Academic literature on the topic 'Echantillonnage et estimation Monte Carlo'
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Journal articles on the topic "Echantillonnage et estimation Monte Carlo"
Zhang, Kaixuan, Jingxian Wang, Daizong Tian, and Thrasyvoulos N. Pappas. "Film Grain Rendering and Parameter Estimation." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–14. http://dx.doi.org/10.1145/3592127.
Full textFilinov, V. S., P. R. Levashov, and A. S. Larkin. "The density–temperature range of exchange–correlation exciton existence by the fermionic path integral Monte Carlo method." Physics of Plasmas 29, no. 5 (May 2022): 052106. http://dx.doi.org/10.1063/5.0089836.
Full textRichard, Jean-François. "Conférence François-Albert Angers (1999). Enchères : théorie économique et réalité." Articles 76, no. 2 (February 5, 2009): 173–98. http://dx.doi.org/10.7202/602320ar.
Full textAbubakar Sadiq, Ibrahim, S. I. S. Doguwa, Abubakar Yahaya, and Abubakar Usman. "Development of New Generalized Odd Fréchet-Exponentiated-G Family of Distribution." UMYU Scientifica 2, no. 4 (December 30, 2023): 169–78. http://dx.doi.org/10.56919/usci.2324.021.
Full textXu, Xiaosi, and Ying Li. "Quantum-assisted Monte Carlo algorithms for fermions." Quantum 7 (August 3, 2023): 1072. http://dx.doi.org/10.22331/q-2023-08-03-1072.
Full textAl-Nasse, Amjad D. "An information-theoretic approach to the measurement error model." Statistics in Transition new series 11, no. 1 (July 16, 2010): 9–24. http://dx.doi.org/10.59170/stattrans-2010-001.
Full textPlekhanov, Kirill, Matthias Rosenkranz, Mattia Fiorentini, and Michael Lubasch. "Variational quantum amplitude estimation." Quantum 6 (March 17, 2022): 670. http://dx.doi.org/10.22331/q-2022-03-17-670.
Full textVida, Denis, Peter S. Gural, Peter G. Brown, Margaret Campbell-Brown, and Paul Wiegert. "Estimating trajectories of meteors: an observational Monte Carlo approach – I. Theory." Monthly Notices of the Royal Astronomical Society 491, no. 2 (November 15, 2019): 2688–705. http://dx.doi.org/10.1093/mnras/stz3160.
Full textChen, Jau-er, Chien-Hsun Huang, and Jia-Jyun Tien. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions." Econometrics 9, no. 2 (April 2, 2021): 15. http://dx.doi.org/10.3390/econometrics9020015.
Full textTakeuchi, Tsutomu T., Kohji Yoshikawa, and Takako T. Ishii. "Galaxy Luminosity Function: Applications and Cosmological Implications." Symposium - International Astronomical Union 201 (2005): 519–20. http://dx.doi.org/10.1017/s007418090021694x.
Full textDissertations / Theses on the topic "Echantillonnage et estimation Monte Carlo"
Argouarc, h. Elouan. "Contributions to posterior learning for likelihood-free Bayesian inference." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS021.
Full textBayesian posterior inference is used in many scientific applications and is a prevalent methodology for decision-making under uncertainty. It enables practitioners to confront real-world observations with relevant observation models, and in turn, infer the distribution over an explanatory variable. In many fields and practical applications, we consider ever more intricate observation models for their otherwise scientific relevance, but at the cost of intractable probability density functions. As a result, both the likelihood and the posterior are unavailable, making posterior inference using the usual Monte Carlo methods unfeasible.In this thesis, we suppose that the observation model provides a recorded dataset, and our aim is to bring together Bayesian inference and statistical learning methods to perform posterior inference in a likelihood-free setting. This problem, formulated as learning an approximation of a posterior distribution, includes the usual statistical learning tasks of regression and classification modeling, but it can also be an alternative to Approximate Bayesian Computation methods in the context of simulation-based inference, where the observation model is instead a simulation model with implicit density.The aim of this thesis is to propose methodological contributions for Bayesian posterior learning. More precisely, our main goal is to compare different learning methods under the scope of Monte Carlo sampling and uncertainty quantification.We first consider the posterior approximation based on the likelihood-to-evidence ratio, which has the main advantage that it turns a problem of conditional density learning into a problem of binary classification. In the context of Monte Carlo sampling, we propose a methodology for sampling from such a posterior approximation. We leverage the structure of the underlying model, which is conveniently compatible with the usual ratio-based sampling algorithms, to obtain straightforward, parameter-free, and density-free sampling procedures.We then turn to the problem of uncertainty quantification. On the one hand, normalized models such as the discriminative construction are easy to apply in the context of Bayesian uncertainty quantification. On the other hand, while unnormalized models, such as the likelihood-to-evidence-ratio, are not easily applied in uncertainty-aware learning tasks, a specific unnormalized construction, which we refer to as generative, is indeed compatible with Bayesian uncertainty quantification via the posterior predictive distribution. In this context, we explain how to carry out uncertainty quantification in both modeling techniques, and we then propose a comparison of the two constructions under the scope of Bayesian learning.We finally turn to the problem of parametric modeling with tractable density, which is indeed a requirement for epistemic uncertainty quantification in generative and discriminative modeling methods. We propose a new construction of a parametric model, which is an extension of both mixture models and normalizing flows. This model can be applied to many different types of statistical problems, such as variational inference, density estimation, and conditional density estimation, as it benefits from rapid and exact density evaluation, a straightforward sampling scheme, and a gradient reparameterization approach
Gajda, Dorota. "Optimisation des méthodes algorithmiques en inférence bayésienne. Modélisation dynamique de la transmission d'une infection au sein d'une population hétérogène." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00659618.
