Search results for " Models"

showing 10 items of 4240 documents

Epidemic spreading and aging in temporal networks with memory

2018

Time-varying network topologies can deeply influence dynamical processes mediated by them. Memory effects in the pattern of interactions among individuals are also known to affect how diffusive and spreading phenomena take place. In this paper we analyze the combined effect of these two ingredients on epidemic dynamics on networks. We study the susceptible-infected-susceptible (SIS) and the susceptible-infected-removed (SIR) models on the recently introduced activity-driven networks with memory. By means of an activity-based mean-field approach we derive, in the long time limit, analytical predictions for the epidemic threshold as a function of the parameters describing the distribution of …

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceAnalytical predictionsEpidemic dynamicsFOS: Physical sciencesPhysics and Society (physics.soc-ph)Network topology01 natural sciences010305 fluids & plasmasNetworks and Complex Systems0103 physical sciencesQuantitative Biology::Populations and EvolutionStatistical physicsLimit (mathematics)010306 general physicsQuantitative Biology - Populations and EvolutionEpidemic controlSocial and Information Networks (cs.SI)Populations and Evolution (q-bio.PE)Computer Science - Social and Information NetworksFunction (mathematics)Computer Science::Social and Information NetworksArticlesDynamic modelsEpidemic thresholdEpidemic spreadingFOS: Biological sciencesMean field approachPhysical Review. E
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

2019

Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariate…

FOS: Computer and information sciencesStatistics and ProbabilityMultivariate statisticssequence analysisaikasarjatComputer sciencerMarkov modelStatistics - ComputationStatistics - Applications01 natural sciencesUnobservablecategorical time seriesR-kieli010104 statistics & probabilitymulti-channel sequences; categorical time series; visualizing sequence data; visualizing models; latent Markov models; latent class models; RCovariateApplications (stat.AP)Sannolikhetsteori och statistikComputer software0101 mathematicsTime seriesProbability Theory and StatisticsHidden Markov modelCluster analysislcsh:Statisticslcsh:HA1-4737Categorical variableComputation (stat.CO)ta112business.industryvisualizing sequence dataR (programming languages)Pattern recognitionmulti-channel sequencesvisualizing modelslatent class modelssekvenssianalyysiArtificial intelligencelatent markov modelstime seriesStatistics Probability and UncertaintybusinessSoftwareJournal of Statistical Software
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KFAS : Exponential Family State Space Models in R

2017

State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state space modelling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modelling is presented.

FOS: Computer and information sciencesStatistics and ProbabilityaikasarjatGaussianNegative binomial distributionforecastingPoisson distribution01 natural sciencesStatistics - ComputationMethodology (stat.ME)010104 statistics & probability03 medical and health sciencessymbols.namesake0302 clinical medicineExponential familyexponential familyGamma distributionStatistical inferenceState spaceApplied mathematicsSannolikhetsteori och statistik030212 general & internal medicine0101 mathematicsProbability Theory and Statisticslcsh:Statisticslcsh:HA1-4737Computation (stat.CO)Statistics - MethodologyMathematicsR; exponential family; state space models; time series; forecasting; dynamic linear modelsta112state space modelsSeries (mathematics)RStatistics; Computer softwaresymbolsStatistics Probability and Uncertaintytime seriesSoftwaredynamic linear models
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Implicit differentiation for fast hyperparameter selection in non-smooth convex learning

2022

International audience; Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order methods when the inner optimization problem is convex but non-smooth. We show that the forward-mode differentiation of proximal gradient descent and proximal coordinate descent yield sequences of Jacobians converging toward the exact Jacobian. Using implicit differentiation, we show it is possible to leverage the non-smoothness of the inner problem to speed up the computation. Finally, we provide a bound on the error made on the hypergradient when the inner optimization problem is solved approxim…

FOS: Computer and information sciencesbilevel optimizationComputer Science - Machine Learninghyperparameter selec- tionMachine Learning (stat.ML)[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]generalized linear modelsMachine Learning (cs.LG)Convex optimizationStatistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Optimization and Control (math.OC)FOS: Mathematics[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]hyperparameter optimizationLassoMathematics - Optimization and Control[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
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Bayesian Analysis of Population Health Data

2021

The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and…

FOS: Computer and information sciencesmedicine.medical_specialtyComputer scienceGeneral MathematicsBayesian probabilitydisease mappingPopulation healthbayesian inference; disease mapping; integrated nested Laplace approximation; spatial models; survival modelsBayesian inferenceLogistic regressionStatistics - Applications01 natural sciences010104 statistics & probability03 medical and health sciences0302 clinical medicineStatisticsComputer Science (miscellaneous)medicineApplications (stat.AP)spatial models0101 mathematicsEngineering (miscellaneous)Socioeconomic statusbayesian inferencesurvival modelslcsh:MathematicsPublic healthintegrated nested Laplace approximationlcsh:QA1-939Random effects modelSpatial variability030217 neurology & neurosurgeryMathematics
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Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular co…

