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Monte Carlo and Quasi-Monte Carlo Methods

MCQMC 2020, Oxford, United Kingdom, August 10–14

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Alexander Keller

    1. NVIDIA, Berlin, Germany

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  • Includes proceedings of MCQMC 2020
  • Provides major source of information for Monte Carlo and quasi-Monte Carlo researchers
  • Acts as excellent reference resource in solving high-dimensional computational problems

Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 387)

Included in the following conference series:

Conference proceedings info: MCQMC 2020.

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Table of contents (15 papers)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Invited Talks and Tutorials

    1. Front Matter

      Pages 1-1
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    2. Density Estimation by Monte Carlo and Quasi-Monte Carlo

      • Pierre L’Ecuyer, Florian Puchhammer
      Pages 3-21

    3. Quasi-Monte Carlo Software

      • Sou-Cheng T. Choi, Fred J. Hickernell, Rathinavel Jagadeeswaran, Michael J. McCourt, Aleksei G. Sorokin
      Pages 23-47

  3. Regular Talks

    1. Front Matter

      Pages 49-49
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    2. A Tool for Custom Construction of QMC and RQMC Point Sets

      • Pierre L’Ecuyer, Pierre Marion, Maxime Godin, Florian Puchhammer
      Pages 51-70

    3. On Dropping the First Sobol’ Point

      • Art B. Owen
      Pages 71-86

    4. On the Distribution of Scrambled \((0,m,s)-\)Nets Over Unanchored Boxes

      • Christiane Lemieux, Jaspar Wiart
      Pages 87-130

    5. Lower Bounds for the Number of Random Bits in Monte Carlo Algorithms

      • Stefan Heinrich
      Pages 131-147

    6. Massively Parallel Path Space Filtering

      • Nikolaus Binder, Sascha Fricke, Alexander Keller
      Pages 149-168

    7. A fresh Take on ‘Barker Dynamics’ for MCMC

      • Max Hird, Samuel Livingstone, Giacomo Zanella
      Pages 169-184

    8. On the Selection of Random Field Evaluation Points in the p-MLQMC Method

      • Philippe Blondeel, Pieterjan Robbe, Stijn François, Geert Lombaert, Stefan Vandewalle
      Pages 185-203

    9. Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization

      • Shijing Si, Chris. J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
      Pages 205-221

    10. Simulation of Conditional Expectations Under Fast Mean-Reverting Stochastic Volatility Models

      • Andrei S. Cozma, Christoph Reisinger
      Pages 223-240

    11. Generating From the Strauss Process Using Stitching

      • Mark Huber
      Pages 241-251

    12. A Note on Transformed Fourier Systems for the Approximation of Non-periodic Signals

      • Robert Nasdala, Daniel Potts
      Pages 253-271

    13. Applications of Multivariate Quasi-Random Sampling with Neural Networks

      • Marius Hofert, Avinash Prasad, Mu Zhu
      Pages 273-289

    14. Artificial Neural Networks Generated by Low Discrepancy Sequences

      • Alexander Keller, Matthijs Van keirsbilck
      Pages 291-311
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  1. Monte Carlo and Quasi-Monte Carlo Methods

Keywords

About this book

This volume presents the revised papers of the 14th International Conference in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2020, which took place online during August 10-14, 2020. This book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in statistics, machine learning, finance, and computer graphics, offering information on the latest developments in Monte Carlo and quasi-Monte Carlo methods and their randomized versions.



Editors and Affiliations

  • NVIDIA, Berlin, Germany

    Alexander Keller

Bibliographic Information

  • Book Title: Monte Carlo and Quasi-Monte Carlo Methods

  • Book Subtitle: MCQMC 2020, Oxford, United Kingdom, August 10–14

  • Editors: Alexander Keller

  • Series Title: Springer Proceedings in Mathematics & Statistics

  • DOI: https://doi.org/10.1007/978-3-030-98319-2

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-98318-5Published: 21 May 2022

  • Softcover ISBN: 978-3-030-98321-5Published: 22 May 2023

  • eBook ISBN: 978-3-030-98319-2Published: 20 May 2022

  • Series ISSN: 2194-1009

  • Series E-ISSN: 2194-1017

  • Edition Number: 1

  • Number of Pages: XVI, 311

  • Number of Illustrations: 16 b/w illustrations, 53 illustrations in colour

  • Topics: Statistics, general, Number Theory, Functional Analysis, Optimization

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