Full textCarpentier, Alexandra. "De l' echantillonnage optimal en grande et petite dimension." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2012. http://tel.archives-ouvertes.fr/tel-00844361.
Full textOunaissi, Daoud. "Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10043/document.
Full textThe thesis contains 6 chapters. The first chapter contains an introduction to linear regression, the Lasso and the Bayesian Lasso problems. Chapter 2 recalls the convex optimization algorithms and presents the Fista algorithm for calculating the Lasso estimator. The properties of the convergence of this algorithm is also given in this chapter using the entropy estimator and Pitman-Yor estimator. Chapter 3 is devoted to comparison of Monte Carlo and quasi-Monte Carlo methods in numerical calculations of Bayesian Lasso. It comes out of this comparison that the Hammersely points give the best results. Chapter 4 gives a geometric interpretation of the partition function of the Bayesian lasso expressed as a function of the incomplete Gamma function. This allowed us to give a convergence criterion for the Metropolis Hastings algorithm. Chapter 5 presents the Bayesian estimator as the law limit a multivariate stochastic differential equation. This allowed us to calculate the Bayesian Lasso using numerical schemes semi-implicit and explicit Euler and methods of Monte Carlo, Monte Carlo multilevel (MLMC) and Metropolis Hastings algorithm. Comparing the calculation costs shows the couple (semi-implicit Euler scheme, MLMC) wins against the other couples (scheme method). Finally in chapter 6 we found the Lasso convergence rate of the Bayesian Lasso when the signal / noise ratio is constant and when the noise tends to 0. This allowed us to provide a new criteria for the convergence of the Metropolis algorithm Hastings
Gilquin, Laurent. "Échantillonnages Monte Carlo et quasi-Monte Carlo pour l'estimation des indices de Sobol' : application à un modèle transport-urbanisme." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM042/document.
Full textLand Use and Transportation Integrated (LUTI) models have become a norm for representing the interactions between land use and the transportation of goods and people in a territory. These models are mainly used to evaluate alternative planning scenarios, simulating their impact on land cover and travel demand.LUTI models and other mathematical models used in various fields are most of the time based on complex computer codes. These codes often involve poorly-known inputs whose uncertainty can have significant effects on the model outputs.Global sensitivity analysis methods are useful tools to study the influence of the model inputs on its outputs. Among the large number of available approaches, the variance based method introduced by Sobol' allows to calculate sensitivity indices called Sobol' indices. These indices quantify the influence of each model input on the outputs and can detect existing interactions between inputs.In this framework, we favor a particular method based on replicated designs of experiments called replication method. This method appears to be the most suitable for our application and is advantageous as it requires a relatively small number of model evaluations to estimate first-order or second-order Sobol' indices.This thesis focuses on extensions of the replication method to face constraints arising in our application on the LUTI model Tranus, such as the presence of dependency among the model inputs, as far as multivariate outputs.Aside from that, we propose a recursive approach to sequentially estimate Sobol' indices. The recursive approach is based on the iterative construction of stratified designs, latin hypercubes and orthogonal arrays, and on the definition of a new stopping criterion. With this approach, more accurate Sobol' estimates are obtained while recycling previous sets of model evaluations. We also propose to combine such an approach with quasi-Monte Carlo sampling.An application of our contributions on the LUTI model Tranus is presented
Lamberti, Roland. "Contributions aux méthodes de Monte Carlo et leur application au filtrage statistique." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLL007/document.
Full textThis thesis deals with integration calculus in the context of Bayesian inference and Bayesian statistical filtering. More precisely, we focus on Monte Carlo integration methods. We first revisit the importance sampling with resampling mechanism, then its extension to the dynamic setting known as particle filtering, and finally conclude our work with a multi-target tracking application. Firstly, we consider the problem of estimating some moment of a probability density, known up to a constant, via Monte Carlo methodology. We start by proposing a new estimator affiliated with the normalized importance sampling estimator but using two proposition densities rather than a single one. We then revisit the importance sampling with resampling mechanism as a whole in order to produce Monte Carlo samples that are independent, contrary to the classical mechanism, which enables us to develop two new estimators. Secondly, we consider the dynamic aspect in the framework of sequential Bayesian inference. We thus adapt to this framework our new independent resampling technique, previously developed in a static setting. This yields the particle filtering with independent resampling mechanism, which we reinterpret as a special case of auxiliary particle filtering. Because of the increased cost required by this technique, we next propose a semi independent resampling procedure which enables to control this additional cost. Lastly, we consider an application of multi-target tracking within a sensor network using a new Bayesian model, and empirically analyze the results from our new particle filtering algorithm as well as a sequential Markov Chain Monte Carlo algorithm
Chesneau, Héléna. "Estimation personnalisée de la dose délivrée au patient par l’imagerie embarquée kV-CBCT et réflexions autour de la prise en charge clinique." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS059.
