Overview

This volume presents the refereed proceedings of the 16th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held in Waterloo, Ontario, Canada, and organized by the University of Waterloo in August 2024. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems arising, in particular, in finance, statistics, and computer graphics.

Full Product Details

Author:   Christiane Lemieux ,  Ben Feng
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032105899

ISBN 10:   3032105897
Pages:   406
Publication Date:   02 March 2026
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of Contents

I Invited Talks, Fred J. Hickernell, Nathan Kirk, Aleksei G. Sorokin, Quasi-Monte Carlo Methods: What, Why, and How?.- Alexander Keller, Frances Y. Kuo, Dirk Nuyens, and Ian H. Sloan, Lattice-based Deep Neural Networks: Regularity and Tailored Regularization.- Qingyang Liu, Heishiro Kanagawa, Matthew Fisher, François-Xavier Briol, and Chris. J. Oates, Fast Approximate Solutions of Stein Equations for Post-Processing of MCMC.- Art Owen, Error Estimation for Quasi-Monte Carlo.- II Regular Talks, Ben Adcock, Function Recovery and Optimal Sampling in the Presence of Nonuniform Evaluation Costs.- Charly Andral, Combining Normalizing Flows and Quasi-Monte Carlo.- Vishnupriya Anupindi and Peter Kritzer, Reduced Digital Nets.- Philippe Blondeel, Filip Van Utterbeeck, and Ben Lauwens, Application of quasi-Monte Carlo in Mine Countermeasure Simulations with a Stochastic Optimal Control Framework.- Arne Bouillon, Toon Ingelaere, and Giovanni Samaey, Single-Ensemble Multilevel Monte Carlo for Discrete Ensemble Kalman Methods.- Ana Djurdjevac, Vesa Kaarnioja, Max Orteu, and Claudia Schillings, Quasi-Monte Carlo for Bayesian Shape Inversion Governed by the Poisson Problem Subject to Gevrey Regular Domain Deformations.- Ambrose Emmett-Iwaniw and Nathan Kirk, Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction.-  Vesa Kaarnioja, Ilja Klebanov, Claudia Schillings, and Yuya Suzuki, Lattice Rules Meet Kernel Cubature.- Andrzej Kałuża, Leszek Plaskota, Asymptotic Analysis of Adaptive Simpson Quadratures for Piecewise Smooth Functions.- Pierre L’Ecuyer and Christian Weiß, Lattice Tester: A Software Tool to Analyze Integral Lattices.- Moritz Moeller, Kateryna Pozharska, and Tino Ullrich, Sampling Designs for Function Recovery – Theoretical Guarantees, Comparison and Optimality.- Chinmay Patwardhan, Pia Stammer, Emil Løvbak, Jonas Kusch, Sebastian Krumscheid, Low-Rank Variance Reduction for Uncertain Radiative Transfer with Control Variates.- Pieterjan Robbe, Tiernan A. Casey, Michael W. D. Cooper, Christopher Matthews, Khachik Sargsyan, David A. Andersson, and Habib N. Najm, Bayesian Calibration of Fission Gas Diffusivity in Nuclear Fuels using Multilevel Delayed Acceptance MCMC.- Asaki Saito and Akihiro Yamaguchi, Accelerating True Orbit Pseudorandom Bit Generation Using Newton’s Method.- Christoph Schied and Alexander Keller, Parametric Integration with Neural Integral Operators.- Silei Song, Arash Fahim, and Michael Mascagni, WoS-NN: an Effective Stochastic Solver for Elliptic PDEs with Machine Learning.

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Author Information

Christiane Lemieux is a professor in the Department of Statistics and Actuarial Science at the University of Waterloo. She obtained a PhD in Computer Science from the Université de Montréal in 2000. Her research interests include quasi-Monte Carlo constructions and applications, and dependence concepts in sampling.   Mingbin (Ben) Feng is an Associate Professor and Director of the Master of Actuarial Science Program at the University of Waterloo. He is an Associate of the Society of Actuaries (ASA) and Certified Analytics Professional (CAP-X). He holds a PhD in Industrial Engineering and Management Sciences from Northwestern University. His research focuses on quantitative risk management, financial engineering, Monte Carlo simulation, and nonlinear optimization, with particular interest in efficient simulation algorithms for risk measurement and AI applications in actuarial science.

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