Monte-Carlo Quasi Monte-Carlo
Quantum Quasi-Monte Carlo Technique for Many-Body Perturbative Expansions
... and quasi-random se- quences lie in the ancient field of Diophantine Approxima- tion – the theory of approximating sets of real numbers by rational ones, especially in its modern form pioneered by Roth [ 41 ] – ...
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Comment on "Sequential Quasi-Monte Carlo Sampling"
... “Sequential Quasi-Monte Carlo Sampling” Pierre L’Ecuyer DIRO, Universit ´e de Montr ´eal, Canada Gerber and Chopin combine SMC with RQMC to accelerate ...
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Stochastic Quasi-Newton Langevin Monte Carlo
... Chain Monte Carlo (SG-MCMC) methods have been proposed for scaling up Monte Carlo compu- tations to large data ...from Quasi- Newton optimization ...
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Bayesian and Quasi-Monte Carlo spherical integration for global illumination
... 4 Summary of contributions The contributions presented in this thesis can be summarized as follows: Spherical Fibonacci Point Sets for Illumination Integrals Quasi-Monte Carlo (QMC) methods exhibit a ...
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Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models
... estimation, Quasi random variables, Monte Carlo methods, Sequential ...Sequential quasi Monte Carlo (SQMC) sampling is a novel sampling scheme proposed by Gerber and Chopin ( ...
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Pépite | Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien
... Le chapitre 5 est extrait de la prépublication [ 8 ]. Dans ce chapitre, nous proposons d’estimer la loi a posteriori en utilisant un système d’équations différentielles stochastique (EDS) dont la dérive est singulière. ...
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Construction d'ensembles de points basée sur des récurrences linéaires dans un corps fini de caractéristique 2 pour la simulation Monte Carlo et l'intégration quasi-Monte Carlo
... appliquer du tempering à la sortie du générateur. Habituellement, la matrice B est une matrice de plein rang k. La matrice Y est la matrice qui détermine le nombre de bits de résolution [r] ...
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Étude de la combinaison de la technique quasi-Monte Carlo randomisé vectoriel avec l'échantillonnage exact
... Dans l’équation (3.2), on peut voir que si la somme des covariances est négative, alors on obtient une réduction de la variance par rapport à l’estimateur Monte Carlo standard. On veut m[r] ...
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Monte Carlo Methods in Statistics
... iterations using 100 independent runs of the Gibbs sampler, along with a single Gibbs run. linear mixed models (Zeger and Karim 1991), the wealth of multivariate models with available condi- tional distributions (and ...
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Monte-Carlo Kakuro
... The quasi group completion problem is easy for low and high percentages of holes and hard for intermediate ...all quasi group completion problems, with any percentage of holes, up to size ...than ...
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Monte Carlo with Determinantal Point Processes
... 2.4. Monte Carlo with DPPs is also reminiscent of randomized quasi-Monte Carlo methods such as scrambled nets, introduced in Section ...
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Monte Carlo method and sensitivity estimations
... interest because of its ability to deal with complex geometries and=or complex spectral properties [10,11]. To our knowledge, the question of computing corresponding parametric sensitivities has not yet been addressed. ...
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Introduction d'orbitales corrélées dans les approches Monte-Carlo quantiques
... calcul Monte-Carlo débute par le choix d’une fonction d’onde de cette ...timisation Monte-Carlo d’une fonction d’essai est une procédure longue et ...
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Monte Carlo Methods and stochastic approximations
... dans la simulation Monte Carlo. Ensuite, nous avons d evelopp e une version adaptative, dans laquelle la variance est r eduite dynamiquement au cours des it erations Monte Carlo. En n, ...
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Etude de l'nteration electon-matiere par methode monte carlo
... I.5 .4. 1 . Nombres aléatoires et pseudo-aléatoires : Nous avons vu que la méthode Monte Carlo est basée sur l’utilisation des nombres aléatoires, c'est-à-dire tirés au sort. Cette expression nous éloigne ...
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Monte Carlo Estimates of Domain-Deformation Sensitivities
... 05.60.2k Monte Carlo methods provide powerful tools for a wide range of problems in statistical physics, biology, engineer- ing science, and medical applications ...for Monte Carlo methods is ...
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Population Monte Carlo for Ion Channel Restoration
... The simulation of the Gamma process is based on an importance sampling scheme, using a hidden Markov representation of the ion channel model.. We study through this model the degeneracy [r] ...
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On variable splitting for Markov chain Monte Carlo
... can stand for parameters of a given model in machine learning [1] or may represent a signal or image to be recovered within an inverse problem. The main approaches to solve these problems can be casted into the class of ...
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Score Bounded Monte-Carlo Tree Search
... Solving Seki problems has been addressed in [16]. We use more simple and easy to define problems than in [16]. Our aim is to show that Monte-Carlo with bounds can improve on Monte-Carlo ...
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Line-sampling-based Monte Carlo method
... for Monte Carlo neutronics calculations in reactors and other systems of complex geometry Proceedings of the Conference on Applications of Computing Methods to Reactor Problems [2] Skullerud H 1968 Journal ...
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