Full textProtocols for cancer treatment using intensity-modulated radiation therapy (IMRT) allow to target the tumor with an increased precision. They require accurate anatomical information of the patient just before the treatment, which can be obtained using on-board imaging systems mounted on the medical linear accelerator delivering the treatment beam. These systems, composed of an X-ray tube and a 2D planar detector, are called kV-Cone Beam CT (kV-CBCT). Nowadays, they are widely used in the context of IMRT treatments. However, these kV-CBCT examinations are also responsible for an additional dose of ionizing radiations which is far to be negligible and could be the cause for secondary effects, such as radiation-induced second cancers for treated patients. During this PhD work, a simulator based on the Monte Carlo method was developed in order to calculate accurately the doses delivered to organs during kV-CBCT examinations. Then, this tool was used to study several strategies to take in account for the imaging additional doses in clinical environment. The study reported here includes, in particular, a fast and personalized method to estimate the doses delivered to organs. This strategy was developed using a cohort of 50 patients including 40 children and 10 adults. This work has been done in collaboration with the medical physics unit of the Eugène Marquis medical center in Rennes, which has collected the clinical data used for this study
Bidon, Stéphanie Tourneret Jean-Yves Besson Olivier. "Estimation et détection en milieu non-homogène application au traitement spatio-temporel adaptatif /." Toulouse : INP Toulouse, 2009. http://ethesis.inp-toulouse.fr/archive/00000683.
Full textIchir, Mahieddine Mehdi. "Estimation bayésienne et approche multi-résolution en séparation de sources." Paris 11, 2005. http://www.theses.fr/2005PA112370.
Full textLamberti, Roland. "Contributions aux méthodes de Monte Carlo et leur application au filtrage statistique." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLL007.
Full textThis thesis deals with integration calculus in the context of Bayesian inference and Bayesian statistical filtering. More precisely, we focus on Monte Carlo integration methods. We first revisit the importance sampling with resampling mechanism, then its extension to the dynamic setting known as particle filtering, and finally conclude our work with a multi-target tracking application. Firstly, we consider the problem of estimating some moment of a probability density, known up to a constant, via Monte Carlo methodology. We start by proposing a new estimator affiliated with the normalized importance sampling estimator but using two proposition densities rather than a single one. We then revisit the importance sampling with resampling mechanism as a whole in order to produce Monte Carlo samples that are independent, contrary to the classical mechanism, which enables us to develop two new estimators. Secondly, we consider the dynamic aspect in the framework of sequential Bayesian inference. We thus adapt to this framework our new independent resampling technique, previously developed in a static setting. This yields the particle filtering with independent resampling mechanism, which we reinterpret as a special case of auxiliary particle filtering. Because of the increased cost required by this technique, we next propose a semi independent resampling procedure which enables to control this additional cost. Lastly, we consider an application of multi-target tracking within a sensor network using a new Bayesian model, and empirically analyze the results from our new particle filtering algorithm as well as a sequential Markov Chain Monte Carlo algorithm
Book chapters on the topic "Echantillonnage et estimation Monte Carlo"
BOURINET, Jean-Marc. "Estimation de probabilité d’événements rares." In Ingénierie mécanique en contexte incertain, 153–222. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9010.ch5.
Full textConference papers on the topic "Echantillonnage et estimation Monte Carlo"
Aladejare, A. E., K. A. Idowu, and T. M. Ozoji. "Reliability-Based Design (RBD) Updating by Sample Reweighting for Rock Slope Design." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-0807.
Full textAlcantara, Ricardo, Luis Humberto Santiago, Ilse Paola Melquiades, Blanca Estefani Ruiz, Jorge Ricardez, Cesar Israel Mendez, and Victor Mercado. "Coupling Numerical Dynamic Models of PTA and RTA with Detailed Geological Models Integrating Quantitative Analysis, Using Rock Physics, Seismic Inversion and Nonlinear Techniques in Siliciclastic Reservoirs." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215245-ms.
Full textReports on the topic "Echantillonnage et estimation Monte Carlo"
Acharya, Sushant, William Chen, Marco Del Negro, Keshav Dogra, Aidan Gleich, Shlok Goyal, Ethan Matlin, Donggyu Lee, Reca Sarfati, and Sikata Sengupta. Estimating HANK for Central Banks. Federal Reserve Bank of New York, August 2023. http://dx.doi.org/10.59576/sr.1071.
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