Search results for "ta111"

showing 10 items of 251 documents

Broken ray transform on a Riemann surface with a convex obstacle

2014

We consider the broken ray transform on Riemann surfaces in the presence of an obstacle, following earlier work of Mukhometov. If the surface has nonpositive curvature and the obstacle is strictly convex, we show that a function is determined by its integrals over broken geodesic rays that reflect on the boundary of the obstacle. Our proof is based on a Pestov identity with boundary terms, and it involves Jacobi fields on broken rays. We also discuss applications of the broken ray transform.

Statistics and ProbabilityMathematics - Differential GeometryGeodesicAstrophysics::High Energy Astrophysical PhenomenaBoundary (topology)Curvature01 natural sciencessymbols.namesakeMathematics - Analysis of PDEsFOS: Mathematics0101 mathematicsMathematicsRiemann surface010102 general mathematicsMathematical analysista111Regular polygonSurface (topology)boundary010101 applied mathematicsDifferential Geometry (math.DG)Obstaclesymbolstensor tomographyGeometry and TopologyStatistics Probability and UncertaintydimensionsConvex functionAnalysisAnalysis of PDEs (math.AP)
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Deflation-based separation of uncorrelated stationary time series

2014

In this paper we assume that the observed pp time series are linear combinations of pp latent uncorrelated weakly stationary time series. The problem is then to find an estimate for an unmixing matrix that transforms the observed time series back to uncorrelated time series. The so called SOBI (Second Order Blind Identification) estimate aims at a joint diagonalization of the covariance matrix and several autocovariance matrices with varying lags. In this paper, we propose a novel procedure that extracts the latent time series one by one. The limiting distribution of this deflation-based SOBI is found under general conditions, and we show how the results can be used for the comparison of es…

Statistics and ProbabilityNumerical Analysista112Series (mathematics)matematiikkaCovariance matrixaikasarjatmathematicsta111Asymptotic distributionDeflationBlind signal separationAutocovarianceMatrix (mathematics)StatisticsApplied mathematicsStatistics Probability and Uncertaintytime seriesLinear combinationMathematicsJournal of Multivariate Analysis
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Genericity of dimension drop on self-affine sets

2017

We prove that generically, for a self-affine set in $\mathbb{R}^d$, removing one of the affine maps which defines the set results in a strict reduction of the Hausdorff dimension. This gives a partial positive answer to a folklore open question.

Statistics and ProbabilityPure mathematicsthermodynamic formalismDynamical Systems (math.DS)01 natural sciencesself-affine setsingular value functionAffine combinationAffine hullClassical Analysis and ODEs (math.CA)FOS: MathematicsMathematics - Dynamical Systems0101 mathematicsMathematicsDiscrete mathematicsta111010102 general mathematicsMinkowski–Bouligand dimensionproducts of matricesEffective dimension010101 applied mathematicsAffine coordinate systemMathematics - Classical Analysis and ODEsHausdorff dimensionAffine transformationStatistics Probability and UncertaintyStatistics & Probability Letters
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Hard-Core Thinnings of Germ‒Grain Models with Power-Law Grain Sizes

2013

Random sets with long-range dependence can be generated using a Boolean model with power-law grain sizes. We study thinnings of such Boolean models which have the hard-core property that no grains overlap in the resulting germ‒grain model. A fundamental question is whether long-range dependence is preserved under such thinnings. To answer this question, we study four natural thinnings of a Poisson germ‒grain model where the grains are spheres with a regularly varying size distribution. We show that a thinning which favors large grains preserves the slow correlation decay of the original model, whereas a thinning which favors small grains does not. Our most interesting finding concerns the c…

Statistics and ProbabilityRegular variationDisjoint sets02 engineering and technologyPoisson distribution60D05 60G55Power law01 natural sciencesmarked Poisson processsymbols.namesake010104 statistics & probabilityFOS: Mathematics0202 electrical engineering electronic engineering information engineeringgerm‒grain modelGermStatistical physics60D050101 mathematicsMathematicsta115ta114ThinningBoolean modelApplied MathematicsProbability (math.PR)ta111Boolean model020206 networking & telecommunicationsHard sphereshard-core modelsymbolsSPHERES60G55hard-sphere modelMathematics - ProbabilityAdvances in Applied Probability
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An Adaptive Parallel Tempering Algorithm

