Search results for "Applied Mathematic"

showing 10 items of 4398 documents

Main individual and product characteristics influencing in-mouth flavour release during eating masticated food products with different textures: mech…

2013

Research Areas: Life Sciences & Biomedicine - Other Topics; Mathematical & Computational Biology; A mechanistic model predicting flavour release during oral processing of masticated foods was developed. The description of main physiological steps (product mastication and swallowing) and physical mechanisms (mass transfer, product breakdown and dissolution) occurring while eating allowed satisfactory simulation of in vivo release profiles of ethyl propanoate and 2-nonanone, measured by Atmospheric Pressure Chemical Ionization Mass Spectrometry on ten representative subjects during the consumption of four cheeses with different textures. Model sensitivity analysis showed that the main paramet…

Statistics and Probability[ INFO.INFO-MO ] Computer Science [cs]/Modeling and SimulationPhysiology[ SDV.AEN ] Life Sciences [q-bio]/Food and NutritionFlavourAroma compoundMass spectrometryModels BiologicalDynamic modelMass SpectrometryGeneral Biochemistry Genetics and Molecular BiologyEatingchemistry.chemical_compound[SPI]Engineering Sciences [physics]CheeseMass transfer[ SPI ] Engineering Sciences [physics]HumansAroma compoundMass transferFood scienceParticle SizeSalivaMasticationAromaFood oral processing2. Zero hungerMass transfer coefficientMouthGeneral Immunology and MicrobiologybiologyAirApplied MathematicsSaliva ArtificialGeneral MedicineKetonesbiology.organism_classification[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationDeglutitionchemistryFoodTasteModeling and SimulationMasticationDigestionPropionatesBolus (digestion)General Agricultural and Biological Sciences[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
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Donsker-Type Theorem for BSDEs: Rate of Convergence

2019

In this paper, we study in the Markovian case the rate of convergence in Wasserstein distance when the solution to a BSDE is approximated by a solution to a BSDE driven by a scaled random walk as introduced in Briand, Delyon and Mémin (Electron. Commun. Probab. 6 (2001) Art. ID 1). This is related to the approximation of solutions to semilinear second order parabolic PDEs by solutions to their associated finite difference schemes and the speed of convergence. peerReviewed

Statistics and Probability[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Markov processType (model theory)scaled random walk01 natural sciencesconvergence rate010104 statistics & probabilitysymbols.namesakeMathematics::ProbabilityConvergence (routing)FOS: MathematicsOrder (group theory)Applied mathematicsWasserstein distance0101 mathematicsDonsker's theoremstokastiset prosessitMathematicskonvergenssiProbability (math.PR)010102 general mathematicsFinite differenceRandom walk[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Rate of convergencebackward stochastic differential equationssymbolsapproksimointiDonsker’s theoremfinite difference schemedifferentiaaliyhtälötMathematics - Probability
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A PHASE TRANSITION FOR LARGE VALUES OF BIFURCATING AUTOREGRESSIVE MODELS

2019

We describe the asymptotic behavior of the number $$Z_n[a_n,\infty )$$ of individuals with a large value in a stable bifurcating autoregressive process, where $$a_n\rightarrow \infty $$ . The study of the associated first moment is equivalent to the annealed large deviation problem of an autoregressive process in a random environment. The trajectorial behavior of $$Z_n[a_n,\infty )$$ is obtained by the study of the ancestral paths corresponding to the large deviation event together with the environment of the process. This study of large deviations of autoregressive processes in random environment is of independent interest and achieved first. The estimates for bifurcating autoregressive pr…

Statistics and Probability[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Phase transitionrandom environmentGeneral Mathematicsmedia_common.quotation_subjectmoderate deviationslimit-theoremsmarkov-chainsStatistics::Other StatisticsBranching processdeviation inequalities92D2501 natural sciencesAsymmetry010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Convergence (routing)[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]Applied mathematics60C05[MATH]Mathematics [math]0101 mathematicsautoregressive process60J20lawMathematicsBranching processmedia_commonEvent (probability theory)parametersconvergenceMarkov chain010102 general mathematics[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO][MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Large deviationslarge deviations Mathematics Subject Classification (2010): 60J8060K37Autoregressive modelcellsLarge deviations theoryStatistics Probability and Uncertaintyasymmetry60F10
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Bayesian measures of surprise for outlier detection

2003

From a Bayesian point of view, testing whether an observation is an outlier is usually reduced to a testing problem concerning a parameter of a contaminating distribution. This requires elicitation of both (i) the contaminating distribution that generates the outlier and (ii) prior distributions on its parameters. However, very little information is typically available about how the possible outlier could have been generated. Thus easy, preliminary checks in which these assessments can often be avoided may prove useful. Several such measures of surprise are derived for outlier detection in normal models. Results are applied to several examples. Default Bayes factors, where the contaminating…

Statistics and Probabilitybusiness.industryApplied MathematicsBayesian probabilityPosterior probabilityPattern recognitionBayes factorStatisticsPrior probabilityOutlierNuisance parameterAnomaly detectionArtificial intelligenceStatistics Probability and UncertaintybusinessMathematicsStatistical hypothesis testingJournal of Statistical Planning and Inference
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Resuming Shapes with Applications

2004

Many image processing tasks need some kind of average of different shapes. Frequently, different shapes obtained from several images have to be summarized. If these shapes can be considered as different realizations of a given random compact set, then the natural summaries are the different mean sets proposed in the literature. In this paper, new mean sets are defined by using the basic transformations of Mathematical Morphology (dilation, erosion, opening and closing). These new definitions can be considered, under some additional assumptions, as particular cases of the distance average of Baddeley and Molchanov. The use of the former and new mean sets as summary descriptors of shapes is i…

