Search results for " 65C30"

showing 4 items of 4 documents

Unbiased Estimators and Multilevel Monte Carlo

2018

Multilevel Monte Carlo (MLMC) and unbiased estimators recently proposed by McLeish (Monte Carlo Methods Appl., 2011) and Rhee and Glynn (Oper. Res., 2015) are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction…

FOS: Computer and information sciencesMonte Carlo methodWord error rate010103 numerical & computational mathematicsstochastic differential equationManagement Science and Operations ResearchStatistics - Computation01 natural sciences010104 statistics & probabilityStochastic differential equationstratificationSquare rootFOS: MathematicsApplied mathematics0101 mathematicsComputation (stat.CO)stokastiset prosessitMathematicsProbability (math.PR)ta111EstimatorVariance (accounting)unbiased estimatorsComputer Science ApplicationsMonte Carlo -menetelmät65C05 (Primary) 65C30 (Secondary)efficiencykerrostuneisuusVariance reductionunbiasemultilevel Monte CarlodifferentiaaliyhtälötMathematics - ProbabilityOperations Research
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Spatial Besov regularity for stochastic partial differential equations on Lipschitz domains

2010

We use the scale of Besov spaces B^\alpha_{\tau,\tau}(O), \alpha>0, 1/\tau=\alpha/d+1/p, p fixed, to study the spatial regularity of the solutions of linear parabolic stochastic partial differential equations on bounded Lipschitz domains O\subset R^d. The Besov smoothness determines the order of convergence that can be achieved by nonlinear approximation schemes. The proofs are based on a combination of weighted Sobolev estimates and characterizations of Besov spaces by wavelet expansions.

Mathematics::Functional AnalysisSmoothness (probability theory)General MathematicsProbability (math.PR)Mathematics::Analysis of PDEsScale (descriptive set theory)Numerical Analysis (math.NA)Lipschitz continuitySobolev spaceStochastic partial differential equation60H15 Secondary: 46E35 65C30WaveletRate of convergenceBounded functionFOS: MathematicsApplied mathematicsMathematics - Numerical AnalysisMathematics - ProbabilityMathematicsStudia Mathematica
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Statistics of nonlinear stochastic dynamical systems under Lévy noises by a convolution quadrature approach

2010

This paper describes a novel numerical approach to find the statistics of the non-stationary response of scalar non-linear systems excited by L\'evy white noises. The proposed numerical procedure relies on the introduction of an integral transform of Wiener-Hopf type into the equation governing the characteristic function. Once this equation is rewritten as partial integro-differential equation, it is then solved by applying the method of convolution quadrature originally proposed by Lubich, here extended to deal with this particular integral transform. The proposed approach is relevant for two reasons: 1) Statistics of systems with several different drift terms can be handled in an efficie…

Statistics and Probability65R10 65D32 60H15 65C30PACS: 02.50.FzPartial differential equationDynamical systems theoryGeneral Physics and AstronomyStatistical and Nonlinear Physics05.45.-aWhite noise02.30.UuIntegral transformDifferential operatorFractional calculusQuadrature (mathematics)Nonlinear systemModeling and SimulationStatisticsSettore ICAR/08 - Scienza Delle CostruzioniCondensed Matter - Statistical MechanicsMathematical PhysicsMathematics
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Random walk approximation of BSDEs with H{\"o}lder continuous terminal condition

2018

In this paper, we consider the random walk approximation of the solution of a Markovian BSDE whose terminal condition is a locally Hölder continuous function of the Brownian motion. We state the rate of the L2-convergence of the approximated solution to the true one. The proof relies in part on growth and smoothness properties of the solution u of the associated PDE. Here we improve existing results by showing some properties of the second derivative of u in space. peerReviewed

Statistics and Probabilitynumerical schemeHölder conditionSpace (mathematics)01 natural sciences010104 statistics & probabilityMathematics::Probability0101 mathematicsBrownian motionrandom walk approximationSecond derivativeMathematicsstokastiset prosessitSmoothness (probability theory)numeeriset menetelmät010102 general mathematicsMathematical analysisSpeed of convergenceBackward stochastic differential equationsFunction (mathematics)State (functional analysis)Random walk[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]random walk approxi-mationbackward stochastic differential equationsspeed of convergencespeed of convergence MSC codes : 65C30 60H35 60G50 65G99Mathematics - Probability
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