Academic literature on the topic 'Monte-Carlo (Méthode de)'
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Journal articles on the topic "Monte-Carlo (Méthode de)"
Maya, Cecilia. "Monte Carlo Option Princing." Lecturas de Economía, no. 61 (November 3, 2009): 53–70. http://dx.doi.org/10.17533/udea.le.n61a2729.
Full textSambou, S. "Comparaison par simulation de Monte-Carlo des propriétés de deux estimateurs du paramètre d'échelle de la loi exponentielle : méthode du maximum de vraisemblance (MV) et méthode des moindres carrés (MC)." Revue des sciences de l'eau 17, no. 1 (April 12, 2005): 23–47. http://dx.doi.org/10.7202/705521ar.
Full textJourdain, Benjamin, and Laurent Nguyen. "Minimisation de l'entropie relative par méthode de Monte-Carlo." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 332, no. 4 (February 2001): 345–50. http://dx.doi.org/10.1016/s0764-4442(01)01835-3.
Full textDufour, Jean-Marie, Abdeljelil Farhat, and Lynda Khalaf. "Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression*." Articles 80, no. 2-3 (October 24, 2005): 501–22. http://dx.doi.org/10.7202/011397ar.
Full textAuvinet, G. "Étude des écoulements en milieux poreux par la méthode de Monte Carlo." Revue Française de Géotechnique, no. 70 (1995): 15–24. http://dx.doi.org/10.1051/geotech/1995070015.
Full textCottrell, G., M. Cot, and J. Y. Mary. "L’imputation multiple des données manquantes aléatoirement : concepts généraux et présentation d’une méthode Monte-Carlo." Revue d'Épidémiologie et de Santé Publique 57, no. 5 (October 2009): 361–72. http://dx.doi.org/10.1016/j.respe.2009.04.011.
Full textMoulin, A., J. Henry, and E. Lemaire. "Simulation de la texture de recuit d’aciers IF par la méthode de Monte-Carlo." Revue de Métallurgie 91, no. 9 (September 1994): 1252. http://dx.doi.org/10.1051/metal/199491091252.
Full textEBRARD, G., A. ALLARD, and N. FISCHER. "Un logiciel simple d’utilisation pour évaluer l’incertitude de mesure par la méthode de Monte-Carlo." Revue française de métrologie, no. 43 (February 2, 2017): 27–36. http://dx.doi.org/10.1051/rfm/2016013.
Full textMartinie, B., J. Lecomte, J. Lebreton, and N. Ben Kaddour. "Simulation de la transition de phase orthorhombiquequadratique de YBa2Cu3O7-x par la méthode de Monte-Carlo." Journal de Physique III 1, no. 11 (November 1991): 1787–94. http://dx.doi.org/10.1051/jp3:1991233.
Full textTanguy, D., and T. Magnin. "Piégeage de l’hydrogène aux joints de grains dans Al-5Mg : simulation par la méthode Monte Carlo." Matériaux & Techniques 88, no. 9-10 (2000): 45–48. http://dx.doi.org/10.1051/mattech/200088090045.
Full textDissertations / Theses on the topic "Monte-Carlo (Méthode de)"
Cornebise, Julien. "Méthodes de Monte Carlo séquentielles adaptatives." Paris 6, 2009. http://www.theses.fr/2009PA066152.
Full textGüçlü, Alev Devrim. "Simulation des dispositifs optoélectroniques par la méthode Monte Carlo." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0021/MQ54037.pdf.
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
Arouna, Bouhari. "Algotithmes stochastiques et méthodes de Monte Carlo." Phd thesis, Ecole des Ponts ParisTech, 2004. http://pastel.archives-ouvertes.fr/pastel-00001269.
Full textJaeckel, Alain. "Simulations Monte Carlo de chaînes confinées." Montpellier 2, 1997. http://www.theses.fr/1997MON20206.
Full textChabut, Emmanuel. "Simulation aérothermodynamique en régime d'écoulement raréfié par méthode de Monte-Carlo." Orléans, 2005. http://www.theses.fr/2005ORLE2017.
Full textForster, Simon. "Nouveau matériau semi-conducteur à large bande interdite à base de carbures ternaires - Enquête sur Al4SiC4." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAI095.
