Monte Carlo and Quasi-Monte Carlo Methods

Monte Carlo and Quasi-Monte Carlo Methods

MCQMC 2018, Rennes, France, July 1-6

Tuffin, Bruno; L'Ecuyer, Pierre

Springer Nature Switzerland AG

05/2021

539

Mole

Inglês

9783030434670

15 a 20 dias

836

Descrição não disponível.
Part I Invited Talks, H. Dong and M. K. Nakayama, A Tutorial on Quantile Estimation via Monte Carlo.- L. Herrmann and C. Schwab, Multilevel Quasi-Monte Carlo Uncertainty Quantification for Advection-Diffusion-Reaction.- B. L. Nelson, Selecting the Best Simulated System: Thinking Differently About an Old Problem.- F. Pillichshammer, Discrepancy of Digital Sequences: New Results on a Classical QMC Topic.- Part II Regular Talks, L. Bian, T. Cui, G. Sofronov and J. Keith, Network Structure Change Point Detection by Posterior Predictive Discrepancy.- M. Billaud-Friess, A. Macherey, A. Nouy and C. Prieur, Stochastic Methods for Solving High-Dimensional Partial Differential Equations.- N. Binder and A. Keller, Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete or Piecewise Constant Probability Distributions.- Y. Ding, F. J. Hickernell, and L. A. J. Rugama, An Adaptive Algorithm Employing Continuous Linear Functionals.- A. Ebert,P. Kritzer and D. Nuyens, Constructing QMC Finite Element Methods for Elliptic PDEs with Random Coefficients by a Reduced CBC Construction.- R. El Haddad, J. El Maalouf, C. Lecot and P. L'Ecuyer, Sudoku Latin, Square Sampling for Markov Chain Simulation.- T. Hartung, K. Jansen, H. Leovey, and J. Volmer, Avoiding the Sign Problem in Lattice Field Theory.- R. Hofer, On Hybrid Point Sets Stemming from Halton-Type Hammersley Point Sets and Polynomial Lattice Point Sets.- M. Huber, Robust Estimation of the Mean with Bounded Relative Standard Deviation.- H. Hult, P. Nyquist and C. Ringqvist, Infinite Swapping Algorithm for Training Restricted Boltzmann Machines.- I. Iscoe and A. Kreinin, Sensitivity Ranks by Monte Carlo.- R. Kritzinger and F. Pillichshammer, Lower Bounds on the Lp Discrepancy of Digital NUT Sequences.- H. Leovey and W. Romisch, Randomized QMC Methods for Mixed-Integer Two-Stage Stochastic Programs with Application to Electricity Optimization.- A. F. Lopez-Lopera, F. Bachoc, N. Durrande, J. Rohmer, D. Idier and O. Roustant, Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC.- E. Lovbak, G. Samaey and S. Vandewalle, A Multilevel Monte Carlo Asymptotic-Preserving Particle Method for Kinetic Equations in the Diffusion Limit.- D. Mandel and G. Okten, Randomized Global Sensitivity Analysis and Model Robustness.- A. Petersson, Rapid Covariance-Based Sampling of Linear SPDE Approximations in the Multilevel Monte Carlo Method.- A. Stein and A. Barth, A Multilevel Monte Carlo Algorithm for Parabolic Advection-Diffusion Problems with Discontinuous Coefficients.- T. A. Stepanyuk, Estimates For Logarithmic and Riesz Energies Of Spherical t-Designs.- Y. Suzuki and D. Nuyens, Rank-1 Lattices and Higher-Order Exponential Splitting for the Time-Dependent Schr?odinger Equation.- C. von Hallern and A. Rossler, An Analysis of the Milstein Scheme for SPDEs without a Commutative Noise Condition.- Fei Xie,M. B. Giles, and Zhijian He, QMC Sampling from Empirical Datasets.
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Multi-level Monte Carlo;Complexity and tractability of multivariate problems;Discrepancy theory;Sequential Monte Carlo and particle methods;Digital nets and lattice rules;Monte Carlo, quasi-Monte Carlo, Markov chain Monte Carlo;Rare event simulation;Randomized quasi-Monte Carlo;Variance reduction methods;MC/QMC methods in chemistry, finance, computer graphics, et al.