Search results for "Probability and Statistics"

showing 10 items of 45 documents

Community characterization of heterogeneous complex systems

2011

We introduce an analytical statistical method to characterize the communities detected in heterogeneous complex systems. By posing a suitable null hypothesis, our method makes use of the hypergeometric distribution to assess the probability that a given property is over-expressed in the elements of a community with respect to all the elements of the investigated set. We apply our method to two specific complex networks, namely a network of world movies and a network of physics preprints. The characterization of the elements and of the communities is done in terms of languages and countries for the movie network and of journals and subject categories for papers. We find that our method is ab…

FOS: Computer and information sciencesStatistics and Probabilityrandom graphs networks statistical inference socio-economic networksPhysics - Physics and SocietyTheoretical computer scienceProperty (programming)Complex systemFOS: Physical sciencesPhysics and Society (physics.soc-ph)socio-economic networksStatistical inferenceSocial and Information Networks (cs.SI)Random graphComputer Science - Social and Information NetworksStatistical and Nonlinear PhysicsProbability and statisticsComplex networkSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Hypergeometric distributionPhysics - Data Analysis Statistics and ProbabilitynetworkStatistics Probability and UncertaintyNull hypothesisData Analysis Statistics and Probability (physics.data-an)random graphstatistical inferenceJournal of Statistical Mechanics: Theory and Experiment
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Calculus for the intermediate point associated with a mean value theorem of the integral calculus

2020

Abstract If f, g: [a, b] → 𝕉 are two continuous functions, then there exists a point c ∈ (a, b) such that ∫ a c f ( x ) d x + ( c - a ) g ( c ) = ∫ c b g ( x ) d x + ( b - c ) f ( c ) . \int_a^c {f\left(x \right)} dx + \left({c - a} \right)g\left(c \right) = \int_c^b {g\left(x \right)} dx + \left({b - c} \right)f\left(c \right). In this paper, we study the approaching of the point c towards a, when b approaches a.

Integral calculusIntermediate pointCalculusmedicineProbability and statisticsmedicine.diseaseCalculus (medicine)MathematicsMean value theoremGeneral Mathematics
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Properties of the intermediate point from a mean value theorem of the integral calculus - II

2019

Abstract In this paper we consider two continuous functions f, g : [a, b] → ℝ and we study for these ones, under which circumstances the intermediate point function is four order di erentiable at the point x = a and we calculate its derivative.

Integral calculusPure mathematicsIntermediate pointMean value theorem (divided differences)Probability and statisticsMathematicsGeneral Mathematics
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A panel cointegration approach to the estimation of the peseta real exchange rate

2001

Abstract In this paper we estimate different specifications of a model for the determination of the bilateral real exchange rate of the peseta relative to nine European Union members. The model is based on Meese and Rogoff (The Journal of Finance 43 (1988) 933) monetary approach as extended by MacDonald (Journal of International Financial Markets, Institutions and Money 8 (1998) 117). The applied econometric techniques are the recent panel cointegration tests developed by Kao (Journal of Econometrics 90 (1999) 1), McCoskey and Kao (A Monte Carlo comparison of tests for cointegration in panel data. Journal of Propagations in Probability and Statistics 1 (2001) 165) and Pedroni (Oxford Bullet…

MacroeconomicsEconomics and Econometricsreal exchange rate European Monetary Union panel cointegrationCointegrationFinancial marketMonte Carlo methodjel:F31Probability and statisticsjel:C33Exchange rateEconometricsEconomicsmedia_common.cataloged_instanceEuropean unionReal interest ratemedia_commonPanel dataJournal of Macroeconomics
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Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials.

2020

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is de…

Materials scienceSpeedupbusiness.industryMechanical EngineeringDeep learningProbability and statistics02 engineering and technology010402 general chemistry021001 nanoscience & nanotechnologyMachine learningcomputer.software_genre01 natural sciencesImaging data0104 chemical sciencesMechanics of MaterialsGeneral Materials ScienceOptical identificationArtificial intelligence0210 nano-technologybusinessTransfer of learningcomputerIntuitionAdvanced materials (Deerfield Beach, Fla.)
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2021

Fluctuation–dissipation relations or “theorems” (FDTs) are fundamental for statistical physics and can be rigorously derived for equilibrium systems. Their applicability to non-equilibrium systems is, however, debated. Here, we simulate an active microrheology experiment, in which a spherical colloid is pulled with a constant external force through a fluid, creating near-equilibrium and far-from-equilibrium systems. We characterize the structural and dynamical properties of these systems, and reconstruct an effective generalized Langevin equation (GLE) for the colloid dynamics. Specifically, we test the validity of two FDTs: The first FDT relates the non-equilibrium response of a system to …

