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X-WR-CALNAME:Département de mathématiques et applications
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X-WR-CALDESC:évènements pour Département de mathématiques et applications
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TZID:Europe/Paris
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TZNAME:CEST
DTSTART:20160327T010000
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DTSTART:20161030T010000
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20161006T140000
DTEND;TZID=Europe/Paris:20161006T160000
DTSTAMP:20260519T003042
CREATED:20161006T120000Z
LAST-MODIFIED:20211104T101629Z
UID:8302-1475762400-1475769600@www.math.ens.psl.eu
SUMMARY:Régularisation spatio-temporelle physique pour la mesure de champs de vitesse des fluides
DESCRIPTION:
URL:https://www.math.ens.psl.eu/evenement/regularisation-spatio-temporelle-physique-pour-la-mesure-de-champs-de-vitesse-des-fluides/
LOCATION:IHP  amphi Darboux
CATEGORIES:Séminaire Parisien des Mathématiques Appliquées à l’Imagerie
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20161006T150000
DTEND;TZID=Europe/Paris:20161006T160000
DTSTAMP:20260519T003042
CREATED:20161006T130000Z
LAST-MODIFIED:20211104T101430Z
UID:8300-1475766000-1475769600@www.math.ens.psl.eu
SUMMARY:Mixed-effect model for the spatiotemporal analysis of longitudinal manifold-valued data
DESCRIPTION:In this work\, we propose a generic hierarchical spatiotemporal model for longitudinal manifold-valued data\, which consist in repeated measurements over time for a group of individuals. This model allows us to estimate a group-average trajectory of progression\, considered as a geodesic of a given Riemannian manifold. Individual trajectories of progression are obtained as random variations\, which consist in parallel shifting and time reparametrization\, of the average trajectory. These spatiotemporal transformations allow us to characterize changes in the direction and in the pace at which trajectories are followed. We propose to estimate the parameters of the model using a stochastic version of the expectation-maximization (EM) algorithm\, the Monte Carlo Markov Chain Stochastic Approximation EM (MCMC SAEM) algorithm. This generic spatiotemporal model is used to analyze the temporal progression of a family of biomarkers. This progression model estimates a normative scenario of the progressive impairments of several cognitive functions\, considered here as biomarkers\, during the course of Alzheimer?RTMs disease. The estimated average trajectory provides a normative scenario of disease progression. Random effects provide unique insights into the variations in the ordering and timing of the succession of cognitive impairments across different individuals.
URL:https://www.math.ens.psl.eu/evenement/mixed-effect-model-for-the-spatiotemporal-analysis-of-longitudinal-manifold-valued-data/
LOCATION:IHP amphi Darboux
CATEGORIES:Séminaire Parisien des Mathématiques Appliquées à l’Imagerie
END:VEVENT
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