Monte Carlo and Quasi-Monte Carlo Methods
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Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10–14(387 Springer Proceedings in Mathematics & Statistics)

Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10–14(387 Springer Proceedings in Mathematics & Statistics)

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About the 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.

Table of Contents:
The MCQMC Conference Series.- The MCQMC Conference Series: P. L’Ecuyer and F. Puchhammer, Density Estimation by Monte Carlo and Quasi-Monte Carlo.- Sou-Cheng T. Choi, Fred J. Hickernell, Rathinavel Jagadeeswaran, Michael J. McCourt, and Aleksei G. Sorokin, Quasi-Monte Carlo Software.- Part II Regular Talks: P. L’Ecuyer, P. Marion, M. Godin, and F. Puchhamme, A Tool for Custom Construction of QMC and RQMC Point Sets.- Art B. Owen, On Dropping the first Sobol’ Point.- C. Lemieux and J. Wiart, On the Distribution of Scrambled Nets over Unanchored Boxes.- S. Heinrich, Lower Bounds for the Number of Random Bits in Monte Carlo Algorithms.- N. Binder, S. Fricke, and A. Keller, Massively Parallel Path Space Filtering.- M. Hird, S. Livingstone, and G. Zanella, A fresh Take on ‘Barker Dynamics’ for MCMC.- P. Blondeel, P. Robbe, S. François, G. Lombaert and S. Vandewalle, On the Selection of Random Field Evaluation Points in the p-MLQMC Method.- S. Si, Chris. J. Oates, Andrew B. Duncan, L. Carin,and François-Xavier Briol, Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization.- Andrei S. Cozma and C. Reisinger, Simulation of Conditional Expectations under fast mean-reverting Stochastic Volatility Models.- M. Huber, Generating from the Strauss Process using stitching.- R. Nasdala and D. Potts, A Note on Transformed Fourier Systems for the Approximation of Non-Periodic Signals.- M. Hofert, A. Prasad, and Mu Zhu, Applications of Multivariate Quasi-Random Sampling with Neural Networks.- A. Keller and Matthijs Van keirsbilck, Artificial Neural Networks generated by Low Discrepancy Sequences.


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Product Details
  • ISBN-13: 9783030983185
  • Publisher: Springer Nature Switzerland AG
  • Publisher Imprint: Springer Nature Switzerland AG
  • Height: 235 mm
  • No of Pages: 311
  • Series Title: 387 Springer Proceedings in Mathematics & Statistics
  • Width: 155 mm
  • ISBN-10: 3030983188
  • Publisher Date: 21 May 2022
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Sub Title: MCQMC 2020, Oxford, United Kingdom, August 10–14


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Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10–14(387 Springer Proceedings in Mathematics & Statistics)
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Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10–14(387 Springer Proceedings in Mathematics & Statistics)
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