy)Settore MAT/01 - Logica Matematica01 natural sciencesDe Morgan's lawsConjunction (grammar)Set (abstract data type)010104 statistics & probabilitysymbols.namesakeNegation0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processing0101 mathematicsAlgorithmCommutative propertyAssociative propertyEvent (probability theory)
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Conditional Random Quantities and Compounds of Conditionals

2013

In this paper we consider finite conditional random quantities and conditional previsions assessments in the setting of coherence. We use a suitable representation for conditional random quantities; in particular the indicator of a conditional event $E|H$ is looked at as a three-valued quantity with values 1, or 0, or $p$, where $p$ is the probability of $E|H$. We introduce a notion of iterated conditional random quantity of the form $(X|H)|K$ defined as a suitable conditional random quantity, which coincides with $X|HK$ when $H \subseteq K$. Based on a recent paper by S. Kaufmann, we introduce a notion of conjunction of two conditional events and then we analyze it in the setting of cohere…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaLogicImport–Export principleProbability (math.PR)Probabilistic logicConjunctionOf the formSettore M-FIL/02 - Logica E Filosofia Della ScienzaCoherence (philosophical gambling strategy)Conditional random quantitieConjunction (grammar)Lower/upper prevision boundsHistory and Philosophy of ScienceNegationIterated functionIterated conditioningFOS: MathematicsConditional eventRepresentation (mathematics)CoherenceDisjunctionMathematics - ProbabilityMathematicsEvent (probability theory)
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Compound conditionals, Fr\'echet-Hoeffding bounds, and Frank t-norms

2021

Abstract In this paper we consider compound conditionals, Frechet-Hoeffding bounds and the probabilistic interpretation of Frank t-norms. By studying the solvability of suitable linear systems, we show under logical independence the sharpness of the Frechet-Hoeffding bounds for the prevision of conjunctions and disjunctions of n conditional events. In addition, we illustrate some details in the case of three conditional events. We study the set of all coherent prevision assessments on a family containing n conditional events and their conjunction, by verifying that it is convex. We discuss the case where the prevision of conjunctions is assessed by Lukasiewicz t-norms and we give explicit s…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaLogical independenceFrank t-normsApplied MathematicsLinear systemProbabilistic logicRegular polygon02 engineering and technologyConjunction and disjunctionConditional previsionTheoretical Computer ScienceConvexityFréchet-Hoeffding boundArtificial Intelligence020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPairwise comparisonCoherenceSoftwareMathematics - ProbabilityCounterexampleMathematicsCorresponding conditional
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Generalized probabilistic modus ponens

2017

Modus ponens (from A and “if A then C” infer C) is one of the most basic inference rules. The probabilistic modus ponens allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e., from P(A) and P(C|A) infer P(C)). In this paper, we generalize the probabilistic modus ponens by replacing A by the conditional event A|H. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic modus ponens coincide with the re…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaProbabilistic logicConjoined conditionalPrevision0102 computer and information sciences02 engineering and technologyCoherence (philosophical gambling strategy)Settore MAT/01 - Logica MatematicaModus ponen01 natural sciencesConditional random quantitieTheoretical Computer ScienceModus ponendo tollens010201 computation theory & mathematicsIterated functionComputer Science0202 electrical engineering electronic engineering information engineeringIterated conditional020201 artificial intelligence & image processingRule of inferenceModus ponensCoherenceEvent (probability theory)Mathematics
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Conditional Random Quantities and Iterated Conditioning in the Setting of Coherence

2013

We consider conditional random quantities (c.r.q.’s) in the setting of coherence. Given a numerical r.q. X and a non impossible event H, based on betting scheme we represent the c.r.q. X|H as the unconditional r.q. XH + μH c , where μ is the prevision assessed for X|H. We develop some elements for an algebra of c.r.q.’s, by giving a condition under which two c.r.q.’s X|H and Y|K coincide. We show that X|HK coincides with a suitable c.r.q. Y|K and we apply this representation to Bayesian updating of probabilities, by also deepening some aspects of Bayes’ formula. Then, we introduce a notion of iterated c.r.q. (X|H)|K, by analyzing its relationship with X|HK. Our notion of iterated conditiona…

Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica MatematicaSettore INF/01 - Informaticaconditional random quantitiesCoherence (statistics)Bayesian inferencebayesian updatingcoherenceCombinatoricsconditional previsionsBayes' theoremIterated functionbayesian updating; conditional random quantities; betting scheme; conditional previsions; coherence; iterated conditioning; iterated conditioning.Coherence betting scheme conditional random quantities conditional previsions Bayesian updating iterated conditioning.Scheme (mathematics)iterated conditioningConditioningRepresentation (mathematics)betting schemeEvent (probability theory)Mathematics
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On approximation of a class of stochastic integrals and interpolation

2004

Given a diffusion Y = (Y_{t})_{t \in [0,T]} we give different equivalent conditions so that a stochastic integral has an L 2-approximation rate of n −η, {\rm \eta \in (0,1/2],} if one approximates by integrals over piece-wise constant integrands where equidistant time nets of cardinality n + 1 are used. In particular, we obtain assertions in terms of smoothness properties of g(Y T ) in the sense of Malliavin calculus. After optimizing over non-equidistant time-nets of cardinality n + 1 in case {\rm \eta > 0} , it turns out that one always obtains a rate of n^{ - 1/2}, which is optimal. This applies to all functions g obtained in an appropriate way by the real interpolation method between th…

Discrete mathematicsSobolev spaceSmoothness (probability theory)CardinalityRate of convergenceEquidistantConstant (mathematics)Malliavin calculusInterpolationMathematicsStochastics and Stochastic Reports
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Axiomatic characterization of the weighted solidarity values

2014

Abstract We define and characterize the class of all weighted solidarity values . Our first characterization employs the classical axioms determining the solidarity value (except symmetry ), that is, efficiency , additivity and the A-null player axiom , and two new axioms called proportionality and strong individual rationality . In our second axiomatization, the additivity and the A-null player axioms are replaced by a new axiom called average marginality .

Discrete mathematicsSociology and Political ScienceAxiom independenceGeneral Social SciencesProportionality (mathematics)RationalitySolidarityEconomia Aspectes psicològicsAxiom of extensionalityMathematics::LogicEconomia matemàticaAdditive functionStatistics Probability and UncertaintyMathematical economicsGeneral PsychologyAxiomMathematics
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QUANTITATIVE CONVERGENCE RATES FOR SUBGEOMETRIC MARKOV CHAINS

2015

We provide explicit expressions for the constants involved in the characterisation of ergodicity of subgeometric Markov chains. The constants are determined in terms of those appearing in the assumed drift and one-step minorisation conditions. The results are fundamental for the study of some algorithms where uniform bounds for these constants are needed for a family of Markov kernels. Our results accommodate also some classes of inhomogeneous chains.

Discrete mathematicsStatistics and ProbabilityMarkov chain mixing timeMarkov chainVariable-order Markov modelGeneral Mathematicsta111Markov chain010102 general mathematicsErgodicity01 natural sciencesInhomogeneous010104 statistics & probability60J05Polynomial ergodicitySubgeometric ergodicityConvergence (routing)60J22Examples of Markov chainsStatistical physics0101 mathematicsStatistics Probability and UncertaintyMathematics
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