Search results for " 60J60."

showing 7 items of 7 documents

Additive functionals and push forward measures under Veretennikov's flow

2014

16 pages; In this work, we will be interested in the push forward measure $(\vf_t)_*\gamma$, where $\vf_t$ is defined by the stochastic differential equation \begin{equation*} d\vf_t(x)=dW_t + \ba(\vf_t(x))dt, \quad \vf_0(x)=x\in\mbR^m, \end{equation*} and $\gamma$ is the standard Gaussian measure. We will prove the existence of density under the hypothesis that the divergence $\div(\ba)$ is not a function, but a signed measure belonging to a Kato class; the density will be expressed with help of the additive functional associated to $\div(\ba)$.

[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010104 statistics & probability[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]010102 general mathematicsstochastic flowsAdditive functionalsmeasures in Kato class0101 mathematics01 natural sciencesAMS 2000 subject classifications. Primary 60H10; secondary 60J35 60J60.[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]
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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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Exact simulation of diffusion first exit times: algorithm acceleration

2020

In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random variables might be either momentarily unavailable or too expensive in terms of computation time. It therefore needs to be replaced by an approximation procedure. As was previously the case, the ambitious exact simulation of exit times for diffusion processes was unreachable though it concerns many applications in different fields like mathematical finance, neuroscience or reliability. The usual way to describe exit times was to use discretization schemes, that are of course approxim…

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Probability (math.PR)primary 65C05 secondary:60G40 68W20 68T05 65C20 91A60 60J60diffusion processes[MATH] Mathematics [math]Exit timeExit time Brownian motion diffusion processes rejection sampling exact simulation multi-armed bandit randomized algorithm.randomized algorithm[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]exact simulationFOS: MathematicsBrownian motionmulti-armed banditMathematics - ProbabilityRejection sampling
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Stochastic differential equations with coefficients in Sobolev spaces

2010

We consider It\^o SDE $\d X_t=\sum_{j=1}^m A_j(X_t) \d w_t^j + A_0(X_t) \d t$ on $\R^d$. The diffusion coefficients $A_1,..., A_m$ are supposed to be in the Sobolev space $W_\text{loc}^{1,p} (\R^d)$ with $p>d$, and to have linear growth; for the drift coefficient $A_0$, we consider two cases: (i) $A_0$ is continuous whose distributional divergence $\delta(A_0)$ w.r.t. the Gaussian measure $\gamma_d$ exists, (ii) $A_0$ has the Sobolev regularity $W_\text{loc}^{1,p'}$ for some $p'>1$. Assume $\int_{\R^d} \exp\big[\lambda_0\bigl(|\delta(A_0)| + \sum_{j=1}^m (|\delta(A_j)|^2 +|\nabla A_j|^2)\bigr)\big] \d\gamma_d0$, in the case (i), if the pathwise uniqueness of solutions holds, then the push-f…

Discrete mathematicsPure mathematicsOrnstein–Uhlenbeck semigroupLebesgue measureSobolev space coefficientsProbability (math.PR)Density60H10 (Primary) 34F05 (Secondary) 60J60 37C10Density estimatePathwise uniquenessGaussian measureLipschitz continuitySobolev spaceStochastic differential equationStochastic flowsGaussian measureBounded functionFOS: Mathematics: Mathematics [G03] [Physical chemical mathematical & earth Sciences]Vector fieldUniqueness: Mathématiques [G03] [Physique chimie mathématiques & sciences de la terre]AnalysisMathematics - ProbabilityMathematics
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Rough nonlocal diffusions

2019

We consider a nonlinear Fokker-Planck equation driven by a deterministic rough path which describes the conditional probability of a McKean-Vlasov diffusion with "common" noise. To study the equation we build a self-contained framework of non-linear rough integration theory which we use to study McKean-Vlasov equations perturbed by rough paths. We construct an appropriate notion of solution of the corresponding Fokker-Planck equation and prove well-posedness.

Statistics and ProbabilityRough pathApplied Mathematics60H05 60H15 60J60 35K55Probability (math.PR)Conditional probabilityMcKean-VlasovNoise (electronics)510Nonlinear systemMathematics - Analysis of PDEsRough paths60H05Modeling and Simulation35K5560H15FOS: MathematicsApplied mathematicsnon-local equationsDiffusion (business)stochastic PDEsMathematics - ProbabilityAnalysis of PDEs (math.AP)Mathematics
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Local Asymptotic Normality for Shape and Periodicity in the Drift of a Time Inhomogeneous Diffusion

2017

We consider a one-dimensional diffusion whose drift contains a deterministic periodic signal with unknown periodicity $T$ and carrying some unknown $d$-dimensional shape parameter $\theta$. We prove Local Asymptotic Normality (LAN) jointly in $\theta$ and $T$ for the statistical experiment arising from continuous observation of this diffusion. The local scale turns out to be $n^{-1/2}$ for the shape parameter and $n^{-3/2}$ for the periodicity which generalizes known results about LAN when either $\theta$ or $T$ is assumed to be known.

Statistics and ProbabilityLocal asymptotic normalityMathematical analysisLocal scale62F12 60J60020206 networking & telecommunicationsMathematics - Statistics Theory02 engineering and technologyStatistics Theory (math.ST)01 natural sciencesShape parameterPeriodic function010104 statistics & probability0202 electrical engineering electronic engineering information engineeringFOS: Mathematics0101 mathematicsDiffusion (business)Mathematics
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M/M/1 queue in two alternating environments and its heavy traffic approximation

2018

We investigate an M/M/1 queue operating in two switching environments, where the switch is governed by a two-state time-homogeneous Markov chain. This model allows to describe a system that is subject to regular operating phases alternating with anomalous working phases or random repairing periods. We first obtain the steady-state distribution of the process in terms of a generalized mixture of two geometric distributions. In the special case when only one kind of switch is allowed, we analyze the transient distribution, and investigate the busy period problem. The analysis is also performed by means of a suitable heavy-traffic approximation which leads to a continuous random process. Its d…

Partial differential equationMarkov chainDistribution (number theory)Stochastic processApplied MathematicsProbability (math.PR)010102 general mathematicsMathematical analysisM/M/1 queue60K25 60K37 60J60 60J70Heavy traffic approximation01 natural sciencesSteady-state distribution010104 statistics & probabilityDiffusion approximationFOS: MathematicsAlternating Wiener process0101 mathematicsFirst-hitting-time modelSteady-state distribution; First-passage time; Diffusion approximation; Alternating Wiener processQueueMathematics - ProbabilityAnalysisFirst-passage timeMathematicsJournal of Mathematical Analysis and Applications
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