Search results for "Probability bound"

showing 8 items of 8 documents

Predictive distributions that mimic frequencies over a restricted subdomain

2020

A predictive distribution over a sequence of $$N+1$$ events is said to be “frequency mimicking” whenever the probability for the final event conditioned on the outcome of the first N events equals the relative frequency of successes among them. Exchangeable distributions that exhibit this feature universally are known to have several annoying concomitant properties. We motivate frequency mimicking assertions over a limited subdomain in practical problems of finite inference, and we identify their computable coherent implications. We provide some examples using reference distributions, and we introduce computational software to generate any complete specification desired. Theorems on reducti…

A_n and H_n distributionSequenceSettore MAT/06 - Probabilita' E Statistica MatematicaComputer scienceConditional probabilityInferenceFrequencyOutcome (probability)Reduction (complexity)Distribution (mathematics)Settore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Probability elicitationExtendable exchangeabilityFeature (machine learning)Probability boundSettore SECS-S/01 - StatisticaGeneral Economics Econometrics and FinanceAlgorithmFinanceEvent (probability theory)Decisions in Economics and Finance
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Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects

2013

We make a probabilistic analysis related to some inference rules which play an important role in nonmonotonic reasoning. In a coherence-based setting, we study the extensions of a probability assessment defined on $n$ conditional events to their quasi conjunction, and by exploiting duality, to their quasi disjunction. The lower and upper bounds coincide with some well known t-norms and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are rules for which any finite family of conditional events p-entails the associated quasi conjunction and quasi disjunction. We examine some cases of logical de…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaInformation Systems and ManagementComputer Science - Artificial Intelligencet-Norms/conormDuality (mathematics)goodman-nguyen inclusion relation; lower/upper probability bounds; t-norms/conorms; generalized loop rule; coherence; quasi conjunction/disjunctionComputer Science::Artificial IntelligenceTheoretical Computer ScienceArtificial IntelligenceFOS: MathematicsProbabilistic analysis of algorithmsNon-monotonic logicRule of inferenceLower/upper probability boundGoodman–Nguyen inclusion relationMathematicsEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniDiscrete mathematicsInterpretation (logic)Probability (math.PR)Probabilistic logicCoherence (philosophical gambling strategy)Generalized Loop ruleComputer Science ApplicationsAlgebraArtificial Intelligence (cs.AI)Control and Systems EngineeringQuasi conjunction/disjunctionCoherenceMathematics - ProbabilitySoftwareInformation Sciences
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On the checking of g-coherence of conditional probability bounds

2003

We illustrate an approach to uncertain knowledge based on lower conditional probability bounds. We exploit the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), which is equivalent to the "avoiding uniform loss" property introduced by Walley for lower and upper probabilities. Based on the additive structure of random gains, we define suitable notions of non relevant gains and of basic sets of variables. Exploiting them, the linear systems in our algorithms can work with reduced sets of variables and/or constraints. In this paper, we illustrate the notions of non relevant gain and of basic set by examining several cases of imprecise assessments d…

Mathematical optimizationSettore MAT/06 - Probabilita' E Statistica MatematicaPosterior probabilityConditional probability tablealgorithmslower conditional probability boundRegular conditional probabilityalgorithms; generalized coherence; linear systems; lower conditional probability bounds; probabilistic reasoning; reduced sets of variables and constraints.Artificial Intelligencelinear systemprobabilistic reasoninggeneralized coherenceMathematicsDiscrete mathematicsreduced sets of variables and constraintsalgorithmlinear systemsProbabilistic logicLaw of total probabilityConditional probabilityCoherence (philosophical gambling strategy)Conditional probability distributionControl and Systems Engineeringlower conditional probability boundsSoftwareInformation Systems
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Coherence Checking and Propagation of Lower Probability Bounds

2003

In this paper we use imprecise probabilities, based on a concept of generalized coherence (g-coherence), for the management of uncertain knowledge and vague information. We face the problem of reducing the computational difficulties in g-coherence checking and propagation of lower conditional probability bounds. We examine a procedure, based on linear systems with a reduced number of unknowns, for the checking of g-coherence. We propose an iterative algorithm to determine the reduced linear systems. Based on the same ideas, we give an algorithm for the propagation of lower probability bounds. We also give some theoretical results that allow, by suitably modifying our algorithms, the g-coher…

