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.
The MCQMC Conference Series.- The MCQMC Conference Series: P. LEcuyer
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. LEcuyer, 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. Franēois, 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 Franēois-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.