2022

Abstract Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and s…

FOS: Computer and information sciencesmultivariate time seriesPhysiologyEntropyRespirationBiomedical EngineeringBiophysicsheart rate variabilitytransfer entropyredundancy and synergyBlood PressureHeartQuantitative Biology - Quantitative MethodsCardiovascular SystemMethodology (stat.ME)Heart RatePhysiology (medical)FOS: Biological sciencesCardiovascular controlSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelsHumansQuantitative Methods (q-bio.QM)Statistics - MethodologyPhysiological measurement
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Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction

2014

In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentration matrix in penalized Gaussian graphical models. Symmetry restrictions on the concentration matrix are particularly useful to reduce the number of parameters to be estimated and to create specific structured graphs. The penalized Gaussian graphical models are suitable for high-dimensional data.

Factorial dynamic Gaussian graphical models Gaussian graphical models graphical lasso cyclic coordinate descent methodsSettore SECS-S/01 - Statistica
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Immune suppression in advanced chronic fascioliasis: an experimental study in a rat model.

2006

Chronicity and Th2 immune responses are features of helminth infections in humans. The liver fluke promotes its own survival through several strategies to down-regulate the immune response of the host during the early phase of infection. However, there is no evidence that this modulation occurs much later. The immune response in advanced chronic fascioliasis was analyzed in an experimental rat model at 20 weeks after infection. Cytokine quantification in infected rat serum revealed basal levels. The predominant immunoglobulin (Ig) isotype was IgG1. Flow cytometry analysis of T cell (CD3 + , CD4 + , and CD8a + ), B cell (CD45R + ), and macrophage (CD11b + ) populations in spleens showed no s…

FascioliasisMononuclear cell proliferationmedicine.medical_treatmentT cellT-LymphocytesImmune systemmedicineImmunology and AllergyAnimalsLymphocyte CountB cellImmunosuppression TherapyB-LymphocytesbiologyIsotypeAntigens DifferentiationFasciolaBlood Cell CountRatsChronic infectionDisease Models AnimalInfectious DiseasesCytokinemedicine.anatomical_structureImmunoglobulin GImmunologybiology.proteinCytokinesEgyptAntibodyCell DivisionSpleenThe Journal of infectious diseases
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High risk of bacterobilia in advanced experimental chronic fasciolosis

2006

Fasciolosis is recognized as an important human disease. Wistar rats experimentally infected with Fasciola hepatica were examined using data obtained in the advanced chronic state of the disease (200, 300 and 400 days post-infection, dpi). Pigment stones (PS) and bile specimens were collected. The same procedure was applied in control rats. Liver tests were determined using stored serum samples. Bacteriological bile culture revealed viable bacteria (Escherichia coli, 45% of cases, Enterococcus faecalis, 45% and Klebsiella pneumoniae, 10%). The presence of bacterobilia was associated with liver serum enzymes, including aspartate aminotransferase (AST or SGOT), alanine aminotransferase (ALT o…

Fascioliasismedicine.medical_specialtyKlebsiella pneumoniaeBiliary Tract DiseasesVeterinary (miscellaneous)HelminthiasisBiologyGastroenterologyEnterococcus faecalisSepsisInternal medicineEnterococcus faecalisEscherichia colimedicineAnimalsBileHumansFasciola hepaticaFasciolosisRats WistarBiliary TractBacterial InfectionsFasciola hepaticamedicine.diseasebiology.organism_classificationCulture MediaRatsDisease Models AnimalKlebsiella pneumoniaeInfectious DiseasesLiverInsect ScienceChronic DiseaseImmunologyAlkaline phosphataseParasitologyBacteriaActa Tropica
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Public transportation and fear of crime at BRT Systems: Approaching to the case of Barranquilla (Colombia) through integrated choice and latent varia…

2021

Abstract Security perception and Fear of Crime (FoC) in urban scenarios have the potential to affect travel behavior, changing people’s travel choices and patterns. In this sense, the feeling of being “safe” or “at-risk” in public transportation not only depends on observable factors like illumination, travel companionship or transport crowding, but also on unobservable individual-specific latent attributes, among which fear of crime constitutes a major issue to consider in transport security policy-making. This study aimed to describe the relationships among sociodemographic features, travel situations, system-design features, and the Fear of Crime at three different locations (buses, bus …

Fear of CrimeRisk perceptionmedia_common.quotation_subjectAerospace EngineeringTransportationManagement Science and Operations ResearchAffect (psychology)Interpersonal relationshipPerceptionLatent variablesCivil and Structural Engineeringmedia_commonPublic TransportationHybrid Choice ModelsLEMBbusiness.industryFear of crimeCrowdingRisk perceptionTravel behaviorPublic transportBusiness Management and Accounting (miscellaneous)PsychologybusinessSocial psychology
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