Search results for "Random"

showing 10 items of 3931 documents

Solving chance constrained optimal control problems in aerospace via Kernel Density Estimation

2017

International audience; The goal of this paper is to show how non-parametric statistics can be used to solve some chance constrained optimization and optimal control problems. We use the Kernel Density Estimation method to approximate the probability density function of a random variable with unknown distribution , from a relatively small sample. We then show how this technique can be applied and implemented for a class of problems including the God-dard problem and the trajectory optimization of an Ariane 5-like launcher.

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC]Mathematical optimizationControl and Optimizationchance constrained optimizationKernel density estimation0211 other engineering and technologiesProbability density function02 engineering and technology01 natural sciencesKernel Density Estimation010104 statistics & probability0101 mathematicsMathematics021103 operations researchApplied MathematicsConstrained optimizationTrajectory optimizationstochastic optimizationOptimal controlOptimal controlDistribution (mathematics)Aerospace engineeringControl and Systems EngineeringStochastic optimization[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Random variableSoftware
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Movement patterns of Tenebrio beetles demonstrate empirically that correlated-random-walks have similitude with a Lévy walk.

2013

AbstractCorrelated random walks are the dominant conceptual framework for modelling and interpreting organism movement patterns. Recent years have witnessed a stream of high profile publications reporting that many organisms perform Lévy walks; movement patterns that seemingly stand apart from the correlated random walk paradigm because they are discrete and scale-free rather than continuous and scale-finite. Our new study of the movement patterns of Tenebriomolitor beetles in unchanging, featureless arenas provides the first empirical support for a remarkable and deep theoretical synthesis that unites correlated random walks and Lévy walks. It demonstrates that the two models are complemen…

[ SDE.BE ] Environmental Sciences/Biodiversity and EcologyMultidisciplinarycoleopteraMovement (music)Computer scienceMovementRandom walkModels BiologicalArticleSimilitudemodèle biologiqueMovement patternLévy flight[ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology environment/SymbiosisAnimalsStatistical physics[SDE.BE]Environmental Sciences/Biodiversity and EcologyTenebrioAlgorithmsécologie comportementale[SDV.EE.IEO]Life Sciences [q-bio]/Ecology environment/Symbiosis
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Whole mirror duplication-random loss model and pattern avoiding permutations

2010

International audience; In this paper we study the problem of the whole mirror duplication-random loss model in terms of pattern avoiding permutations. We prove that the class of permutations obtained with this model after a given number p of duplications of the identity is the class of permutations avoiding the alternating permutations of length p2+1. We also compute the number of duplications necessary and sufficient to obtain any permutation of length n. We provide two efficient algorithms to reconstitute a possible scenario of whole mirror duplications from identity to any permutation of length n. One of them uses the well-known binary reflected Gray code (Gray, 1953). Other relative mo…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]Class (set theory)0206 medical engineeringBinary number0102 computer and information sciences02 engineering and technology[ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]01 natural sciencesIdentity (music)Combinatorial problemsTheoretical Computer ScienceGray codeCombinatoricsPermutation[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]Gene duplicationRandom loss[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]Pattern avoiding permutationGenerating algorithmComputingMilieux_MISCELLANEOUSMathematicsDiscrete mathematicsWhole duplication-random loss modelMathematics::CombinatoricsGenomeParity of a permutationComputer Science Applications[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO][ INFO.INFO-CC ] Computer Science [cs]/Computational Complexity [cs.CC]Binary reflected Gray code010201 computation theory & mathematicsSignal Processing[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]020602 bioinformaticsAlgorithmsInformation Systems
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Probability and algorithmics: a focus on some recent developments

2017

Jean-François Coeurjolly, Adeline Leclercq-Samson Eds.; International audience; This article presents different recent theoretical results illustrating the interactions between probability and algorithmics. These contributions deal with various topics: cellular automata and calculability, variable length Markov chains and persistent random walks, perfect sampling via coupling from the past. All of them involve discrete dynamics on complex random structures.; Cet article présente différents résultats récents de nature théorique illustrant les interactions entre probabilités et algorithmique. Ces contributions traitent de sujets variés : automates cellulaires et calculabilité, chaînes de Mark…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]T57-57.97Focus (computing)Applied mathematics. Quantitative methodsTheoretical computer scienceMarkov chainComputer science[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]Variable lengthRandom walkCellular automaton[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Perfect sampling[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Coupling from the past[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Algorithmics[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]QA1-939Mathematics
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Synchronization and fluctuations for interacting stochastic systems with individual and collective reinforcement

