Search results for "Statistica"

showing 10 items of 5969 documents

On the first- and second-order statistics of the capacity of N*Nakagami-m channels for applications in cooperative networks

2012

This article deals with the derivation and analysis of the statistical properties of the instantaneous channel capacitya of N*Nakagami-m channels, which has been recently introduced as a suitable stochastic model for multihop fading channels. We have derived exact analytical expressions for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades (ADF) of the instantaneous channel capacity of N*Nakagami-m channels. For large number of hops, we have studied the first-order statistics of the instantaneous channel capacity by assuming that the fading amplitude of the channel can approximately be modeled as a lognor…

Computer Networks and CommunicationsStochastic modellingComputer scienceCumulative distribution functionNakagami distributionComputer Science ApplicationsComputer Science::PerformanceChannel capacitySignal ProcessingLog-normal distributionStatisticsComputer Science::Networking and Internet ArchitectureFadingStatistical physicsComputer Science::Information TheoryCommunication channelEURASIP Journal on Wireless Communications and Networking
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Support Vector Machine and Kernel Classification Algorithms

2018

This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the successful binary SVM. Next, it introduces more advanced topics in SVM for classification, including large margin filtering (LMF), SSL, active learning, and large‐scale classification using SVMs. The LMF method performs both signal filtering and classification simultaneously by learning the most appropriate filters. SSL with SVMs exploits the information contained in both labeled and unlabeled e…

Computer Science::Machine LearningOptimization problemActive learning (machine learning)business.industryComputer scienceBinary numberPattern recognitionSupport vector machineStatistical classificationComputingMethodologies_PATTERNRECOGNITIONMargin (machine learning)Kernel (statistics)Pattern recognition (psychology)Artificial intelligencebusiness
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Thermodynamics of the Classical Planar Ferromagnet Close to the Zero-Temperature Critical Point: A Many-Body Approach

2012

We explore the low-temperature thermodynamic properties and crossovers of ad-dimensional classical planar Heisenberg ferromagnet in a longitudinal magnetic field close to its field-induced zero-temperature critical point by employing the two-time Green’s function formalism in classical statistical mechanics. By means of a classical Callen-like method for the magnetization and the Tyablikov-like decoupling procedure, we obtain, for anyd, a low-temperature critical scenario which is quite similar to the one found for the quantum counterpart. Remarkably, ford>2the discrimination between the two cases is found to be related to the different values of the shift exponent which governs the beha…

Computer Science::Machine LearningPhysicsArticle SubjectCondensed matter physicsThermodynamicsStatistical mechanicsCondensed Matter PhysicsComputer Science::Digital Librarieslcsh:QC1-999Statistics::Machine LearningReduced propertiesCritical point (thermodynamics)Critical lineComputer Science::Mathematical SoftwareExponentCritical exponentQuantumlcsh:PhysicsPhase diagramAdvances in Condensed Matter Physics
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Upport vector machines for nonlinear kernel ARMA system identification.

2006

Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…

Computer Science::Machine LearningStatistics::TheoryComputer Networks and CommunicationsBiomedical signal processingInformation Storage and RetrievalMachine learningcomputer.software_genrePattern Recognition AutomatedStatistics::Machine LearningArtificial IntelligenceApplied mathematicsStatistics::MethodologyAutoregressive–moving-average modelComputer SimulationMathematicsTelecomunicacionesHardware_MEMORYSTRUCTURESSupport vector machinesModels StatisticalNonlinear system identificationbusiness.industryAutocorrelationSystem identificationSignal Processing Computer-AssistedGeneral MedicineComputer Science ApplicationsSupport vector machineNonlinear systemKernelAutoregressive modelNonlinear DynamicsARMA modelling3325 Tecnología de las TelecomunicacionesArtificial intelligenceNeural Networks ComputerbusinesscomputerSoftwareAlgorithmsReproducing kernel Hilbert spaceIEEE transactions on neural networks
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Kernel manifold alignment for domain adaptation

2016

The wealth of sensory data coming from different modalities has opened numerous opportu- nities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. How- ever, multimodal architectures must rely on models able to adapt to changes in the data dis- tribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation proble…

Computer and Information SciencesKernel FunctionsInformation Storage and RetrievalSocial Scienceslcsh:Medicine1100 General Agricultural and Biological SciencesResearch and Analysis MethodsInfographicsTopologyPattern Recognition AutomatedKernel MethodsCognitionLearning and MemoryMemory1300 General Biochemistry Genetics and Molecular BiologyImage Interpretation Computer-AssistedData MiningHumansPsychologyLife Science910 Geography & travelOperator TheoryManifoldslcsh:ScienceObject Recognition1000 MultidisciplinaryApplied MathematicsSimulation and ModelingData Visualizationlcsh:RCognitive PsychologyBiology and Life SciencesEigenvaluesFacial ExpressionAlgebra10122 Institute of GeographyLinear AlgebraData Interpretation StatisticalPhysical SciencesCognitive SciencePerceptionlcsh:QEigenvectorsGraphsAlgorithmsMathematicsResearch ArticleNeuroscience
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Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate va…