Microrheology010304 chemical physicsFormalism (philosophy)Probability and statisticsGeneral ChemistryDissipationCondensed Matter Physics01 natural sciencesConstraint (information theory)Kernel (image processing)Orthogonality0103 physical sciencesStatistical physics010306 general physicsConstant (mathematics)MathematicsSoft Matter
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Data Blinding for the nEDM Experiment at PSI

2020

Psychological bias towards, or away from, prior measurements or theory predictions is an intrinsic threat to any data analysis. While various methods can be used to try to avoid such a bias, e.g. actively avoiding looking at the result, only data blinding is a traceable and trustworthy method that can circumvent the bias and convince a public audience that there is not even an accidental psychological bias. Data blinding is nowadays a standard practice in particle physics, but it is particularly difficult for experiments searching for the neutron electric dipole moment (nEDM), as several cross measurements, in particular of the magnetic field, create a self-consistent network into which it …

Nuclear and High Energy Physicsdata analysis methodPhysics - Instrumentation and DetectorsOffset (computer science)BlindingNeutron electric dipole momentOther Fields of PhysicsFOS: Physical sciencesSeparate analysis[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]nucl-ex01 natural sciencesHigh Energy Physics - Experimentphysics.data-anHigh Energy Physics - Experiment (hep-ex)0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Physics - Experiment[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Nuclear Experiment (nucl-ex)Detectors and Experimental Techniques010306 general physicsNuclear Experimentphysics.ins-detPhysicsn: electric moment010308 nuclear & particles physicshep-exProbability and statisticsInstrumentation and Detectors (physics.ins-det)Data setSpecial Article - New Tools and TechniquesTrustworthinessPhysics - Data Analysis Statistics and ProbabilityAlgorithmData Analysis Statistics and Probability (physics.data-an)Particle Physics - Experiment[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis Statistics and Probability [physics.data-an]
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Probability and statistics

2013

Nuclear physicsAstroparticle physicsPhysicsHeavy ionProbability and statisticsStatistical physicsSolar physicsHigh-pT Physics in the Heavy Ion Era
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Diffusive behavior and the modeling of characteristic times in limit order executions

2007

We present an empirical study of the first passage time (FPT) of order book prices needed to observe a prescribed price change Delta, the time to fill (TTF) for executed limit orders and the time to cancel (TTC) for canceled ones in a double auction market. We find that the distribution of all three quantities decays asymptotically as a power law, but that of FPT has significantly fatter tails than that of TTF. Thus a simple first passage time model cannot account for the observed TTF of limit orders. We propose that the origin of this difference is the presence of cancellations. We outline a simple model, which assumes that prices are characterized by the empirically observed distribution …

Physics - Physics and SocietyFOS: Physical sciencesPhysics and Society (physics.soc-ph)Power lawFOS: Economics and businessOrder bookTime to fillLimit (mathematics)Statistical physicsMicrostructureMathematicsQuantitative Finance - Trading and Market MicrostructureEconophysicsLimit order marketEconophysicProbability and statisticsFirst passage timeTrading and Market Microstructure (q-fin.TR)Distribution (mathematics)Physics - Data Analysis Statistics and ProbabilityExponentCensored dataFirst-hitting-time modelGeneral Economics Econometrics and FinanceFinanceData Analysis Statistics and Probability (physics.data-an)
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Goodness-of-fit tests in many dimensions

2004

A method is presented to construct goodness-of-fit statistics in many dimensions for which the distribution of all possible test results in the limit of an infinite number of data becomes Gaussian if also the number of dimensions becomes infinite. Furthermore, an explicit example is presented, for which this distribution as good as only depends on the expectation value and the variance of the statistic for any dimension larger than one.

PhysicsNuclear and High Energy PhysicsGaussianFOS: Physical sciencesProbability and statisticsVariance (accounting)Expectation valuesymbols.namesakeHigh Energy Physics - PhenomenologyHigh Energy Physics - Phenomenology (hep-ph)Goodness of fitPhysics - Data Analysis Statistics and ProbabilitysymbolsLimit (mathematics)Statistical physicsDimension (data warehouse)InstrumentationData Analysis Statistics and Probability (physics.data-an)Statistic
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