Statistics and Probabilitybusiness.industryApplied MathematicsNoise reductionImage processingMathematical morphologyCondensed Matter PhysicsConfidence intervalCompact spaceModeling and SimulationRandom compact setDilation (morphology)SegmentationComputer visionGeometry and TopologyComputer Vision and Pattern RecognitionArtificial intelligencebusinessAlgorithmMathematicsJournal of Mathematical Imaging and Vision
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Outlier detection with automatic modelling: TRAMO/SEATS versus X-12-ARIMA

2012

Statistics and Probabilitybusiness.industryComputer scienceApplied MathematicsModeling and SimulationPattern recognitionAnomaly detectionData miningArtificial intelligenceAutoregressive integrated moving averagecomputer.software_genrebusinesscomputerModel Assisted Statistics and Applications
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A Bayesian comparison of cluster, strata, and random samples

1999

When sampling from finite populations, simple random sampling (SRS) is rarely used in practice, due to either high cost or information to be gained from more efficient designs. Bayesian hierarchical models are a natural framework to model the non-randomness in the sample. This paper concentrates on the effects that the design has on inference about characteristics of the finite population, and makes a critical comparison among some common designs.

Statistics and Probabilityeducation.field_of_studyApplied MathematicsBayesian probabilityPopulationSampling (statistics)Sample (statistics)Simple random sampleStratified samplingsymbols.namesakeStatisticssymbolsCluster samplingStatistics Probability and UncertaintyeducationMathematicsGibbs samplingJournal of Statistical Planning and Inference
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Approachability in Population Games

2014

This paper reframes approachability theory within the context of population games. Thus, whilst one player aims at driving her average payoff to a predefined set, her opponent is not malevolent but rather extracted randomly from a population of individuals with given distribution on actions. First, convergence conditions are revisited based on the common prior on the population distribution, and we define the notion of \emph{1st-moment approachability}. Second, we develop a model of two coupled partial differential equations (PDEs) in the spirit of mean-field game theory: one describing the best-response of every player given the population distribution (this is a \emph{Hamilton-Jacobi-Bell…

Statistics and Probabilityeducation.field_of_studyComputer Science::Computer Science and Game TheoryMEAN-FIELD GAMESComputer scienceApproachabilityREGRETApplied MathematicsPopulationStochastic gameRegretContext (language use)91A13ApproachabilityEVOLUTIONComplete informationOptimization and Control (math.OC)Modeling and SimulationBest responseFOS: MathematicseducationMathematical economicsGame theoryMathematics - Optimization and Controlpopulation games
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On powerful exact nonrandomized tests for the Poisson two-sample setting.

2020

In the case of two independent samples from Poisson distributions, the natural target parameter for hypothesis testing is the ratio of the two population means. The conditional tests which have been derived for this class of problems already in the 1940s are well known to be optimal in terms of power only when randomized decisions between hypotheses are admitted at the boundary of the respective rejection regions. The major objective of this contribution is to show how the approach used by Boschloo in 1970 for constructing a powerful nonrandomized version of Fisher’s exact test for hypotheses about the odds ratio between two binomial parameters can successfully be adapted for the Poisson c…

Statistics and Probabilityeducation.field_of_studyEpidemiologyPopulationBoundary (topology)Poisson distribution01 natural sciences010104 statistics & probability03 medical and health sciencessymbols.namesakeExact test0302 clinical medicineHealth Information ManagementSample size determinationSample SizesymbolsCutoffApplied mathematics030212 general & internal medicinePoisson Distribution0101 mathematicseducationEquivalence (measure theory)Statistical hypothesis testingMathematicsStatistical methods in medical research
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Extremal polynomials in stratified groups

2018

We introduce a family of extremal polynomials associated with the prolongation of a stratified nilpotent Lie algebra. These polynomials are related to a new algebraic characterization of abnormal subriemannian geodesics in stratified nilpotent Lie groups. They satisfy a set of remarkable structure relations that are used to integrate the adjoint equations.

Statistics and Probabilityextremal polynomialsMathematics - Differential GeometryPure mathematicsGeodesicStructure (category theory)Group Theory (math.GR)Characterization (mathematics)algebra01 natural sciencesdifferentiaaligeometriaMathematics - Analysis of PDEsMathematics - Metric Geometry53C17FOS: Mathematics0101 mathematicsAlgebraic numberMathematics - Differential Geometry; Mathematics - Differential Geometry; Mathematics - Analysis of PDEs; Mathematics - Group Theory; Mathematics - Metric Geometry; Mathematics - Optimization and Control; 53C17; 49K30; 17B70Mathematics - Optimization and ControlMathematics010102 general mathematicsStatisticsta111polynomitProlongation53C17 49K30 17B70Lie groupMetric Geometry (math.MG)abnormal extremals010101 applied mathematicsNilpotent Lie algebraNilpotentsub-Riemannian geometryabnormal extremals extremal polynomials Carnot groups sub-Riemannian geometryAbnormal extremals; Carnot groups; Extremal polynomials; Sub-Riemannian geometry; Analysis; Statistics and Probability; Geometry and Topology; Statistics Probability and UncertaintyDifferential Geometry (math.DG)Optimization and Control (math.OC)Carnot groups17B70Probability and UncertaintyGeometry and TopologyStatistics Probability and UncertaintyMathematics - Group TheoryAnalysisAnalysis of PDEs (math.AP)Mathematics - Differential Geometry; Mathematics - Differential Geometry; Mathematics - Analysis of PDEs; Mathematics - Group Theory; Mathematics - Metric Geometry; Mathematics - Optimization and Control; 53C17 49K30 17B7049K30
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