2017

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear funct…

Computer and Information SciencesStatistical methodsConfidence Intervals; Humans; Monte Carlo Method; Regression Analysis; Heart Rate; Biochemistry Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)EntropyCardiologylcsh:MedicineResearch and Analysis MethodsSystems ScienceRegression AnalysiHeart RateConfidence IntervalsMedicine and Health SciencesHumanslcsh:ScienceBiochemistry Genetics and Molecular Biology (all)Simulation and ModelingPhysicslcsh:RProbability TheoryMonte Carlo methodAgricultural and Biological Sciences (all)Nonlinear DynamicsWhite NoiseSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPhysical SciencesSignal ProcessingMathematical and statistical techniquesThermodynamicsEngineering and TechnologyRegression Analysislcsh:QConfidence IntervalMathematicsStatistics (Mathematics)HumanResearch ArticleStatistical DistributionsPLoS ONE
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Unreliable predictions about COVID‐19 infections and hospitalizations make people worry: The case of Italy

2021

Computer modeling &ltmedicine.medical_specialty2019-20 coronavirus outbreakCoronavirus disease 2019 (COVID-19)BioinformaticsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)media_common.quotation_subjectcomputer modeling < biostatistics & bioinformatics; epidemiology; statistical inference < biostatistics & bioinformaticsMEDLINEVirologycomputer modeling < biostatistics & bioinformaticsEpidemiologyHumansMedicineLetters to the EditorIntensive care medicineLetter to the Editormedia_commonSARS-CoV-2business.industryCommunicationBiostatistics &ampCOVID-19Computer modeling &lt; Biostatistics &amp; Bioinformaticsstatistical inference < biostatistics & bioinformaticsVirologyInfectious DiseasesItalyStatistical inference &lt; Biostatistics &amp; BioinformaticsepidemiologyWorrySettore SECS-S/01businessForecastingJournal of Medical Virology
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An Estimative Model of Automated Valuation Method in Italy

2017

The Automated Valuation Method (AVM) is a computer software program that analyzes data using an automated process. It is related to the process of appraising an universe of real estate properties, using common data and standard appraisal methodologies. Generally, the AVM is based on quantitative models (statistical, mathematical, econometric, etc.), related to the valuation of the properties gathered in homogeneous groups (by use and location) for which are collected samples of market data. The real estate data are collected regularly and systematically. Within the AVM, the proposed valuation scheme is an uniequational model to value properties in terms of widespread availability of sample …

Computer science0211 other engineering and technologies021107 urban & regional planningReal estateStatistical model02 engineering and technologyMarket segmentationHomogeneousAVM Valuation Market segment Appraisal functionLinear form021105 building & constructionComputer softwareMarket dataEconometricsSettore ICAR/22 - EstimoOperations managementValuation (finance)
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Using Applications and Tools to Visualize ab initio Calculations Performed in VASP

2018

Visualization of the results of the ab initio calculations is important for the analysis of these results. It improves the quality of the analysis by supplementing the plain numbers received as the output of the calculations with various graphical images and facilitates the analysis of the results. In addition to that visualization helps avoiding some mistakes or inconsistencies. Various tools have been used in this work to construct the unit cell models of the calculated lattices, to check and analyze the calculated lattice structure before and after the relaxation, to plot total and difference electron charge density maps.

Computer scienceAb initio quantum chemistry methodsRelaxation (approximation)Statistical physicsElectric chargePlot (graphics)Visualization
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Revisitation of Nonorthogonal Spin Adaptation in Coupled Cluster Theory.

2015

The benefits of what is alternatively called a nonorthogonally spin-adapted, spin-free, or orbital representation of the coupled cluster equations is discussed relative to orthogonally spin-adapted, spin-orbital, and spin-integrated theories. In particular, specific linear combinations of the orbital cluster amplitudes, denoted spin-summed amplitudes, are shown to reduce the number of contractions that must be explicitly performed and to simplify the expressions and their derivation. The computational efficiency of the spin-summed approach is discussed and compared to orthogonally spin-adapted and spin-integrated approaches. The spin-summed approach is shown to have significant computationa…

Computer scienceAdaptation (eye)computer.software_genreComputer Science ApplicationsAmplitudeCoupled clusterCluster (physics)Condensed Matter::Strongly Correlated ElectronsData miningStatistical physicsPhysical and Theoretical ChemistryRepresentation (mathematics)Linear combinationcomputerSpin-½Journal of chemical theory and computation
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