Full textWide bandgap semiconductor materials are able to withstand harsh environments and operate over a wide range of temperatures. These make them ideal for many applications such as sensors, high-power and radio-frequencies to name a few.However, more novel materials are required to achieve significant power efficiency of various applications or to develop new applications to complement current wide bandgap semiconductors such as GaN and SiC.In this dissertation, three different methods are used to study one of these novelmaterials, aluminium silicon carbide (Al4SiC4): (1) ensemble Monte Carlo simulationsin order to study the electron transport properties of the novel ternary carbide, (2)experimental studies to determine its material properties, and (3) device simulationsof a heterostructure device made possible by this ternary carbide. All these methodsinterlink with each other. Data from each of them can feed into the other to acquire newresults or refine obtained results thus leading way to attractive electrical properties such as a bandgap of 2.78 eV or a peak drift velocity of 1.35×10 cm s .Ensemble Monte Carlo toolbox, developed in-house for simulations of Si, Ge, GaAs,AlxGa1−xAs, AlAs, and InSb; is adopted for simulations of the ternary carbide by adding anew valley transformation to account for the hexagonal structure of Al4SiC4. We predicta peak electron drift velocity of 1.35×107 cms−1 at electric field of 1400 kVcm−1 and a maximum electron mobility of 82.9 cm V s . We have seen a diffusion constant of 2.14 cm2s−1 at a low electric field and of 0.25 cm2s−1 at a high electric field. Finally, weshow that Al4SiC4 has a critical field of 1831 kVcmsemiconductor crystals are used that had previously been grown at IMGP, one by solution grown and the other by crucible melt. Three different experiments are performed on them; (1) UV, IR and Vis Spectroscopy, (2) X-ray Photo Spectroscopy, and (3) Two- and four-probe measurements where metal contact are grown on the crystals. Here we have found a bandgap of 2.78 ± 0.02 eV UV, IR and Vis Spectroscopy and a thick oxide layer on the samples using XPS. Unfortunately the Two- and four-probe measurements failed to give any results other than noise, most likely due to the thick oxide layer that was found on the samples.In the device simulations, a commercial software Atlas by Silvaco is utilized to predict performance of heterostructure devices, with gates lengths of 5 μm, 2 μm and 1 μm, made possible by the ternary carbide in a combination with SiC. The 5 μm gate length SiC/Al4SiC4 heterostructure transistor delivers a maximum drain current of 1.68×10−4 A/μm, which increases to 2.44×10−4 A/μm and 3.50×10−4 A/μm for gate lengths of 2 μm and 1 μm, respectively. The device breakdown voltage is 59.0 V which reduces to 31.0 V and to 18.0 V for the scaled 2 μm and the 1 μm gate length transistors. The scaled down 1 μm gate length device switches faster because of the higher transconductance of6.51×10−5 S/μmcomparedtoonly1.69×10−6 S/μmforthelargestdevice.Finally,a sub-threshold slope of the scaled devices is 197.3 mV/dec, 97.6 mV/dec, and 96.1 mV/dec for gate lengths of 5 μm, 2 μm, and 1 μm, respectively
Reilhac-Laborde, Anthonin. "Validation et exploitation d'un simulateur TEP de Monte Carlo." Lyon, INSA, 2007. http://theses.insa-lyon.fr/publication/2007ISAL0071/these.pdf.
Full textThe evaluation of algorithms dedicated to process, reconstruct, or analyze PET data is a challenging task. The common strategy is to compare the algorithm output to a controlled gold standard. A part of the difficulty follows from the unavailability of such ground truth with in vivo data. Consequently, validation often relies on the use of simulated data whose geometry and contents are precisely known. This method provides a great flexibility as it allows the use of realistic numerical phantoms. Also, the ability to control the factors that degrade the image formation is a significant advantage. The aim of this PhD research was to conduct first the validation experiments of the simulation model of a Monte Carlo-based PET simulator (PET-SORTEO) and second, to use the simulator as a tool for the design and development of moethods that aim at improving the quantification of the PET data
Maigne, Lydia. "Personnalized dosimetry using GATE Monte Carlo simulations on a grid architecture." Clermont-Ferrand 2, 2005. http://www.theses.fr/2005CLF21607.
Full textSzkolnik, Jean-Jacques. "Application des méthodes de Monte-Carlo séquentielles à l'extraction de trames radar." Brest, 2004. http://www.theses.fr/2004BRES2023.
Full textThe purpose of this study consists in determining an algorithm able to ensure the blind extraction of pulses resulting from the same radar system and to characterize the sequence evolution of their characteristic parameters. We precisely explicit the context and the physical nature of the pulses, the parameters which characterize them and the various parameter modulations taken into account, in order to lead to a radar pulse train state formulation. Then, we deal with the current method limitations implemented on operational equipments and draws up the state of the art of research on the matter through free publications appearing on the subject. We release and justify our own research orientation relying on the application of the particle methods to the previously described problems. We detail Monte-Carlo sequential theories, their limitations and additional techniques used to overcome drawbacks. We stipulate the adaptation of the presented techniques to our problem in order to deduce the formulation of a generic deinterleaving module specified exclusively from pulse times of arrival (TOA). The extraction module adaptation capabilities to news modulations, possibly nonlinear, are improved by integrating the unscented transformation. We extend then the concepts used for the TOA extraction module to new extraction modules specified from the other parameters. We give an example of scenario complexity degree likely to be processed with the association of two extraction modules running for the first from TOA and for the second from an other parameter. Finally the last part is dedicated to a series of extraction module evaluations intended to determine its application field
Books on the topic "Monte-Carlo (Méthode de)"
R, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. London: Chapman & Hall, 1996.
