Search results for "Continuous-time stochastic process"

showing 10 items of 15 documents

The interrelation between stochastic differential inclusions and set-valued stochastic differential equations

2013

Abstract In this paper we connect the well established theory of stochastic differential inclusions with a new theory of set-valued stochastic differential equations. Solutions to the latter equations are understood as continuous mappings taking on their values in the hyperspace of nonempty, bounded, convex and closed subsets of the space L 2 consisting of square integrable random vectors. We show that for the solution X to a set-valued stochastic differential equation corresponding to a stochastic differential inclusion, there exists a solution x for this inclusion that is a ‖ ⋅ ‖ L 2 -continuous selection of X . This result enables us to draw inferences about the reachable sets of solutio…

Continuous-time stochastic processApplied MathematicsMathematical analysisStochastic calculusMalliavin calculusStochastic partial differential equationsymbols.namesakeStochastic differential equationDifferential inclusionRunge–Kutta methodsymbolsApplied mathematicsAnalysisMathematicsAlgebraic differential equationJournal of Mathematical Analysis and Applications
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A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

2014

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization-without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environme…

Continuous-time stochastic processMathematical optimizationOptimization problemControlled random walkTime reversibilityDiscretized learning02 engineering and technologyTime reversibilityLearning automataStochastic-point problem0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringStochastic neural networkMathematicsBinary treeLearning automata020206 networking & telecommunicationsRandom walkComputer Science ApplicationsHuman-Computer InteractionControl and Systems Engineering020201 artificial intelligence & image processingStochastic optimizationSoftwareInformation Systems
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Representation of Strongly Stationary Stochastic Processes

1993

A generalization of the orthogonality conditions for a stochastic process to represent strongly stationary processes up to a fixed order is presented. The particular case of non-normal delta correlated processes, and the probabilistic characterization of linear systems subjected to strongly stationary stochastic processes are also discussed.

Continuous-time stochastic processMathematical optimizationStochastic processGeneralizationMechanical EngineeringLinear systemStationary sequenceCondensed Matter PhysicsOrthogonalityMechanics of MaterialsLocal timeStatistical physicsGauss–Markov processMathematicsJournal of Applied Mechanics
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Role of conditional probability in multiscale stationary markovian processes.

2010

The aim of the paper is to understand how the inclusion of more and more time-scales into a stochastic stationary Markovian process affects its conditional probability. To this end, we consider two Gaussian processes: (i) a short-range correlated process with an infinite set of time-scales bounded from below, and (ii) a power-law correlated process with an infinite and unbounded set of time-scales. For these processes we investigate the equal position conditional probability P(x,t|x,0) and the mean First Passage Time T(L). The function P(x,t|x,0) can be considered as a proxy of the persistence, i.e. the fact that when a process reaches a position x then it spends some time around that posit…

Continuous-time stochastic processPure mathematicsStationary processStationary distributionStatistical Mechanics (cond-mat.stat-mech)Stochastic processStochastic ProcesseFokker-Plank EquationFOS: Physical sciencesOrnstein–Uhlenbeck processConditional probability distributionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)CombinatoricsStable processPhysics - Data Analysis Statistics and ProbabilityMarkovian processeFirst-hitting-time modelCondensed Matter - Statistical MechanicsData Analysis Statistics and Probability (physics.data-an)MathematicsPhysical review. E, Statistical, nonlinear, and soft matter physics
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Stochastic equation of population dynamics with diffusion on a domain

2003

We consider Lotka-Volterra competition model with diffusion in a territorial domain with a stochastic perturbation which represents the random variations of environment conditions. We prove the existence, the uniqueness and the positivity of the solution. Moreover, the stochastic boundedness of the solution is analized.

Continuous-time stochastic processeducation.field_of_studyCompetition modelGeneral MathematicsPopulationMathematical analysisA domainQuantitative Biology::Populations and EvolutionPerturbation (astronomy)Applied mathematicsUniquenesseducationMathematicsRendiconti del Circolo Matematico di Palermo
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The Master Equation

2009

Continuous-time stochastic processsymbols.namesakeStochastic differential equationQuantum stochastic calculusStochastic processMaster equationKinetic schemesymbolsStatistical physicsChapman–Kolmogorov equationMathematics
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Ambit processes and stochastic partial differential equations

2011

Ambit processes are general stochastic processes based on stochastic integrals with respect to Levy bases. Due to their flexible structure, they have great potential for providing realistic models for various applications such as in turbulence and finance. This papers studies the connection between ambit processes and solutions to stochastic partial differential equations. We investigate this relationship from two angles: from the Walsh theory of martingale measures and from the viewpoint of the Levy noise analysis.

Continuous-time stochastic processwhite noise analysisambit processesstochastic partial differential equationsStochastic modellingMathematical analysisStochastic calculusMalliavin calculusStochastic partial differential equationStochastic differential equationmartingale measuresMathematics::ProbabilityLocal martingaleLévy basesApplied mathematicsMartingale (probability theory)Mathematics
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On Fuzzy Stochastic Integral Equations—A Martingale Problem Approach

2011

In the paper we consider fuzzy stochastic integral equations using the methods of stochastic inclusions. The idea is to consider an associated martingale problem and its solutions in order to obtain a solution to the fuzzy stochastic equation.

Doob's martingale inequalityStratonovich integralMathematical optimizationContinuous-time stochastic processComputingMethodologies_SIMULATIONANDMODELINGMathematicsofComputing_NUMERICALANALYSISLocal martingaleMartingale difference sequenceStochastic optimizationMartingale (probability theory)Fuzzy logicMathematics
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Stability analysis for stochastic hybrid systems: A survey

2014

This survey addresses stability analysis for stochastic hybrid systems (SHS), which are dynamical systems that combine continuous change and instantaneous change and that also include random effects. We re-emphasize the common features found in most of the models that have appeared in the literature, which include stochastic switched systems, Markov jump systems, impulsive stochastic systems, switching diffusions, stochastic impulsive systems driven by renewal processes, diffusions driven by Lévy processes, piecewise-deterministic Markov processes, general stochastic hybrid systems, and stochastic hybrid inclusions. Then we review many of the stability concepts that have been studied, inclu…

Lyapunov functionLyapunov stabilityContinuous-time stochastic processLyapunov functionDynamical systems theoryStochastic differential equationMarkov chainStochastic stabilityConverse theoremStochastic hybrid systemsymbols.namesakeStochastic differential equationSettore ING-INF/04 - AutomaticaControl and Systems EngineeringControl theoryHybrid systemStability theorysymbolsSwitching diffusionStochastic optimizationElectrical and Electronic EngineeringRobustnessStochastic switched systemMathematics
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Stochastic Kinetics with Wave Nature

2003

We consider stochastic second-order partial differential equations. We indroduce a noisy non-linear wave equation and discuss its connections, in particular via the Lorentz transformation, with known stochastic models.

PhysicsStochastic partial differential equationContinuous-time stochastic processStochastic differential equationQuantum stochastic calculusStochastic modellingDifferential equationFirst-order partial differential equationStatistical and Nonlinear PhysicsStatistical physicsPhysics::Classical PhysicsCondensed Matter PhysicsHyperbolic partial differential equationModern Physics Letters B
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