Search results for "Probability Distribution"

showing 10 items of 263 documents

Exploring the uncertainty in capacity estimation at roundabouts

2017

Abstract Purpose In gap-acceptance theory the critical and the follow-up headways have a significant role in determining roundabout entry capacities which in turn depend on circulating flow rates under a specified arrival headway distribution. Calculation considers single mean values of the gap-acceptance parameters, neglecting the inherent variations in these random variables and providing a single value of entry capacity. The purpose of this paper is to derive the entry capacity distribution which accounts for the variations of the contributing (random) variables and suggest how to consider this issue in the operational analysis of the roundabouts. Methods We performed a Monte Carlo simul…

EngineeringRoundaboutMonte Carlo methodTransportationProbability density function010501 environmental sciences01 natural sciencesGap-acceptance parameter0502 economics and businessHeadwayStatisticsOperationsSettore ICAR/04 - Strade Ferrovie Ed AeroportiPoint estimationSimulation0105 earth and related environmental sciences050210 logistics & transportationbusiness.industryMechanical Engineering05 social sciencesEntry capacityUncertaintylcsh:TA1001-1280lcsh:HE1-9990Distribution (mathematics)Roundabout entry capacity gap-acceptance parameter operations uncertaintyAutomotive EngineeringRoundaboutProbability distributionlcsh:Transportation engineeringlcsh:Transportation and communicationsbusinessRandom variableEuropean Transport Research Review
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Uncertainty assessment of a model for biological nitrogen and phosphorus removal: Application to a large wastewater treatment plant

2012

Abstract In the last few years, the use of mathematical models in WasteWater Treatment Plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data-gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only few studies have been carried out in the wastewater treatment field, and those studies only included a few of …

EngineeringSettore ICAR/03 - Ingegneria Sanitaria-AmbientaleMathematical modelbusiness.industryNitrogen phosphorus removalMonte Carlo methodUncertainty analysiEnvironmental engineeringWastewater modellingGeophysicsGeochemistry and PetrologyData qualityCalibrationProbability distributionBiochemical engineeringUncertainty quantificationGLUEbusinessActivated-sludge modelReliability (statistics)Uncertainty analysisPhysics and Chemistry of the Earth, Parts A/B/C
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Comparison of the Rain Flow Algorithm and the Spectral Method for Fatigue Life Determination Under Uniaxial and Multiaxial Random Loading

2008

This paper presents the strain energy density parameter used for fatigue life calculation under random loading by two methods. The first method is based on schematization of energy parameter histories with the rain flow algorithm. The other one is based on moments of the power spectral density function of the energy parameter. The experimental data of fatigue tests of 10HNAP steel under constant amplitude and random uniaxial loading with non-gaussion probability distribution, zero mean value, and wide-band frequency spectrum used for comparison of the rain flow algorithm and the spectral method gave satisfactory results. Next, histories of the random stress tensor with normal probability di…

Environmental EngineeringMaterials scienceCauchy stress tensorPublic Health Environmental and Occupational HealthGeneral EngineeringBiaxial tensile testSpectral densityStrain energy density functionNormal distributionNuclear Energy and EngineeringProbability distributionGeneral Materials ScienceSpectral methodAlgorithmVibration fatigueJournal of ASTM International
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Backwards Martingales and Exchangeability

2020

With many data acquisitions, such as telephone surveys, the order in which the data come does not matter. Mathematically, we say that a family of random variables is exchangeable if the joint distribution does not change under finite permutations. De Finetti’s structural theorem says that an infinite family of E-valued exchangeable random variables can be described by a two-stage experiment. At the first stage, a probability distribution Ξ on E is drawn at random. At the second stage, independent and identically distributed random variables with distribution Ξ are implemented.

Exchangeable random variablesDiscrete mathematicsIndependent and identically distributed random variablesDistribution (number theory)Conditional independenceJoint probability distributionProbability distributionConditional probability distributionRandom variableMathematics
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A probabilistic estimation and prediction technique for dynamic continuous social science models: The evolution of the attitude of the Basque Country…

2015

In this paper, a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences is presented.Considering data from surveys,the method consists of determining the probability distribution of the survey output and this allows to sample data and fit the model to the sampled data using a goodness-of-fit criterion based the χ2-test. Taking the fitted parameters that were not rejected by the χ2-test, substituting them into the model and computing their outputs, 95% confidence intervals in each time instant capturing the uncertainty of the survey data (probabilistic estimation) is built. Using the same set of obtained model parameters, a prediction over …

FOS: Computer and information sciencesAttitude dynamicsProbabilistic predictionComputer sciencePopulationDivergence-from-randomness modelSample (statistics)computer.software_genreMachine Learning (cs.LG)Probabilistic estimationSocial scienceeducationProbabilistic relevance modeleducation.field_of_studyApplied MathematicsProbabilistic logicConfidence intervalComputer Science - LearningComputational MathematicsSocial dynamic modelsProbability distributionSurvey data collectionData miningMATEMATICA APLICADAcomputerApplied Mathematics and Computation
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Order-distance and other metric-like functions on jointly distributed random variables

