Search results for "Random walk"

showing 10 items of 132 documents

Rare events and scaling properties in field-induced anomalous dynamics

2012

We show that, in a broad class of continuous time random walks (CTRW), a small external field can turn diffusion from standard into anomalous. We illustrate our findings in a CTRW with trapping, a prototype of subdiffusion in disordered and glassy materials, and in the L\'evy walk process, which describes superdiffusion within inhomogeneous media. For both models, in the presence of an external field, rare events induce a singular behavior in the originally Gaussian displacements distribution, giving rise to power-law tails. Remarkably, in the subdiffusive CTRW, the combined effect of highly fluctuating waiting times and of a drift yields a non-Gaussian distribution characterized by long sp…

Statistics and ProbabilityField (physics)GaussianFOS: Physical sciencesQuantitative Biology::Cell Behaviorsymbols.namesaketransport processes/heat transfer (theory). diffusionRare eventsstochastic particle dynamics (theory)Statistical physicsDiffusion (business)ScalingPhysicsdiffusiondriven diffusive systems (theory)Statistical and Nonlinear PhysicsDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksRandom walkDistribution (mathematics)Lévy flighttransport processes/heat transfer (theory)symbolsdiffusion; stochastic particle dynamics (theory); driven diffusive systems (theory); transport processes/heat transfer (theory)Statistics Probability and UncertaintyStatistical and Nonlinear PhysicJournal of Statistical Mechanics: Theory and Experiment
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Multicanonical Monte Carlo simulations

1998

Canonical Monte Carlo simulations of disordered systems like spin glasses and systems undergoing first-order phase transitions are severely hampered by rare event states which lead to exponentially diverging autocorrelation times with increasing system size and hence to exponentially large statistical errors. One possibility to overcome this problem is the multicanonical reweighting method. Using standard local update algorithms it could be demonstrated that the dependence of autocorrelation times on the system size V is well described by a less divergent power law, τ∝Vα, with 1<α<3, depending on the system. After a brief review of the basic ideas, combinations of multicanonical reweighting…

Statistics and ProbabilityMultigrid methodMonte Carlo methodAutocorrelationExponentWang and Landau algorithmStatistical physicsCondensed Matter PhysicsRandom walkPower lawOrder of magnitudeMathematicsPhysica A: Statistical Mechanics and its Applications
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Random walk approach to the analytic solution of random systems with multiplicative noise—The Anderson localization problem

2006

We discuss here in detail a new analytical random walk approach to calculating the phase-diagram for spatially extended systems with multiplicative noise. We use the Anderson localization problem as an example. The transition from delocalized to localized states is treated as a generalized diffusion with a noise-induced first-order phase transition. The generalized diffusion manifests itself in the divergence of averages of wavefunctions (correlators). This divergence is controlled by the Lyapunov exponent $\gamma$, which is the inverse of the localization length, $\xi=1/\gamma$. The appearance of the generalized diffusion arises due to the instability of a fundamental mode corresponding to…

Statistics and ProbabilityPhase transitionAnderson localizationMathematical analysisFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Lyapunov exponentCondensed Matter - Disordered Systems and Neural NetworksCondensed Matter PhysicsRandom walkMultiplicative noisesymbols.namesakeBounded functionsymbolsDiffusion (business)Divergence (statistics)MathematicsPhysica A: Statistical Mechanics and its Applications
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Grover Search with Lackadaisical Quantum Walks

2015

The lazy random walk, where the walker has some probability of staying put, is a useful tool in classical algorithms. We propose a quantum analogue, the lackadaisical quantum walk, where each vertex is given $l$ self-loops, and we investigate its effects on Grover's algorithm when formulated as search for a marked vertex on the complete graph of $N$ vertices. For the discrete-time quantum walk using the phase flip coin, adding a self-loop to each vertex boosts the success probability from 1/2 to 1. Additional self-loops, however, decrease the success probability. Using instead the Ambainis, Kempe, and Rivosh (2005) coin, adding self-loops simply slows down the search. These coins also diffe…

Statistics and ProbabilityQuantum PhysicsComplete graphFOS: Physical sciencesGeneral Physics and AstronomyStatistical and Nonlinear PhysicsRandom walk01 natural sciences010305 fluids & plasmasVertex (geometry)CombinatoricsModeling and Simulation0103 physical sciencesQuantum walkQuantum Physics (quant-ph)010306 general physicsQuantumMathematical PhysicsMathematics
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Random walk networks

2004

Abstract Random Boolean networks are among the best-known systems used to model genetic networks. They show an on–off dynamics and it is easy to obtain analytical results with them. Unfortunately very few genes are strictly on–off switched. On the other hand, continuous methods are in principle more suitable to capture the real behavior of the genome, but have difficulties when trying to obtain analytical results. In this work, we introduce a new model of random discrete network: random walk networks, where the state of each gene is changed by small discrete variations, being thus a natural bridge between discrete and continuous models.

Statistics and ProbabilityRandom graphDiscrete mathematicsHeterogeneous random walk in one dimensionRandom variateStochastic simulationLoop-erased random walkRandom functionRandom elementCondensed Matter PhysicsRandom walkAlgorithmMathematicsPhysica A: Statistical Mechanics and its Applications
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On the Analysis of a Random Interleaving Walk–Jump Process with Applications to Testing

2011

Abstract Although random walks (RWs) with single-step transitions have been extensively studied for almost a century as seen in Feller (1968), problems involving the analysis of RWs that contain interleaving random steps and random “jumps” are intrinsically hard. In this article, we consider the analysis of one such fascinating RW, where every step is paired with its counterpart random jump. In addition to this RW being conceptually interesting, it has applications in testing of entities (components or personnel), where the entity is never allowed to make more than a prespecified number of consecutive failures. The article contains the analysis of the chain, some fascinating limiting proper…

Statistics and ProbabilityRandom graphDiscrete mathematicsRandom variateRandom fieldModeling and SimulationRandom compact setRandom functionRandom elementRandom permutationRandom walkAlgorithmMathematicsSequential Analysis
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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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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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Deducing self-interaction in eye movement data using sequential spatial point processes

2016

Eye movement data are outputs of an analyser tracking the gaze when a person is inspecting a scene. These kind of data are of increasing importance in scientific research as well as in applications, e.g. in marketing and man-machine interface planning. Thus the new areas of application call for advanced analysis tools. Our research objective is to suggest statistical modelling of eye movement sequences using sequential spatial point processes, which decomposes the variation in data into structural components having interpretation. We consider three elements of an eye movement sequence: heterogeneity of the target space, contextuality between subsequent movements, and time-dependent behaviou…

Statistics and ProbabilitymallintaminenFOS: Computer and information sciencesrecurrenceComputer sciencestochastic geometrylikelihoodcoverageVariation (game tree)Management Monitoring Policy and Lawheterogeneous media01 natural sciences050105 experimental psychologyPoint processMethodology (stat.ME)010104 statistics & probabilitysilmänliikkeetStatistical inference0501 psychology and cognitive sciences0101 mathematicsComputers in Earth SciencesStatistics - Methodologytietojärjestelmätstokastiset prosessitta112self-interacting random walkbusiness.industry05 social sciencesEye movementPattern recognitionStatistical modelRandom walkkatseenseurantakatseArtificial intelligenceGeometric modelingbusinessStochastic geometry
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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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