Search results for "102"

showing 10 items of 2892 documents

Uniform measure density condition and game regularity for tug-of-war games

2018

We show that a uniform measure density condition implies game regularity for all 2 < p < ∞ in a stochastic game called “tug-of-war with noise”. The proof utilizes suitable choices of strategies combined with estimates for the associated stopping times and density estimates for the sum of independent and identically distributed random vectors. peerReviewed

Statistics and ProbabilityIndependent and identically distributed random variablesComputer Science::Computer Science and Game Theorygame regularitydensity estimate for the sum of i.i.d. random vectorsTug of war01 natural sciencesMeasure (mathematics)$p$-regularityMathematics - Analysis of PDEsFOS: MathematicsApplied mathematicspeliteoriastochastic games0101 mathematics91A15 60G50 35J92Mathematicsp-harmonic functionsstokastiset prosessit$p$-harmonic functionsosittaisdifferentiaaliyhtälöthitting probability010102 general mathematicsStochastic gametug-of-war gamesProbability (math.PR)uniform measure density condition010101 applied mathematicsNoiseuniform distribution in a ballMathematics - ProbabilityAnalysis of PDEs (math.AP)
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Ergodicity and limit theorems for degenerate diffusions with time periodic drift. Application to a stochastic Hodgkin−Huxley model

2016

We formulate simple criteria for positive Harris recurrence of strongly degenerate stochastic differential equations with smooth coefficients on a state space with certain boundary conditions. The drift depends on time and space and is periodic in the time argument. There is no time dependence in the diffusion coefficient. Control systems play a key role, and we prove a new localized version of the support theorem. Beyond existence of some Lyapunov function, we only need one attainable inner point of full weak Hoermander dimension. Our motivation comes from a stochastic Hodgkin−Huxley model for a spiking neuron including its dendritic input. This input carries some deterministic periodic si…

Statistics and ProbabilityLyapunov function010102 general mathematicsErgodicityDegenerate energy levels01 natural sciencesPeriodic function010104 statistics & probabilitysymbols.namesakeStochastic differential equationsymbolsState spaceApplied mathematicsLimit (mathematics)0101 mathematicsBrownian motionMathematicsESAIM: Probability and Statistics
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Recursive estimation of the conditional geometric median in Hilbert spaces

2012

International audience; A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirm…

Statistics and ProbabilityMallows-Wasserstein distanceRobbins-Monroasymptotic normalityCLTcentral limit theoremAsymptotic distributionMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMallows–Wasserstein distanceonline data010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]60F05FOS: MathematicsApplied mathematics[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematics62L20MathematicsaveragingSequential estimation010102 general mathematicsEstimatorRobbins–MonroConditional probability distribution[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]Geometric medianstochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]robust estimatorRate of convergenceConvergence of random variablesStochastic gradient.kernel regressionsequential estimationKernel regressionStatistics Probability and Uncertainty
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Pairwise Markov properties for regression graphs

2016

With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of conditional distributions, named regressions in joint responses. The involved random variables may be discrete, continuous or of both types. Such a generating process specifies for each response a conditioning set that contains just its regressor variables, and it leads to at least one valid ordering of all nodes in the corresponding regression graph that has three types of edge: one for undirected dependences among context variables, another for undirect…

Statistics and ProbabilityMarkov chain010102 general mathematicsMixed graphConditional probability distribution01 natural sciencesCombinatorics010104 statistics & probabilityConditional independenceJoint probability distributionMarkov property0101 mathematicsStatistics Probability and UncertaintyMarginal distributionRandom variableMathematicsStat
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Estimation of orientation characteristic of fibrous material

2001

A new statistical method for estimating the orientation distribution of fibres in a fibre process is suggested where the process is observed in the form of a degraded digital greyscale image. The method is based on line transect sampling of the image in a few fixed directions. A well-known method based on stereology is available if the intersections between the transects and fibres can be counted. We extend this to the case where, instead of the intersection points, only scaled variograms of grey levels along the transects are observed. The nonlinear estimation equations for a parametric orientation distribution as well as a numerical algorithm are given. The method is illustrated by a real…

Statistics and ProbabilityMathematical optimizationOrientation (computer vision)Applied Mathematics010102 general mathematicsGeometry01 natural sciencesGrayscaleIntersection (Euclidean geometry)010104 statistics & probabilityNonlinear systemDigital imageLine (geometry)0101 mathematicsTransectMathematicsParametric statisticsAdvances in Applied Probability
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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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Central Limit Theorem for Linear Eigenvalue Statistics for a Tensor Product Version of Sample Covariance Matrices

