Search results for " State-space model"

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Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Graphical models for estimating dynamic networks

2012

Het bepalen van dynamische netwerken met behulp van data is een actief onderzoeksgebied, met name in de systeem biologie. Het schatten van de structuur van een netwerk heeft te maken met het bepalen van de aan of afwezigheid van een relatie tussen de hoekpunten in de graaf. Grafische modellen definiëren deze relaties via conditionele afhankelijkheid. In Gaussiaanse grafische modellen (GGM) wordt verondersteld dat de hoekpunten een normale verdeling volgen. Dit heeft grote voordelen vanwege de computationele handelbaarheid van GGM. Standaard GGM zijn echter niet bruikbaar om grote netwerken te bestuderen, i.e. als het aantal waarnemingen minder is dan het aantal hoekpunten van de graaf. Rece…

Penalized Likelihood Graphical Models Dynamic Networks State-space modelDynamische modellenLatent VariablesNetwerkenmathematische statistiekgraphical modelestimating dynamic networks.Proefschriften (vorm)Settore SECS-S/01 - StatisticaNormale verdelingDynamische systemenSysteembiologieGrafische methoden
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