Quasi-Monte Carlo Methods (QMC)
An application of quasi Monte Carlo methods for the numerical assessment of maintenance strategies
... for quasi Monte Carlo ...istic methods are below the mean of relative errors of MC ...ministic methods have a better estimation than MC ...deterministic methods for N = 2 15 = ...
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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 ...
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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 ...
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On Markov chain Monte Carlo methods for tall data
... putational complexity, the variance of the log-likelihood estimator should be kept around 1.0 when the ideal MH having access to the exact likelihood generates quasi-i.i.d samples from π; or set to around 3.0 ...
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Pépite | Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien
... normal probability integral for large values of the argument, Annals of Mathematical Statistics, vol. 12 364–366 (1941) [8] W.K. Hastings, Multilevel Monte Carlo methods, In LSSC’01 Procee- dings of ...
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Quantum Quasi-Monte Carlo Technique for Many-Body Perturbative Expansions
... optimized quasi-random generators for these problems [ 54 ...of quasi-Monte Carlo relative to Markov Chain Monte Carlo is its greatly accelerated convergence rate in applications ...
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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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Fitting coefficients of differential systems with Monte Carlo methods
... available methods to estimate the coefficients of a differential ...of methods are the quasi-Newton methods ...Other methods, the collocation methods ([6],[8]), estimate the ...
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Stochastic Quasi-Newton Langevin Monte Carlo
... 2 x = 1. In these experiments, we use a constant step size for each method and discard the first 50 samples as burn-in. Note that for constant step size, we no longer have asymptotic unbiased- ness; however, the bias and ...
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Nested Monte-Carlo Search
... cazenave@lamsade.dauphine.fr Abstract Many problems have a huge state space and no good heuristic to order moves so as to guide the search toward the best positions. Random games can be used to score positions and ...
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Reflexive Monte-Carlo Search
... In the next section we present Morpion Solitaire. In the third section we detail re- flexive Monte-Carlo search. In the fourth section we give experimental results. 2 Morpion Solitaire Morpion Solitaire was ...
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Monte Carlo Beam Search
... Nested Monte-Carlo search has been success- fully applied to hard puzzles [15] such as Mor- pion Solitaire or SameGame ...Nested Monte-Carlo Search combining it with beam ...
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Clock Monte Carlo methods
... Keywords: Monte Carlo methods; Metropolis algorithm; factorized Metropolis filter; long-range interactions; spin glasses Markov-chain Monte Carlo methods (MCMC) are powerful ...
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Population Monte Carlo
... sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values, as shown in this ...population Monte Carlo principle we describe here consists ...
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Monte-Carlo Hex
... – type4: bridges, level 2 templates and Ziggurats. Results against type 3 are in table 3. However the time to match the Ziggurat makes the sequential algorithm slower and experiments with a fixed time per move resulted ...
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Monte-Carlo Kakuro
... Nested Monte-Carlo Search at level 1 and level ...Nested Monte-Carlo search at level 2 easily solves almost all the problems in less than 10 seconds when Forward Checking and Iterative ...
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Embedding Monte Carlo search of features in tree-based ensemble methods
... selection methods may also be classified as filters or wrappers, the last decade has seen an increasing interest for so-called embedded feature selection meth- ...latter methods, the feature selection task ...
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Optimized Population Monte Carlo
... The introduction of optimization-based rules for improving the exploration properties of other AIS methods, not belonging to the PMC family, has been explored in the recent works [18], [19], [20], [48]. In [19], ...
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Troc Combinatoire à Monte-Carlo
... de Monte-Carlo 1 Allocation Distribuée de Ressources Indivisibles Cet article s’intéresse au problème de partage de ressources indivisibles par des mécanismes distri- ...
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Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods
... † Current address: Computer Science Department, Princeton University, Princeton, NJ 08540, USA. Received: 13 December 2017; Accepted: 1 February 2018; Published: 9 February 2018 Abstract: This paper presents an approach ...
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