Search results for "Poisson distribution"

showing 10 items of 110 documents

Conditional predictive inference for online surveillance of spatial disease incidence

2011

This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of mult…

multiple comparisonsGeorgiaIncidenceSouth Carolinalagged loss functionBayes TheoremBayesian hierarchical modelspublic health surveillanceArticleconditional predictive ordinatePopulation Surveillancespatial dataSalmonella InfectionsCluster AnalysisHumansComputer SimulationPoisson Distribution
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Stationary and Nontationary Response Probability Density Function of a Beam under Poisson White Noise

2011

In this paper an approximate explicit probability density function for the analysis of external oscillations of a linear and geometric nonlinear simply supported beam driven by random pulses is proposed. The adopted impulsive loading model is the Poisson White Noise , that is a process having Dirac’s delta occurrences with random intensity distributed in time according to Poisson’s law. The response probability density function can be obtained solving the related Kolmogorov-Feller (KF) integro-differential equation. An approximated solution, using path integral method, is derived transforming the KF equation to a first order partial differential equation. The method of characteristic is the…

symbols.namesakeCharacteristic function (probability theory)Cumulative distribution functionMathematical analysissymbolsFirst-order partial differential equationProbability distributionProbability density functionWhite noiseMoment-generating functionPoisson distributionMathematics
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The Poisson Point Process

2020

Poisson point processes can be used as a cornerstone in the construction of very different stochastic objects such as, for example, infinitely divisible distributions, Markov processes with complex dynamics, objects of stochastic geometry and so forth.

symbols.namesakeCompound Poisson distributionComputer sciencePoisson point processCompound Poisson processsymbolsMarkov processStatistical physicsFractional Poisson processLévy processStochastic geometryPoint process
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Phase retrieval of vitreous floaters: simulation experiment

2020

Knowledge of the structure of vitreous floaters is crucial to evaluate the need for surgical removal of these floaters. We simulated the phase retrieval of microstructures simulating vitreous floaters by an algorithm PhaseLift and investigate the effects of various parameters on the retrieved phase. The object under test was modulated and the coded diffraction patterns were calculated. Next, PhaseLift was used to retrieve the phase. In the current study, we simulate the effect of Gaussian and Poison noise on the phase retrieval of pure phase objects. We apply an iterative algorithm PhaseLift for phase retrieval as this algorithm requires a very few modulating masks and is able to retrieve t…

symbols.namesakeComputer scienceIterative methodGaussian noiseGaussiansymbolsPhase (waves)Shot noisePoisson distributionPhase retrievalAlgorithmNoise (electronics)Optical Design and Testing X
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Truncation, Information, and the Coefficient of Variation

1989

The Fisher information in a random sample from the truncated version of a distribution that belongs to an exponential family is compared with the Fisher information in a random sample from the un- truncated distribution. Conditions under which there is more information in the selection sample are given. Examples involving the normal and gamma distributions with various selection sets, and the zero-truncated binomial, Poisson, and negative binomial distributions are discussed. A property pertaining to the coefficient of variation of certain discrete distributions on the non-negative integers is introduced and shown to be satisfied by all binomial, Poisson, and negative binomial distributions.

symbols.namesakeExponential familyBinomial (polynomial)Negative binomial distributionsymbolsGamma distributionApplied mathematicsProbability distributionTruncation (statistics)Poisson distributionMathematicsTruncated distribution
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Non-Stationary Probabilistic Response of Linear Systems Under Non-Gaussian Input

1991

The probabilistic characterization of the response of linear systems subjected to non-normal input requires the evaluation of higher order moments than two. In order to obtain the equations governing these moments, in this paper the extension of the Ito’s differential rule for linear systems excited by non-normal delta correlated processes is presented. As an application the case of the delta correlated compound Poisson input process is treated.

symbols.namesakeGaussianLinear systemsymbolsProbabilistic logicProcess (computing)Order (ring theory)Applied mathematicsExtension (predicate logic)Differential (infinitesimal)Poisson distributionMathematics
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Modelling Systemic Cojumps with Hawkes Factor Models

2013

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.

symbols.namesakeMultivariate statisticsStock exchangeEconometricssymbolsEconomicsPoisson distributionSynchronizationTime clusteringFactor analysisSign (mathematics)SSRN Electronic Journal
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Non Linear Systems Under Complex α-Stable Le´vy White Noise

2003

The problem of predicting the response of linear and nonlinear systems under Levy white noises is examined. A method of analysis is proposed based on the observation that these processes have impulsive character, so that the methods already used for Poisson white noise or normal white noise may be also recast for Levy white noises. Since both the input and output processes have no moments of order two and higher, the response is here evaluated in terms of characteristic function.Copyright © 2003 by ASME

symbols.namesakeNonlinear systemAdditive white Gaussian noiseControl theoryStochastic resonanceGaussian noiseMathematical analysissymbolsBrownian noiseImpulsive characterWhite noisePsychologyPoisson distributionApplied Mechanics and Biomedical Technology
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Convergence of Measures

2020

One focus of probability theory is distributions that are the result of an interplay of a large number of random impacts. Often a useful approximation can be obtained by taking a limit of such distributions, for example, a limit where the number of impacts goes to infinity. With the Poisson distribution, we have encountered such a limit distribution that occurs as the number of very rare events when the number of possibilities goes to infinity (see Theorem 3.7). In many cases, it is necessary to rescale the original distributions in order to capture the behavior of the essential fluctuations, e.g., in the central limit theorem. While these theorems work with real random variables, we will a…

symbols.namesakeProbability theoryWeak convergencesymbolsLimit (mathematics)Statistical physicsPoisson distributionConvergence of measuresRandom variableBrownian motionMathematicsCentral limit theorem
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Regularity of a Degenerated Convolution Semi-Group Without to Use the Poisson Process

2011

We translate in semi-group theory our regularity result for a degenerated convolution semi-group got by the Malliavin Calculus of Bismut type for Poisson processes.

symbols.namesakePure mathematicsMathematics::ProbabilityGroup (mathematics)symbolsPoisson processType (model theory)Poisson distributionMalliavin calculusMathematicsConvolution
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