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Monte Carlo and Quasi-Monte Carlo Methods

MCQMC 2018, Rennes, France, July 1–6

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Bruno Tuffin

    1. INRIA Rennes Bretagne-Atlantique, Campus Universtaire de Beaulieu, Rennes, France

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  2. Pierre L'Ecuyer

    1. Département d’informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montreal, Canada

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  • Recent advances on Monte Carlo (MC) methods and their application
  • Prime source of information on quasi-Monte Carlo (QMC) methods and their randomized versions
  • Covers applications of MC and QMC in statistics, automatic learning, finance, physics, partial differential equations, etc

Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 324)

Included in the following conference series:

Conference proceedings info: MCQMC 2018.

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Table of contents (26 papers)

  1. Front Matter

    Pages i-xi
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  3. Regular Talks

    1. Front Matter

      Pages 105-105
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    2. Network Structure Change Point Detection by Posterior Predictive Discrepancy

      • Lingbin Bian, Tiangang Cui, Georgy Sofronov, Jonathan Keith
      Pages 107-123

    3. Stochastic Methods for Solving High-Dimensional Partial Differential Equations

      • Marie Billaud-Friess, Arthur Macherey, Anthony Nouy, Clémentine Prieur
      Pages 125-141

    4. Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete or Piecewise Constant Probability Distributions

      • Nikolaus Binder, Alexander Keller
      Pages 143-159

    5. An Adaptive Algorithm Employing Continuous Linear Functionals

      • Yuhan Ding, Fred J. Hickernell, Lluís Antoni Jiménez Rugama
      Pages 161-181

    6. Constructing QMC Finite Element Methods for Elliptic PDEs with Random Coefficients by a Reduced CBC Construction

      • Adrian Ebert, Peter Kritzer, Dirk Nuyens
      Pages 183-205

    7. Sudoku Latin Square Sampling for Markov Chain Simulation

      • Rami El Haddad, Joseph El Maalouf, Christian Lécot, Pierre L’Ecuyer
      Pages 207-230

    8. Avoiding the Sign Problem in Lattice Field Theory

      • Tobias Hartung, Karl Jansen, Hernan Leövey, Julia Volmer
      Pages 231-249

    9. On Hybrid Point Sets Stemming from Halton-Type Hammersley Point Sets and Polynomial Lattice Point Sets

      • Roswitha Hofer
      Pages 251-269

    10. Robust Estimation of the Mean with Bounded Relative Standard Deviation

      • Mark Huber
      Pages 271-284

    11. Infinite Swapping Algorithm for Training Restricted Boltzmann Machines

      • Henrik Hult, Pierre Nyquist, Carl Ringqvist
      Pages 285-307

    12. Sensitivity Ranks by Monte Carlo

      • Ian Iscoe, Alexander Kreinin
      Pages 309-328

    13. Lower Bounds on the \(L_p\) Discrepancy of Digital NUT Sequences

      • Ralph Kritzinger, Friedrich Pillichshammer
      Pages 329-344

    14. Randomized QMC Methods for Mixed-Integer Two-Stage Stochastic Programs with Application to Electricity Optimization

      • H. Leövey, W. Römisch
      Pages 345-362
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  1. Monte Carlo and Quasi-Monte Carlo Methods

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About this book

​This book presents the refereed proceedings of the 13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Rennes, France, and organized by Inria, in July 2018. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.

Editors and Affiliations

  • INRIA Rennes Bretagne-Atlantique, Campus Universtaire de Beaulieu, Rennes, France

    Bruno Tuffin

  • Département d’informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Montreal, Canada

    Pierre L'Ecuyer

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