[PDF] Top 20 Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale
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Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale
... space by accepting large transitions with high probability, this efficiency is marred by its high sensitivity to hand-tuned parameters, namely the step size ǫ of the discretisation scheme, the number of ... Voir le document complet
18
Monte Carlo Methods in Statistics
... exhibited by the widespread use of BUGS Lunn et ...offered by the Metropolis–Hastings schemes offer much more efficient solutions when the proposal q(y |x) is appropriately ...improved by ex- ... Voir le document complet
5
A hamiltonian Monte Carlo method for non-smooth energy sampling
... and scale parameters are the same as in experiment # ...converging faster than the rw-MH and ESS ...is faster for a Bernoulli-GG distribution than for a multivariate GG distribu- ...modified ... Voir le document complet
12
A hamiltonian Monte Carlo method for non-smooth energy sampling
... and scale parameters are the same as in experiment # ...converging faster than the rw-MH and ESS ...is faster for a Bernoulli-GG distribution than for a multivariate GG distribu- ...modified ... Voir le document complet
11
Parallel Nested Monte-Carlo search
... We have presented two algorithms that parallelize Nested Monte-Carlo Search on a cluster. The speedup for 64 clients is approximately 56 for Morpion Solitaire which is a problem with a large state space and ... Voir le document complet
6
Méthodes de Monte Carlo en Vision Stéréoscopique
... MCMC estimation Fig. 4.9 – Evolution au cours du temps d’int´ egration de l’estimation de h τ (les 50 000 premi` eres it´ erations sont repr´ esent´ ees) de h τ , en fonction du nombre l de simulations cons´ ecutives ... Voir le document complet
205
Contributions to Monte Carlo Search
... tuning. By way of example, the parameter C > 0 of UCT is in nearly all applications tuned through a more or less automated trial and error ...done by humans, i.e., by researchers that modify or ... Voir le document complet
149
Addressing nonlinearities in Monte Carlo
... 1,2 Monte Carlo is famous for accepting model extensions and model refinements up to infinite ...alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space ... Voir le document complet
12
Quelques contributions sur les méthodes de Monte Carlo
... Dans notre deuxième essai nous proposons plusieurs méthodes de réduction de variance pour l’algorithme de Metropolis Indépendant.. Avant une description plus détaillée du contenu des ess[r] ... Voir le document complet
91
Calcul d’Intégral par la Méthode de Monte-Carlo
... de Monte-Carlo Les méthodes de Monte-Carlo sont particulièrement utilisées pour calculer des intégrales en dimensions plus grandes que 1 (en particulier, pour calculer des surfaces, des ... Voir le document complet
58
Faster Statistical Model Checking by Means of Abstraction and Learning
... incomprehensible by an average system ...case, Monte-Carlo simulation becomes problematic as individ- ual simulation time (time to obtain a single execution trace) could be very ... Voir le document complet
17
Computation of Bias on Measured $\alpha_p$ by Monte-Carlo Simulation
... created by delayed neutrons and kinetic param- eters can be found by studying correlations in the neutron ...created by an external source and parameters are found from studying the flux decay ... Voir le document complet
11
Pépite | Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien
... In this paper we treated LASSO using Gibbs measures. We showed that the scaling of the Gibbs measures as the temperature goes to zero depends on the support and the null components of LASSO. We obtained as a by- ... Voir le document complet
107
Faster Rates for Policy Learning
... a faster rate than the standard error of an efficient estimator of the value of an optimal ...that faster regret decay is possible via plug-in estimation provided a margin condition ... Voir le document complet
38
Étude des artefacts en tomodensitométrie par simulation Monte Carlo
... CHAPITRE 2 INTERACTIONS DES PHOTONS AVEC LA MATIÈRE En imagerie médicale, on peut utiliser une source de rayons X ou de rayons gamma pour produire des images. La variation des tons de gris sur ces images dépend de la ... Voir le document complet
101
Score Bounded Monte-Carlo Tree Search
... If the node n is terminal then the pessimistic and the optimistic values correspond to the score of the terminal position pess(n) = opti(n) = score(n). Initial bounds for in- ternal nodes can either be set to the lowest ... Voir le document complet
12
Utilisation de la recherche arborescente Monte-Carlo au Hex
... algorithme Monte-Carlo, l’algorithme Monte- Carlo utilisant les simulations aléatoires de B gagnait largement contre l’algorithme utilisant les simulations aléatoires de ...de ... Voir le document complet
21
Line-sampling-based Monte Carlo method
... E-mail: mathieu.galtier@insa-lyon.fr Null-collision Monte Carlo algorithms [1, 2, 3, 4] consist in adding a virtual null-collision coefficient to the real extinction one. These collisions, corresponding to ... Voir le document complet
4
Monte-Carlo and Domain-Deformation Sensitivities
... driven by physical ...function. Monte-Carlo methods are preferred for complex geometry process simulations where radiative transfer is preponderant ...the Monte-Carlo method is used ... Voir le document complet
9
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, ... Voir le document complet
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