2013

We construct a class of real-valued nonnegative binary functions on a set of jointly distributed random variables, which satisfy the triangle inequality and vanish at identical arguments (pseudo-quasi-metrics). These functions are useful in dealing with the problem of selective probabilistic causality encountered in behavioral sciences and in quantum physics. The problem reduces to that of ascertaining the existence of a joint distribution for a set of variables with known distributions of certain subsets of this set. Any violation of the triangle inequality or its consequences by one of our functions when applied to such a set rules out the existence of this joint distribution. We focus on…

FOS: Computer and information sciencesMeasurable functionComputer Science - Artificial IntelligenceGeneral MathematicsMathematics - Statistics TheoryStatistics Theory (math.ST)Quantitative Biology - Quantitative Methods01 natural sciences050105 experimental psychologyJoint probability distribution0103 physical sciencesFOS: Mathematics0501 psychology and cognitive sciences010306 general physicsQuantitative Methods (q-bio.QM)60B99 (Primary) 81Q99 91E45 (Secondary)Probability measureMathematicsDiscrete mathematicsTriangle inequalityApplied MathematicsProbability (math.PR)05 social sciencesFunction (mathematics)Artificial Intelligence (cs.AI)Distribution (mathematics)FOS: Biological sciencesSample spaceRandom variableMathematics - ProbabilityProceedings of the American Mathematical Society
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

2021

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…

FOS: Computer and information sciencesStatistics and ProbabilityComputer Science - Machine LearningcausalityComputer Science - Artificial IntelligenceHeuristic (computer science)Computer scienceeducationMachine Learning (stat.ML)transportabilitycomputer.software_genre01 natural sciencesMachine Learning (cs.LG)R-kielimissing dataQA76.75-76.765; QA273-280010104 statistics & probabilitydo-calculuscausality; do-calculus; selection bias; transportability; missing data; case-control design; meta-analysisStatistics - Machine LearningSearch algorithmselection bias0101 mathematicsParametric statisticspäättelymeta-analyysicase-control designhakualgoritmit113 Computer and information sciencesMissing datameta-analysisIdentification (information)Artificial Intelligence (cs.AI)Causal inferencekausaliteettiIdentifiabilityProbability distributionData miningStatistics Probability and UncertaintycomputerSoftwareJournal of Statistical Software
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A study on the degree of relationship between two individuals.

2000

The paper studies the likely degree of relationship between two individuals who could possibly be half sibs. The possible common ancestor was dead, which further complicated the problem. The model used was devised by Thompson [in Rao and Chakraborty (eds): Handbook of Statistics, North-Holland, Amsterdam, 1991] and establishes a correspondence between the possible degree of relationship and certain feasible probability distributions on the number of identical by descent genes. Two statistical approaches are considered: the classical one, in which the maximum likelihood estimation for the parameters of Thompson’s model are obtained, and the Bayesian one, in which the test of the hypothesis o…

Family HealthLikelihood FunctionsDegree (graph theory)GenotypeModels GeneticMaximum likelihoodBayesian probabilityBayes TheoremIdentity by descentPhenotypeRobustness (computer science)StatisticsHalf sibsGeneticsProbability distributionHumansMonte Carlo MethodGenetics (clinical)MathematicsHuman heredity
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TREEZZY2, a Fuzzy Logic Computer Code for Fault Tree and Event Tree Analyses

2004

In conventional approach to reliability analysis using logical trees methodologies, uncertainties in system components or basic events failure probabilities are approached by assuming probability distribution functions. However, data are often insufficient for statistical estimation, and therefore it is required to resort to approximate estimations. Moreover, complicate calculations are needed to propagate uncertainties up to the final results. In our work, in order to take account of the uncertainties in system failure probabilities, the methodology based on fuzzy sets theory is used both in fault tree and event tree analyses. This paper just presents our work in this issue, which resulted…

Fault tree analysisEvent treeIncremental decision treeTree (data structure)Computer scienceEvent tree analysisFuzzy setProbability distributionData miningcomputer.software_genreFuzzy logiccomputerAlgorithm
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Quantum inductive inference by finite automata

2008

AbstractFreivalds and Smith [R. Freivalds, C.H. Smith Memory limited inductive inference machines, Springer Lecture Notes in Computer Science 621 (1992) 19–29] proved that probabilistic limited memory inductive inference machines can learn with probability 1 certain classes of total recursive functions, which cannot be learned by deterministic limited memory inductive inference machines. We introduce quantum limited memory inductive inference machines as quantum finite automata acting as inductive inference machines. These machines, we show, can learn classes of total recursive functions not learnable by any deterministic, nor even by probabilistic, limited memory inductive inference machin…

Finite-state machineGeneral Computer Sciencebusiness.industryProbabilistic logicInductive inferenceInductive reasoningAutomataTheoretical Computer ScienceAutomatonTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESQuantum computationLearningQuantum finite automataProbability distributionArtificial intelligencebusinessQuantumComputer Science(all)Quantum computerMathematicsTheoretical Computer Science
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