2017

For $$k,m,n\in {\mathbb {N}}$$ , we consider $$n^k\times n^k$$ random matrices of the form $$\begin{aligned} {\mathcal {M}}_{n,m,k}({\mathbf {y}})=\sum _{\alpha =1}^m\tau _\alpha {Y_\alpha }Y_\alpha ^T,\quad {Y}_\alpha ={\mathbf {y}}_\alpha ^{(1)}\otimes \cdots \otimes {\mathbf {y}}_\alpha ^{(k)}, \end{aligned}$$ where $$\tau _{\alpha }$$ , $$\alpha \in [m]$$ , are real numbers and $${\mathbf {y}}_\alpha ^{(j)}$$ , $$\alpha \in [m]$$ , $$j\in [k]$$ , are i.i.d. copies of a normalized isotropic random vector $${\mathbf {y}}\in {\mathbb {R}}^n$$ . For every fixed $$k\ge 1$$ , if the Normalized Counting Measures of $$\{\tau _{\alpha }\}_{\alpha }$$ converge weakly as $$m,n\rightarrow \infty $$…

Statistics and ProbabilityMathematics(all)Multivariate random variableGeneral Mathematics010102 general mathematicslinear eigenvalue statisticsrandom matrices01 natural sciencesSample mean and sample covariance010104 statistics & probabilityDistribution (mathematics)Tensor productStatisticssample covariance matricescentral Limit Theorem0101 mathematicsStatistics Probability and UncertaintyRandom matrixEigenvalues and eigenvectorsMathematicsReal numberCentral limit theoremJournal of Theoretical Probability
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Three-qutrit entanglement and simple singularities

2016

In this paper, we use singularity theory to study the entanglement nature of pure three-qutrit systems. We first consider the algebraic variety $X$ of separable three-qutrit states within the projective Hilbert space $\mathbb{P}(\mathcal{H}) = \mathbb{P}^{26}$. Given a quantum pure state $|\varphi\rangle\in \mathbb{P}(\mathcal{H})$ we define the $X_\varphi$-hypersuface by cutting $X$ with a hyperplane $H_\varphi$ defined by the linear form $\langle\varphi|$ (the $X_\varphi$-hypersurface of $X$ is $X\cap H_\varphi \subset X$). We prove that when $|\varphi\rangle$ ranges over the SLOCC entanglement classes, the "worst" possible singular $X_\varphi$-hypersuface with isolated singularities, has…

Statistics and ProbabilityMathematics::Functional AnalysisQuantum PhysicsPure mathematicsSingularity theory010102 general mathematicsGeneral Physics and AstronomyStatistical and Nonlinear PhysicsAlgebraic varietyQuantum PhysicsQuantum entanglementSingular point of a curve01 natural sciencesMathematics - Algebraic GeometryHypersurfaceHyperplaneModeling and Simulation0103 physical sciencesProjective Hilbert space0101 mathematicsQutrit010306 general physicsMathematical PhysicsMathematicsJournal of Physics A: Mathematical and Theoretical
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Self-stabilizing processes: uniqueness problem for stationary measures and convergence rate in the small-noise limit

2011

In the context of self-stabilizing processes, that is processes attracted by their own law, living in a potential landscape, we investigate different properties of the invariant measures. The interaction between the process and its law leads to nonlinear stochastic differential equations. In [S. Herrmann and J. Tugaut. Electron. J. Probab. 15 (2010) 2087–2116], the authors proved that, for linear interaction and under suitable conditions, there exists a unique symmetric limit measure associated to the set of invariant measures in the small-noise limit. The aim of this study is essentially to point out that this statement leads to the existence, as the noise intensity is small, of one unique…

Statistics and ProbabilityMcKean-Vlasov equationLaplace transformdouble-well potential010102 general mathematicsMathematical analysisFixed-point theoremfixed point theoremDouble-well potentialInvariant (physics)01 natural sciencesself-interacting diffusionuniqueness problem[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010104 statistics & probabilityRate of convergenceLaplace's methodUniquenessInvariant measureperturbed dynamical systemstationary measures0101 mathematicsLaplace's methodprimary 60G10; secondary: 60J60 60H10 41A60Mathematics
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Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers

2018

We establish quantitative bounds for rates of convergence and asymptotic variances for iterated conditional sequential Monte Carlo (i-cSMC) Markov chains and associated particle Gibbs samplers. Our main findings are that the essential boundedness of potential functions associated with the i-cSMC algorithm provide necessary and sufficient conditions for the uniform ergodicity of the i-cSMC Markov chain, as well as quantitative bounds on its (uniformly geometric) rate of convergence. Furthermore, we show that the i-cSMC Markov chain cannot even be geometrically ergodic if this essential boundedness does not hold in many applications of interest. Our sufficiency and quantitative bounds rely on…

Statistics and ProbabilityMetropoliswithin-Gibbsgeometric ergodicity01 natural sciencesCombinatorics010104 statistics & probabilitysymbols.namesakeFOS: MathematicsMetropolis-within-GibbsApplied mathematicsErgodic theory0101 mathematicsGibbs measureQAMathematics65C40 (Primary) 60J05 65C05 (Secondary)Particle GibbsMarkov chainGeometric ergodicity010102 general mathematicsErgodicityuniform ergodicityProbability (math.PR)iterated conditional sequential Monte CarloMarkov chain Monte CarloIterated conditional sequential Monte CarloRate of convergencesymbolsUniform ergodicityparticle GibbsParticle filterMathematics - ProbabilityGibbs sampling
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