Search results for "Mathematica"

showing 10 items of 7971 documents

Lyapunov exponent and topological entropy plateaus in piecewise linear maps

2013

We consider a two-parameter family of piecewise linear maps in which the moduli of the two slopes take different values. We provide numerical evidence of the existence of some parameter regions in which the Lyapunov exponent and the topological entropy remain constant. Analytical proof of this phenomenon is also given for certain cases. Surprisingly however, the systems with that property are not conjugate as we prove by using kneading theory.

Statistics and ProbabilityMathematical analysisGeneral Physics and AstronomyStatistical and Nonlinear PhysicsTopological entropyLyapunov exponentTopological entropy in physicsModuliPiecewise linear functionsymbols.namesakeModeling and SimulationsymbolsConstant (mathematics)Mathematical PhysicsMathematicsJournal of Physics A: Mathematical and Theoretical
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Fractal eigenstates in disordered systems

1990

Abstract The wave functions of the non-interacting electrons in disordered systems described by a tight-binding model with site-diagonal disorder are investigated by means of the inverse participation ratio. The wave functions are shown to be fractal objects. In three-dimensional samples, a critical fractal dimension can be defined for the mobility edge in the band centre, which yields the mobility edge trajectory in the whole energy range in good agreement with previous calculations based on the investigation of the exponentially decaying transmission coefficient.

Statistics and ProbabilityMathematical analysisInverseElectronCondensed Matter PhysicsFractal dimensionsymbols.namesakeFractalFractal derivativesymbolsTransmission coefficientStatistical physicsWave functionHamiltonian (quantum mechanics)MathematicsPhysica A: Statistical Mechanics and its Applications
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Statistics of return times for weighted maps of the interval

2000

For non markovian, piecewise monotonic maps of the interval associated to a potential, we prove that the law of the entrance time in a cylinder, when renormalized by the measure of the cylinder, converges to an exponential law for almost all cylinders. Thanks to this result, we prove that the fluctuations of Rn, first return time in a cylinder, are lognormal.

Statistics and ProbabilityMathematical analysisMarkov processMonotonic functionCylinder (engine)law.inventionPhysics::Fluid DynamicsReturn timesymbols.namesakelawLog-normal distributionPiecewisesymbolsStatistics Probability and UncertaintyExponential lawMathematicsAnnales de l'Institut Henri Poincare (B) Probability and Statistics
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Mass-flux-based outlet boundary conditions for the lattice Boltzmann method

2009

We present outlet boundary conditions for the lattice Boltzmann method. These boundary conditions are constructed with a mass-flux-based approach. Conceptually, the mass-flux-based approach provides a mathematical framework from which specific boundary conditions can be derived by enforcing given physical conditions. The object here is, in particular, to explain the mass-flux-based approach. Furthermore, we illustrate, transparently, how boundary conditions can be derived from the emerging mathematical framework. For this purpose, we derive and present explicitly three outlet boundary conditions. By construction, these boundary conditions have an apparent physical interpretation which is fu…

Statistics and ProbabilityMathematical analysisMason–Weaver equationBoundary conformal field theoryStatistical and Nonlinear PhysicsDifferent types of boundary conditions in fluid dynamicsSingular boundary methodBoundary knot methodBoundary conditions in CFDFree boundary problemBoundary value problemStatistical physicsStatistics Probability and UncertaintyMathematicsJournal of Statistical Mechanics: Theory and Experiment
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Componentwise adaptation for high dimensional MCMC

2005

We introduce a new adaptive MCMC algorithm, based on the traditional single component Metropolis-Hastings algorithm and on our earlier adaptive Metropolis algorithm (AM). In the new algorithm the adaption is performed component by component. The chain is no more Markovian, but it remains ergodic. The algorithm is demonstrated to work well in varying test cases up to 1000 dimensions.

