Search results for "Applied Mathematics"

showing 10 items of 4379 documents

An extended continuous mapping theorem for outer almost sure weak convergence

2019

International audience; We prove an extended continuous mapping theorem for outer almost sure weak convergence in a metric space, a notion that is used in bootstrap empirical processes theory. Then we make use of those results to establish the consistency of several bootstrap procedures in empirical likelihood theory for functional parameters.

Statistics and ProbabilityWeak convergence010102 general mathematicsContinuous mapping theorem16. Peace & justiceEmpirical measure01 natural sciences010104 statistics & probabilityMetric spaceEmpirical likelihoodConsistency (statistics)[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Applied mathematicsStatistics::Methodology0101 mathematicsStatistics Probability and UncertaintyMathematics
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The coalescent in population models with time-inhomogeneous environment

2002

AbstractThe coalescent theory, well developed for the class of exchangeable population models with time-homogeneous reproduction law, is extended to a class of population models with time-inhomogeneous environment, where the population size is allowed to vary deterministically with time and where the distribution of the family sizes is allowed to change from generation to generation. A new class of time-inhomogeneous coalescent limit processes with simultaneous multiple mergers arises. Its distribution can be characterized in terms of product integrals.

Statistics and ProbabilityWeak convergencePopulation geneticsApplied MathematicsPopulation sizeVarying environmentPopulation geneticsProduct integralHeavy traffic approximationProduct integralStirling numbersCoalescent theoryFamily SizesDiffusion approximationPopulation modelAncestorsModelling and SimulationModeling and SimulationEconometricsQuantitative Biology::Populations and EvolutionCoalescentStatistical physicsWeak convergenceMathematicsStochastic Processes and their Applications
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Simulation of BSDEs with jumps by Wiener Chaos Expansion

2016

International audience; We present an algorithm to solve BSDEs with jumps based on Wiener Chaos Expansion and Picard's iterations. This paper extends the results given in Briand-Labart (2014) to the case of BSDEs with jumps. We get a forward scheme where the conditional expectations are easily computed thanks to chaos decomposition formulas. Concerning the error, we derive explicit bounds with respect to the number of chaos, the discretization time step and the number of Monte Carlo simulations. We also present numerical experiments. We obtain very encouraging results in terms of speed and accuracy.

Statistics and ProbabilityWiener Chaos expansionDiscretizationMonte Carlo methodTime stepConditional expectation01 natural sciences010104 statistics & probabilitybackward stochastic differential equations with jumpsFOS: MathematicsApplied mathematics60H10 60J75 60H35 65C05 65G99 60H070101 mathematicsMathematicsPolynomial chaosApplied MathematicsNumerical analysis010102 general mathematicsMathematical analysista111Probability (math.PR)numerical methodCHAOS (operating system)[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Modeling and SimulationScheme (mathematics)Mathematics - Probability
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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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