2020

The Pólya urn is the paradigmatic example of a reinforced stochastic process. It leads to a random (non degenerated) time-limit. The Friedman urn is a natural generalization whose a.s. time-limit is not random anymore. In this work, in the stream of previous recent works, we introduce a new family of (finite) systems of reinforced stochastic processes, interacting through an additional collective reinforcement of mean field type. The two reinforcement rules strengths (one componentwise, one collective) are tuned through (possibly) different rates n −γ. In the case the reinforcement rates are like n −1 , these reinforcements are of Pólya or Friedman type as in urn contexts and may thus lead …

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Interacting random systemssynchronisation[MATH] Mathematics [math]Almost sure convergenceReinforced stochastic processes[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]62P35Secondary 62L2060F05Central limit theoremsFluctuationsFluctuations MSC2010 Classification Primary 60K3560F15[MATH]Mathematics [math]stable convergence
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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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Variable Length Markov Chains, Persistent Random Walks: a close encounter

2020

This is the story of the encounter between two worlds: the world of random walks and the world of Variable Length Markov Chains (VLMC). The meeting point turns around the semi-Markov property of underlying processes.

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Property (philosophy)Markov chain010102 general mathematicsProbability (math.PR)Close encounterVariable lengthRandom walk01 natural sciences[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010104 statistics & probabilityFOS: MathematicsPoint (geometry)Statistical physics0101 mathematicsMathematics - ProbabilityMathematics
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Persistent random walks, variable length Markov chains and piecewise deterministic Markov processes *

2013

A classical random walk $(S_t, t\in\mathbb{N})$ is defined by $S_t:=\displaystyle\sum_{n=0}^t X_n$, where $(X_n)$ are i.i.d. When the increments $(X_n)_{n\in\mathbb{N}}$ are a one-order Markov chain, a short memory is introduced in the dynamics of $(S_t)$. This so-called "persistent" random walk is nolonger Markovian and, under suitable conditions, the rescaled process converges towards the integrated telegraph noise (ITN) as the time-scale and space-scale parameters tend to zero (see Herrmann and Vallois, 2010; Tapiero-Vallois, Tapiero-Vallois2}). The ITN process is effectively non-Markovian too. The aim is to consider persistent random walks $(S_t)$ whose increments are Markov chains with…

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Variable length Markov chainProbability (math.PR)Semi Markov processesIntegrated telegraph noise[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Mathematics::ProbabilitySimple and double infinite combs.Variable memoryFOS: Mathematics[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilityPersistent random walkSimple and double infinite combsPiecewise Deterministic Markov Processes
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Cut-off method for endogeny of recursive tree processes

2016

Given a solution to a recursive distributional equation, a natural (and non-trivial) question is whether the corresponding recursive tree process is endogenous. That is, whether the random environment almost surely defines the tree process. We propose a new method of proving endogeny, which applies to various processes. As explicit examples, we establish endogeny of the random metrics on non-pivotal hierarchical graphs defined by multiplicative cascades and of mean-field optimization problems as the mean-field matching and travelling salesman problems in pseudo-dimension q>1.

[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]endogenyrandom metrics[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Primary 60E05 60B10 60E15. Secondary 81T20 82B44 90C27Probability (math.PR)FOS: Mathematics60E05 60B10 60E15 81T20 82B44 90C27recursive distributional equationsmean-field combinatorial optimization[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
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All-Optical Measurement of Background, Amplitude and Timing Jitter for high speed pulse trains or prbs sequences using autocorrelation function

2006

We present a simple method for all-optical measurements of background, amplitude- and timing-jitter of ultra high speed pulse trains or prbs sequences using the jitter dependences of the intercorrelation-peak shape.

[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics]Optical fiber02 engineering and technology01 natural sciencesPseudorandom binary sequencelaw.invention010309 optics020210 optoelectronics & photonicsOpticsHardware_GENERALlawComputer Science::Multimedia0103 physical sciencesHardware_INTEGRATEDCIRCUITS0202 electrical engineering electronic engineering information engineeringComputingMilieux_MISCELLANEOUSJitterPhysics[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]Pulse (signal processing)business.industryAutocorrelationComputer Science::PerformanceAmplitudePhysics::Accelerator PhysicsTrainbusinessDegradation (telecommunications)
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