Statistics and ProbabilityMathematical optimization010504 meteorology & atmospheric sciencesMonte Carlo methodMarkov processMarkov chain Monte Carlo01 natural sciencesStatistics::Computation010104 statistics & probabilityComputational Mathematicssymbols.namesakeMetropolis–Hastings algorithmTest caseChain (algebraic topology)Component (UML)symbolsStatistics::MethodologyErgodic theory0101 mathematicsStatistics Probability and Uncertainty0105 earth and related environmental sciencesMathematicsComputational Statistics
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Including covariates in a space-time point process with application to seismicity

2020

AbstractThe paper proposes a spatio-temporal process that improves the assessment of events in space and time, considering a contagion model (branching process) within a regression-like framework to take covariates into account. The proposed approach develops the forward likelihood for prediction method for estimating the ETAS model, including covariates in the model specification of the epidemic component. A simulation study is carried out for analysing the misspecification model effect under several scenarios. Also an application to the Italian seismic catalogue is reported, together with the reference to the developed R package.

Statistics and ProbabilityMathematical optimization010504 meteorology & atmospheric sciencesSpacetimeComputer scienceSpace timeSpace-time point processes ETAS model R package for seismic datacovariatesProcess (computing)01 natural sciencesPoint process010104 statistics & probabilitySpecificationComponent (UML)Covariate0101 mathematicsStatistics Probability and Uncertainty0105 earth and related environmental sciencesBranching process
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Elasticity function of a discrete random variable and its properties

2017

ABSTRACTElasticity (or elasticity function) is a new concept that allows us to characterize the probability distribution of any random variable in the same way as characteristic functions and hazard and reverse hazard functions do. Initially defined for continuous variables, it was necessary to extend the definition of elasticity and study its properties in the case of discrete variables. A first attempt to define discrete elasticity is seen in Veres-Ferrer and Pavia (2014a). This paper develops this definition and makes a comparative study of its properties, relating them to the properties shown by discrete hazard and reverse hazard, as both defined in Chechile (2011). Similar to continuou…

Statistics and ProbabilityMathematical optimization021103 operations researchDiscretizationHazard ratio0211 other engineering and technologies02 engineering and technology01 natural sciencesElasticity of a functionContinuous variable010104 statistics & probabilityApplied mathematicsProbability distribution0101 mathematicsElasticity (economics)Random variableMathematicsCommunications in Statistics - Theory and Methods
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Criteria for Bayesian model choice with application to variable selection

2012

In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first formalize the most general and compelling of the various criteria that have been suggested, together with a new criterion. We then illustrate the potential of these criteria in determining objective model selection priors by considering their application to the problem of variable selection in normal linear models. This results in a new model selection objective prior with a number of compelling properties.

Statistics and ProbabilityMathematical optimization62C10Model selectiong-priorLinear modelMathematics - Statistics TheoryFeature selectionStatistics Theory (math.ST)Model selectionBayesian inferenceObjective model62J05Prior probability62J15FOS: MathematicsStatistics Probability and Uncertaintyobjective BayesSelection (genetic algorithm)variable selectionMathematicsThe Annals of Statistics
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Exponential and bayesian conjugate families: Review and extensions

1997

The notion of a conjugate family of distributions plays a very important role in the Bayesian approach to parametric inference. One of the main features of such a family is that it is closed under sampling, but a conjugate family often provides prior distributions which are tractable in various other respects. This paper is concerned with the properties of conjugate families for exponential family models. Special attention is given to the class of natural exponential families having a quadratic variance function, for which the theory is particularly fruitful. Several classes of conjugate families have been considered in the literature and here we describe some of their most interesting feat…

Statistics and ProbabilityMathematical optimizationClass (set theory)Exponential familyQuadratic equationBayesian probabilityApplied mathematicsStatistics Probability and UncertaintyBayesian inferenceExponential functionConjugateVariance functionMathematicsTest
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Spatio-temporal stochastic modelling: environmental and health processes

2010

Guest editorial

Statistics and ProbabilityMathematical optimizationComputer scienceStochastic modellingEcological Modeling
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