Probability boxMathematical optimizationSettore MAT/06 - Probabilita' E Statistica MatematicaPosterior probabilitynon relevant gainLaw of total probabilityConditional probabilitybasic setsbasic sets; basic sets.; g-coherence checking; lower conditional probability bounds; non relevant gains; propagationCoherence (statistics)Conditional probability distributiong-coherence checking; lower conditional probability bounds; non relevant gainsImprecise probabilityTheoretical Computer Sciencelower conditional probability boundRegular conditional probabilitynon relevant gainspropagationlower conditional probability boundsGeometry and Topologyg-coherence checkingSoftwareMathematics
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Quasi conjunction and p-entailment in nonmonotonic reasoning

2010

We study, in the setting of coherence, the extension of a probability assessment defined on n conditional events to their quasi conjunction. We consider, in particular, two special cases of logical dependencies; moreover, we examine the relationship between the notion of p-entailment of Adams and the inclusion relation of Goodman and Nguyen. We also study the probabilistic semantics of the QAND rule of Dubois and Prade; then, we give a theoretical result on p-entailment.

Settore MAT/06 - Probabilita' E Statistica MatematicaProbability assessmentProbabilistic semanticsInclusion relationExtension (predicate logic)Coherence (statistics)Logical consequenceConjunction (grammar)Coherence lower/upper probability bounds quasi conjunction QAND rule p-entailmentCalculusp-entailment.; quasi conjunction; lower/upper probability bounds; qand rule; coherence; p-entailmentNon-monotonic logicAlgorithmMathematics
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Logical Conditions for Coherent Qualitative and Numerical Probability Assessments

2003

Settore MAT/06 - Probabilita' E Statistica Matematicalower and upper probability boundsUncertain knowledge coherence g-coherence imprecise probabilities conditional probability bounds lower and upper probabilities coherent qualitative probability assessmentsqualitative probabilitieslogical conditionsGeneralized coherenceGeneralized coherence; lower and upper probability bounds; logical conditions; qualitative probabilities.
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Reassessing Accuracy Rates of Median Decisions

2007

We show how Bruno de Finetti''s fundamental theorem of prevision has computable applications in statistical problems that involve only partial information. Specifically, we assess accuracy rates for median decision procedures used in the radiological diagnosis of asbestosis. Conditional exchangeability of individual radiologists'' diagnoses is recognized as more appropriate than independence which is commonly presumed. The FTP yields coherent bounds on probabilities of interest when available information is insufficient to determine a complete distribution. Further assertions that are natural to the problem motivate a partial ordering of conditional probabilities, extending the computation …

Statistics and ProbabilityFOS: Computer and information sciencesFundamental theorem of previsionComputer scienceGeneral MathematicsComputationSpecificity.Quadratic programmingStatistics - ApplicationsMedical diagnosiSensitivityLinear programmingProbability boundApplications (stat.AP)Second opinionQuadratic programmingMedical diagnosisIndependence (probability theory)Fundamental theoremAsbestosiConditional probabilityDistribution (mathematics)ExchangeabilityPredictivevalueStatistics Probability and UncertaintyPartially ordered setCoherenceMathematical economics
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Some results on generalized coherence of conditional probability bounds

2003

Based on the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), we adopt a probabilistic approach to uncertainty based on conditional probability bounds. Our notion of g-coherence is equivalent to the 'avoiding uniform loss' property for lower and upper probabilities (a la Walley). Moreover, given a g-coherent imprecise assessment by our algorithms we can correct it obtaining the associated coherent assessment (in the sense of Walley and Williams). As is well known, the problems of checking g-coherence and propagating tight g-coherent intervals are NP and FP^NP complete, respectively, and thus NP-hard. Two notions which may be helpful to reduce co…

g-coherenceUncertain knowledge; coherence; g-coherence; imprecise probabilities; conditional probability bounds; lower and upper probabilities; non relevant gains; basic sets.Settore MAT/06 - Probabilita' E Statistica Matematicanon relevant gainsUncertain knowledgeconditional probability boundslower and upper probabilitiesbasic setsimprecise probabilitiesUncertain knowledge coherence g-coherence imprecise probabilities conditional probability bounds lower and upper probabilities non relevant gains basic setscoherence
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