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Artykuły w czasopismach na temat "Schéma de régression de type Monte-Carlo"
Dagenais, Marcel G., i Denyse L. Dagenais. "L’estimation de modèles de régression linéaire autorégressifs avec erreurs résiduelles autocorrélées et erreurs sur les variables". Contributions économétriques 73, nr 1-2-3 (9.02.2009): 507–23. http://dx.doi.org/10.7202/602237ar.
Pełny tekst źródłaSenou, Marcel, i L. Dempfle. "Simulation Monte-Carlo pour évaluer l’impact des schémas MOET adultes chez les bovins Somba". Revue d’élevage et de médecine vétérinaire des pays tropicaux 61, nr 2 (1.02.2008): 115. http://dx.doi.org/10.19182/remvt.9997.
Pełny tekst źródłaRozprawy doktorskie na temat "Schéma de régression de type Monte-Carlo"
Izydorczyk, Lucas. "Probabilistic backward McKean numerical methods for PDEs and one application to energy management". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAE008.
Pełny tekst źródłaThis thesis concerns McKean Stochastic Differential Equations (SDEs) to representpossibly non-linear Partial Differential Equations (PDEs). Those depend not onlyon the time and position of a given particle, but also on its probability law. In particular, we treat the unusual case of Fokker-Planck type PDEs with prescribed final data. We discuss existence and uniqueness for those equations and provide a probabilistic representation in the form of McKean type equation, whose unique solution corresponds to the time-reversal dynamics of a diffusion process.We introduce the notion of fully backward representation of a semilinear PDE: thatconsists in fact in the coupling of a classical Backward SDE with an underlying processevolving backwardly in time. We also discuss an application to the representationof Hamilton-Jacobi-Bellman Equation (HJB) in stochastic control. Based on this, we propose a Monte-Carlo algorithm to solve some control problems which has advantages in terms of computational efficiency and memory whencompared to traditional forward-backward approaches. We apply this method in the context of demand side management problems occurring in power systems. Finally, we survey the use of generalized McKean SDEs to represent non-linear and non-conservative extensions of Fokker-Planck type PDEs
Tan, Xiaolu. "Méthodes de contrôle stochastique pour le problème de transport optimal et schémas numériques de type Monte-Carlo pour les EDP". Phd thesis, Ecole Polytechnique X, 2011. http://tel.archives-ouvertes.fr/tel-00661086.
Pełny tekst źródłaBaragatti, Meïli. "Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance". Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX22100/document.
Pełny tekst źródłaThis thesis is divided into two main parts. In the first part, we propose a Bayesian variable selection method for probit mixed models. The objective is to select few relevant variables among tens of thousands while taking into account the design of a study, and in particular the fact that several datasets are merged together. The probit mixed model used is considered as part of a larger hierarchical Bayesian model, and the dataset is introduced as a random effect. The proposed method extends a work of Lee et al. (2003). The first step is to specify the model and prior distributions. In particular, we use the g-prior of Zellner (1986) for the fixed regression coefficients. In a second step, we use a Metropolis-within-Gibbs algorithm combined with the grouping (or blocking) technique of Liu (1994). This choice has both theoritical and practical advantages. The method developed is applied to merged microarray datasets of patients with breast cancer. However, this method has a limit: the covariance matrix involved in the g-prior should not be singular. But there are two standard cases in which it is singular: if the number of observations is lower than the number of variables, or if some variables are linear combinations of others. In such situations we propose to modify the g-prior by introducing a ridge parameter, and a simple way to choose the associated hyper-parameters. The prior obtained is a compromise between the conditional independent case of the coefficient regressors and the automatic scaling advantage offered by the g-prior, and can be linked to the work of Gupta and Ibrahim (2007).In the second part, we develop two new population-based MCMC methods. In cases of complex models with several parameters, but whose likelihood can be computed, the Equi-Energy Sampler (EES) of Kou et al. (2006) seems to be more efficient than the Parallel Tempering (PT) algorithm introduced by Geyer (1991). However it is difficult to use in combination with a Gibbs sampler, and it necessitates increased storage. We propose an algorithm combining the PT with the principle of exchange moves between chains with same levels of energy, in the spirit of the EES. This adaptation which we are calling Parallel Tempering with Equi-Energy Move (PTEEM) keeps the original idea of the EES method while ensuring good theoretical properties and a practical use in combination with a Gibbs sampler.Then, in some complex models whose likelihood is analytically or computationally intractable, the inference can be difficult. Several likelihood-free methods (or Approximate Bayesian Computational Methods) have been developed. We propose a new algorithm, the Likelihood Free-Parallel Tempering, based on the MCMC theory and on a population of chains, by using an analogy with the Parallel Tempering algorithm
Gagnon, Philippe. "Sélection de modèles robuste : régression linéaire et algorithme à sauts réversibles". Thèse, 2017. http://hdl.handle.net/1866/20583.
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