Find full textR, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Boca Raton, Fla: Chapman & Hall, 1998.
Find full text1958-, Howland Frank M., ed. Introductory econometrics: Using Monte Carlo simulation with Microsoft Excel. Cambridge: Cambridge University Press, 2006.
Find full textLászló, Koblinger, ed. Monte Carlo particle transport methods: Neutron and photon calculations. Boca Raton: CRC Press, 1991.
Find full textauteur, Melamed Benjamin, ed. Simulation modeling and analysis with Arena. New Delhi: Elevier, 2012.
Find full textNovak, Erich. Deterministic and stochastic error bounds in numerical analysis. Berlin: Springer-Verlag, 1988.
Find full textRobert, Christian P., and George Casella. Méthodes de Monte-Carlo avec R. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0.
Full textGeorge, Casella, and SpringerLink (Online service), eds. Méthodes de Monte-Carlo avec R. Paris: Springer-Verlag France S.A.R.L., 2011.
Find full textLaurencelle, Louis. Hasard, Nombres Aléatoires et Méthode Monte Carlo. Presses de l'Université du Québec, 2001.
Find full textBook chapters on the topic "Monte-Carlo (Méthode de)"
Antoni, Rodolphe, and Laurent Bourgois. "Principe de la méthode de Monte-Carlo appliquée aux calculs de dosimétrie et de radioprotection." In Ingénierie et Développement Durable, 387–464. Paris: Springer Paris, 2013. http://dx.doi.org/10.1007/978-2-8178-0311-1_6.
Full textRobert, Christian P., and George Casella. "Intégration de Monte-Carlo." In Méthodes de Monte-Carlo avec R, 33–62. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_3.
Full textRobert, Christian P., and George Casella. "Optimisation par les méthodes de Monte-Carlo." In Méthodes de Monte-Carlo avec R, 99–139. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_5.
Full textRobert, Christian P., and George Casella. "Préliminaires." In Méthodes de Monte-Carlo avec R, 1–10. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_1.
Full textRobert, Christian P., and George Casella. "Génération de variables aléatoires." In Méthodes de Monte-Carlo avec R, 11–32. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_2.
Full textRobert, Christian P., and George Casella. "Contrôler et accélérer la convergence." In Méthodes de Monte-Carlo avec R, 63–98. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_4.
Full textRobert, Christian P., and George Casella. "Algorithmes de Metropolis-Hastings." In Méthodes de Monte-Carlo avec R, 141–71. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_6.
Full textRobert, Christian P., and George Casella. "Echantillonneurs de Gibbs." In Méthodes de Monte-Carlo avec R, 173–210. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_7.
Full textRobert, Christian P., and George Casella. "Contrôle de convergence et adaptation des algorithmes MCMC." In Méthodes de Monte-Carlo avec R, 211–42. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_8.
Full textDel Moral, Pierre, and Christelle Vergé. "Méthodes de Monte Carlo par Chaînes de Markov (MCMC)." In Mathématiques et Applications, 147–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_6.
Full textConference papers on the topic "Monte-Carlo (Méthode de)"
Ravaux, Simon. "Nouvelle méthode d'étude en propagation Monte-Carlo." In Outils de calcul scientifique : applications industrielles et perspectives. Les Ulis, France: EDP Sciences, 2018. http://dx.doi.org/10.1051/jtsfen/2018out03.
Full textRomdhani, Fekria, Patrick Juillion, François Hennebelle, and Jean François Fontaine. "Estimation des incertitudes de mesure sur bras polyarticulé portable par méthode de Monte Carlo." In 16th International Congress of Metrology, edited by J. R. Filtz, B. Larquier, P. Claudel, and J. O. Favreau. Les Ulis, France: EDP Sciences, 2013. http://dx.doi.org/10.1051/metrology/201304007.
Full textChapoutier, Nicolas, and Davide Mancusi. "Les codes Monte-Carlo : focus TRIPOLI." In Radioprotection : méthodes et outils de calcul en propagation des rayonnements. Les Ulis, France: EDP Sciences, 2019. http://dx.doi.org/10.1051/jtsfen/2019rad02.
Full textDemgne, J., S. Mercier, W. Lair, J. Lonchampt, and M. Baudin. "Méthodes de Quasi Monte-Carlo pour l’évaluation de stratégies d’investissements." In Congrès Lambda Mu 19 de Maîtrise des Risques et Sûreté de Fonctionnement, Dijon, 21-23 Octobre 2014. IMdR, 2015. http://dx.doi.org/10.4267/2042/56097.
Full textReboul, Florent, and Jean-Michel Pou. "Isolement aux bruits aériens dans les bâtiments - Calcul d’incertitude de l’indice d’affaiblissement acoustique apparent pondéré, R’W, selon les méthodes de Monte-Carlo." In 17th International Congress of Metrology, edited by Bernard Larquier. Les Ulis, France: EDP Sciences, 2015. http://dx.doi.org/10.1051/metrology/20150002015.
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