Monte Carlo and Quasi-Monte Carlo Methods

MCQMC 2020, Oxford, United Kingdom, August 10-14
1. Auflage 2022
ISBN: 978-3-030-98321-5
Verlag: Springer International Publishing

Buch, Englisch, Band 387, 311 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 499 g

Reihe: Springer Proceedings in Mathematics & Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Keller, Alexander

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik Mathematik Mathematische Analysis Funktionalanalysis
• Mathematik | Informatik Mathematik Algebra Zahlentheorie
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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.