Search results for "Probability Theory"

showing 10 items of 269 documents

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)
researchProduct

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
researchProduct

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
researchProduct

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
researchProduct

An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management

2017

We present an agent based model of the Air Traffic Management socio-technical complex system that aims at modeling the interactions between aircrafts and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts resolution between flight trajectories can arise during the en-route phase of each flight due to both not detailed flight trajectory planning or unforeseen events that perturb the planned flight plan. Our mod…

Distribution CurvesAircraftComputer scienceVelocitylcsh:MedicineTransportation02 engineering and technologySystems ScienceAgent-Based Modeling0202 electrical engineering electronic engineering information engineeringlcsh:ScienceAgent-based modelMultidisciplinaryNegotiatingSimulation and ModelingFlight TestingPhysics05 social sciencesAir traffic managementClassical MechanicsAir traffic controlNavigationPhysical SciencesTrajectoryEngineering and Technology020201 artificial intelligence & image processingFree flightResearch ArticleStatistical DistributionsPhysics - Physics and SocietyComputer and Information SciencesReal-time computingFlight planFOS: Physical sciencesAerospace EngineeringComputerApplications_COMPUTERSINOTHERSYSTEMSPhysics and Society (physics.soc-ph)Air traffic management systemResearch and Analysis MethodsMotion0502 economics and businessHumansComputer Simulation050210 logistics & transportationlcsh:RReproducibility of ResultsModels TheoreticalProbability TheorySettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Accidents Aviationlcsh:QAir Traffic management Agent-based models socio-technical complex systemAviationAirspace classMathematicsPLOS ONE
researchProduct

Testing the Martingale Property of Exchange Rates: A Replication

2010

In this paper, we test the martingale property of a set of U.S. exchange rates already analyzed in a recent paper by Yilmaz [J. of Buss. and Ec. Stat., 2003]. We claim that the tests used by Yilmaz are not the most convenient to test the martingale hypothesis (or the equivalent martingale difference of the returns); hence, we compute a recently proposed test by Kuan and Lee [Stud. in Nonlin. Dyn. and Econ., 2004] and compare our results to Yilmaz's. Striking differences arise, which can give a clue about the type of data generating process governing the evolution of exchange rates in each sub-period.

Doob's martingale inequalityEconomics and EconometricsEconometricsApplied mathematicsMartingale difference sequenceMartingale (probability theory)Social Sciences (miscellaneous)AnalysisMathematicsStudies in Nonlinear Dynamics & Econometrics
researchProduct

On Fuzzy Stochastic Integral Equations—A Martingale Problem Approach

2011

In the paper we consider fuzzy stochastic integral equations using the methods of stochastic inclusions. The idea is to consider an associated martingale problem and its solutions in order to obtain a solution to the fuzzy stochastic equation.

Doob's martingale inequalityStratonovich integralMathematical optimizationContinuous-time stochastic processComputingMethodologies_SIMULATIONANDMODELINGMathematicsofComputing_NUMERICALANALYSISLocal martingaleMartingale difference sequenceStochastic optimizationMartingale (probability theory)Fuzzy logicMathematics
researchProduct

Martingale Convergence Theorems and Their Applications

2020

We became familiar with martingales X=(X n ) n∈N0 as fair games and found that under certain transformations (optional stopping, discrete stochastic integral) martingales turn into martingales. In this chapter, we will see that under weak conditions (non-negativity or uniform integrability) martingales converge almost surely. Furthermore, the martingale structure implies L p -convergence under assumptions that are (formally) weaker than those of Chapter 7. The basic ideas of this chapter are Doob’s inequality (Theorem 11.4) and the upcrossing inequality (Lemma 11.3).

Doob's martingale inequalityUniform integrabilityPure mathematicsDoob's martingale convergence theoremsLocal martingaleAlmost surelyMartingale (probability theory)Stock priceStochastic integralMathematics
researchProduct

Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain

2018

Dynamic representation of functional brain networks involved in the sequence analysis of functional connectivity graphs of the brain (FCGB) gains advances in uncovering evolved interaction mechanisms. However, most of the networks, even the event-related ones, are highly heterogeneous due to spurious interactions, which bring challenges to revealing the change patterns of interactive information in the complex dynamic process. In this paper, we propose a network entropy (NE) method to measure connectivity uncertainty of FCGB sequences to alleviate the spurious interaction problem in dynamic network analysis to realize associations with different events during a complex cognitive task. The p…

Dynamic network analysisComputer scienceGeneral Physics and Astronomylcsh:Astrophysicsentropiata3112Measure (mathematics)Articleevent-related analysis050105 experimental psychology03 medical and health sciences0302 clinical medicinelcsh:QB460-4660501 psychology and cognitive sciencesAdjacency matrixdriver fatiguelcsh:ScienceSpurious relationshipRepresentation (mathematics)Event (probability theory)ta113Sequencebrain networkverkkoteoria05 social sciencesnetwork entropy; connectivity; brain network; dynamic network analysis; event-related analysis; driver fatiguelcsh:QC1-999connectivityProbability distributionlcsh:Qdynamic network analysisaivotnetwork entropyAlgorithmlcsh:Physics030217 neurology & neurosurgeryEntropy; Volume 20; Issue 5; Pages: 311
researchProduct

Polar motion prediction using the combination of SSA and Copula-based analysis

2018

The real-time estimation of polar motion (PM) is needed for the navigation of Earth satellite and interplanetary spacecraft. However, it is impossible to have real-time information due to the complexity of the measurement model and data processing. Various prediction methods have been developed. However, the accuracy of PM prediction is still not satisfactory even for a few days in the future. Therefore, new techniques or a combination of the existing methods need to be investigated for improving the accuracy of the predicted PM. There is a well-introduced method called Copula, and we want to combine it with singular spectrum analysis (SSA) method for PM prediction. In this study, first, we…

Earth satellite010504 meteorology & atmospheric scienceslcsh:GeodesyPolar motion010502 geochemistry & geophysics01 natural sciencesCopula (probability theory)Prediction methodsddc:550Applied mathematicsEOPSSASingular spectrum analysis0105 earth and related environmental sciencespolar motionData processinglcsh:QB275-343Full Paperlcsh:QE1-996.5lcsh:Geography. Anthropology. RecreationGeologyInternational Earth Rotation and Reference Systems ServiceMatemática Aplicadaprediction550 Geowissenschaftenlcsh:Geologylcsh:GCopulaSpace and Planetary SciencePolar motionPredictionHybrid modelEarth, Planets and Space
researchProduct