2013

Parallel tempering is a generic Markov chainMonteCarlo samplingmethod which allows good mixing with multimodal target distributions, where conventionalMetropolis- Hastings algorithms often fail. The mixing properties of the sampler depend strongly on the choice of tuning parameters, such as the temperature schedule and the proposal distribution used for local exploration. We propose an adaptive algorithm with fixed number of temperatures which tunes both the temperature schedule and the parameters of the random-walk Metropolis kernel automatically. We prove the convergence of the adaptation and a strong law of large numbers for the algorithm under general conditions. We also prove as a side…

Statistics and ProbabilityScheduleMathematical optimizationta112Adaptive algorithmErgodicityta111Mixing (mathematics)Law of large numbersKernel (statistics)Convergence (routing)Discrete Mathematics and CombinatoricsParallel temperingStatistics Probability and UncertaintyAlgorithmMathematicsJournal of Computational and Graphical Statistics
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On the stability and ergodicity of adaptive scaling Metropolis algorithms

2011

The stability and ergodicity properties of two adaptive random walk Metropolis algorithms are considered. The both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted scaling parameter is not constrained within a predefined compact interval. The first algorithm is based on scale adaptation only, while the second one incorporates also covariance adaptation. A strong law of large numbers is shown to hold assuming that the target density is smooth enough and has either compact support or super-exponentially decaying tails.

Statistics and ProbabilityStochastic approximationMathematics - Statistics TheoryStatistics Theory (math.ST)Law of large numbersMultiple-try Metropolis01 natural sciencesStability (probability)010104 statistics & probabilityModelling and Simulation65C40 60J27 93E15 93E35Adaptive Markov chain Monte CarloFOS: Mathematics0101 mathematicsScalingMetropolis algorithmMathematicsta112Applied Mathematics010102 general mathematicsRejection samplingErgodicityProbability (math.PR)ta111CovarianceRandom walkMetropolis–Hastings algorithmModeling and SimulationAlgorithmStabilityMathematics - ProbabilityStochastic 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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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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Juggler's exclusion process

2012

Juggler's exclusion process describes a system of particles on the positive integers where particles drift down to zero at unit speed. After a particle hits zero, it jumps into a randomly chosen unoccupied site. We model the system as a set-valued Markov process and show that the process is ergodic if the family of jump height distributions is uniformly integrable. In a special case where the particles jump according to a set-avoiding memoryless distribution, the process reaches its equilibrium in finite nonrandom time, and the equilibrium distribution can be represented as a Gibbs measure conforming to a linear gravitational potential.

Statistics and Probabilityset-valued Markov processmaximum entropy60K35 82C41General Mathematics82C41FOS: Physical sciencesMarkov process01 natural sciencespositive recurrencesymbols.namesakeGravitational potentialMarkov renewal process0103 physical sciencesjuggling patternFOS: MathematicsErgodic theory0101 mathematicsGibbs measureMathematical PhysicsMathematicsDiscrete mathematicsnoncolliding random walkProbability (math.PR)ta111010102 general mathematicsErgodicityMathematical analysisExclusion processMathematical Physics (math-ph)Gibbs measureDistribution (mathematics)set-avoiding memoryless distribution60K35Jumpsymbolsergodicity010307 mathematical physicsStatistics Probability and UncertaintyMathematics - Probability
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On the range of the attenuated ray transform for unitary connections

2013

We describe the range of the attenuated ray transform of a unitary connection on a simple surface acting on functions and 1-forms. We use this to determine the range of the ray transform acting on symmetric tensor fields.

Surface (mathematics)Mathematics - Differential Geometryray transformGeneral MathematicsAstrophysics::High Energy Astrophysical PhenomenaMathematical analysista111Unitary stateConnection (mathematics)Range (mathematics)Mathematics - Analysis of PDEsDifferential Geometry (math.DG)Simple (abstract algebra)Quantum mechanicsFOS: MathematicsSymmetric tensorAnalysis of PDEs (math.AP)Mathematics
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