Monte Carlo and Quasi-Monte Carlo Methods: McQmc 2020, Oxford, United Kingdom, August 10-14 (Hardcover, 2022)

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. Francois, 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 Francois-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.