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Academic literature on the topic 'Observations distribuées par Poisson'
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Journal articles on the topic "Observations distribuées par Poisson"
Cament, Leonardo, Martin Adams, and Pablo Barrios. "Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter." Sensors 21, no. 11 (May 26, 2021): 3684. http://dx.doi.org/10.3390/s21113684.
Full textLauderdale, Benjamin E. "Compound Poisson—Gamma Regression Models for Dollar Outcomes That Are Sometimes Zero." Political Analysis 20, no. 3 (2012): 387–99. http://dx.doi.org/10.1093/pan/mps018.
Full textWille, Clara. "Murena id est Lampreda: Quelques observations lexicologiques et culinaires." Reinardus / Yearbook of the International Reynard Society 20 (December 12, 2008): 170–87. http://dx.doi.org/10.1075/rein.20.11wil.
Full textKirouac, Valérie, and Rock Santerre. "Amélioration du positionnement relatif temporel GPS avec les corrections GPS•C." GEOMATICA 65, no. 2 (June 2011): 199–209. http://dx.doi.org/10.5623/cig2011-028.
Full textPétrus, M., C. Dormoy, C. Cadix, V. Mangin, P. Delbast, and G. Dutau. "Syndrome d’entérocolite induite par les protéines de poisson. Trois nouvelles observations." Revue Française d'Allergologie 54, no. 5 (September 2014): 394–96. http://dx.doi.org/10.1016/j.reval.2014.04.002.
Full textBernard, B., T. Mhanna, B. Dugas, P. Gasquez, P. J. Valette, P. Marx, P. Sauvage, A. Naouri, E. Odet, and P. Bernard. "Perforation jéjunale par arête de poisson diagnostiquée par la tomodensitométrie abdominale : à propos de deux observations." Annales de Chirurgie 130, no. 10 (December 2005): 636–39. http://dx.doi.org/10.1016/j.anchir.2005.06.013.
Full textK, Batwila, Apkavi S, Wala K, Kanda M, Akpagana K, and Vodouhe R. "Diversité et Gestion des Légumes de Cueillette au Togo." African Journal of Food, Agriculture, Nutrition and Development 7, no. 14 (May 28, 2007): 02–21. http://dx.doi.org/10.18697/ajfand.14.ipgri1-7.
Full textPercoma, Lassané, Zowindé Koudougou, Oumarou Serdebeogo, Issa Tamboura, Mamadou Ouedraogo, Jérémy Bouyer, Adrien M. G. Belem, and Issa Sidibé. "Enquêtes entomologiques préparatoires à une lutte à grande échelle contre les glossines, assistées par un système d’information géographique : cas de la Pattec au Burkina Faso." Revue d’élevage et de médecine vétérinaire des pays tropicaux 68, no. 4 (May 9, 2016): 157. http://dx.doi.org/10.19182/remvt.31163.
Full textPatrick, Andrew S., Alex Black, and Thomas E. Whalen. "CBC Radio on the Internet: An Experiment in Convergence." Canadian Journal of Communication 21, no. 1 (January 1, 1996). http://dx.doi.org/10.22230/cjc.1996v21n1a926.
Full textGordon, Jr., Donald C., and Ellen L. R. Kenchington. "DEEP-WATER CORALS IN ATLANTIC CANADA: A REVIEW OF DFO RESEARCH (2001-2003)." Proceedings of the Nova Scotian Institute of Science (NSIS) 44, no. 1 (April 19, 2007). http://dx.doi.org/10.15273/pnsis.v44i1.3881.
Full textDissertations / Theses on the topic "Observations distribuées par Poisson"
Iufereva, Olga. "Algorithmes de filtrage avec les observations distribuées par Poisson." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. https://theses.hal.science/tel-04720020.
Full textFiltering theory basically relates to optimal state estimation in stochastic dynamical systems, particularly when faced with partial and noisy data. This field, closely intertwined with control theory, focuses on designing estimators doing real-time computation while maintaining an acceptable level of accuracy as measured by the mean square error. The necessity for such estimates becomes increasingly critical with the proliferation of network-controlled systems, such as autonomous vehicles and complex industrial processes, where the observation processes are subject to randomness in transmission and this gives rise to varying information patterns under which the estimation must be carried out.This thesis addresses the important task of state estimation in continuous-time stochastic dynamical systems when the observation process is available only at some discrete time instants governed by a random process. By adapting classical estimation methods, we derive equations for optimal state estimator, explore their properties and practicality, and propose and evaluates sub-optimal alternatives, showcasing parallels to the existing techniques within the classical estimation domain when applied to Poisson-distributed observation processes.The study covers three classes of mathematical models for the continuous-time dynamical system and the discrete observation process. First, we consider Ito-stochastic differential equations with Lipschitz drift terms and constant diffusion coefficient, whereas the lower-dimensional discrete observation process comprises the nonlinear mapping of the state and additive Gaussian noise. We propose easy-to-implement continuous-discrete suboptimal state estimators for this system class. Assuming that a Poisson counter governs discrete times at which the observations are available, we compute the expectation or error covariance process. Analysis is carried out to provide conditions for boundedness of the error covariance process, as well as, the dependence on the mean sampling rate.Secondly, we consider the dynamical systems described by continuous-time Markov chains with finite state space, and the observation process is obtained by discretizing a conventional stochastic process driven by a Wiener process. For this case, the $L_1$-convergence of the derived optimal estimator to the classical (purely continuous) optimal estimator (Wonham filter) is shown with respect to increasing intensity of Poisson processes.Lastly, we study continuous-discrete particle filters for Ornstein-Uhlenbeck processes with discrete observations described by linear functions of state and additive Gaussian noise. Particle filters have gained a lot of interest for state estimation in large-scale models with noisy measurements where the computation of optimal gain is either computationally expensive or not entirely feasible due to complexity of the dynamics. In this thesis, we propose continuous-discrete McKean–Vlasov type diffusion processes, which serve as the mean-field model for describing the particle dynamics. We study several kinds of mean-field processes depending on how the noise terms are included in mimicking the state process and the observation model. The resulting particles are coupled through empirical covariances which are updated at discrete times with the arrival of new observations. With appropriate analysis of the first and second moments, we show that under certain conditions on system parameters, the performance of the particle filters approaches the optimal filter as the number of particles gets larger