Literatura académica sobre el tema "Processus ponctuels temporels (TPP)"
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Artículos de revistas sobre el tema "Processus ponctuels temporels (TPP)"
Yagouti, A., I. Abi-Zeid, T. B. M. J. Ouarda y B. Bobée. "Revue de processus ponctuels et synthèse de tests statistiques pour le choix d'un type de processus". Revue des sciences de l'eau 14, n.º 3 (12 de abril de 2005): 323–61. http://dx.doi.org/10.7202/705423ar.
Texto completoTesis sobre el tema "Processus ponctuels temporels (TPP)"
Allain, Cédric. "Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.
Texto completoIn the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity
Chimard, Florencia. "Mélanges de processus ponctuels spatio-temporels et approche bayésienne semi-paramétrique". Antilles-Guyane, 2010. http://www.theses.fr/2010AGUY0392.
Texto completoPoint processes are often used as tools for describing spatial or spatio- temporal point patterns. In this Phd dissertation, we give an overview of bayesian statistical analysis for point processes and recent tools Iike the Dirichlet process and its diverse extensions. We focus on situations where the available data are maps of the studied point process at different observations dates. Two contexts are considered. Firstly, we consider occurrences of events in a studied area forming the realization of a spatio-temporal Cox process directed by a generalized shot noise intensity measure. A hidden Poisson process generates contributions to the intensity measure which are distributed according to a Dirichlet process centered on the Gamma distribution. For data consisting of spatial locations of occurrences between several pairs of consecutive observation dates, we develop statistical inference about the parameters of interest by means of MCMC methods within the framework of hierarchical bayesian modeling. A data augmentation algorithm is introduced and tested on artificial data. Secondly, we analyse the case where the point process support is discrete with at most one occurrence for a given element of the support. For such binary data, we present and discuss models based on Bernoulli distribution mixture with a background intensity following a log-gaussian. The statistical inference for these models is developped by using a hierarchical bayesian approach. Tests are carried out on artificial data and data from Yellow Leaf Sugarcane Virus observations
Jacquet, Olivier. "Analyse statistique des processus ponctuels spatio-temporels de propagation sur une grille". Antilles-Guyane, 2008. http://www.theses.fr/2008AGUY0245.
Texto completoThe origin of the work presented in this thesis is a problem of epidemiology in the field of agricultural crops. It is to model the spread of disease on an experimental plot. Individuals statistics, in this case the plants of this plot, are regularly arranged on the nodes on a grid. In such situations, harvest data, in general, dates successive positions of the new infected individuals, putting in place various strategies depending on the size of parcels or human resources and technology available. Ifone is not limited by the cost and effort of observation, the ideal method is to make the exhaustive sampling ie observe the condition ofeach individual on the plot. However, for reasons ofcost observation, we can not have the status of individuals on the grid than the dates of observation data. There fore, if at date S t_OS we have all infected plants S 1_{t_O}S and at date S t_lS we have S1_{t_l}S, a methodological approach is to consider the possible order of infection between S t_0S and S t_l S. The purpose ofthis work is to propose a model for the spatial and temporal evolution of a disease on a regular grid and develop statistical inference of this model for data consisting of infection cards reported on dates fixed widely spaced so that the precise dates ofinfection are missing data. The plan of this thesis in to two parts. \\ In the first, we recall the main tools ofstatistical analysis of ad hoc spatial and/or temporal processes, and a summary on Bayesi an analysis and Markovian exploration techniques which will be used to infer and optimize the parameters of interest. In the second part of the thesis, we present a model inspired by the propagation model Gibson (1996) and various methodologies to tackle the problem of statistical inference in Chapter 3. The proposed methods can be classified in to two broad families. As a first step, we can consider inference techniques that take into account only the temporal order ofarrival of events between the dates ofobservation St_0S and St_lS. These techniques range from the use of simple Monte Carlo methods to generation of Markov chain. In a second time, the methodology is to generate the precise dates of occurrence of events between the dates of observations S t_0S and S t_l St, then usin the theory of Bayes combined with Markov chains to estimate the parameters of interest
Valmy, Larissa. "Modèles hiérarchiques et processus ponctuels spatio-temporels - Applications en épidémiologie et en sismologie". Phd thesis, Université des Antilles-Guyane, 2012. http://tel.archives-ouvertes.fr/tel-00841146.
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