Synopses & Reviews
Monte Carlo methods are numerical methods based on random sampling and quasi-Monte Carlo methods are their deterministic versions. This volume contains the refereed proceedings of the Second International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing which was held at the University of Salzburg (Austria) from July 9--12, 1996. The conference was a forum for recent progress in the theory and the applications of these methods. The topics covered in this volume range from theoretical issues in Monte Carlo and simulation methods, low-discrepancy point sets and sequences, lattice rules, and pseudorandom number generation to applications such as numerical integration, numerical linear algebra, integral equations, binary search, global optimization, computational physics, mathematical finance, and computer graphics. These proceedings will be of interest to graduate students and researchers in Monte Carlo and quasi-Monte Carlo methods, to numerical analysts, and to practitioners of simulation methods.
Synopsis
Monte Carlo methods are numerical methods based on random sampling and quasi-Monte Carlo methods are their deterministic versions. This proceedings will be of interest to researchers in these areas as well as to numerical analysts and practitioners of simulation methods.
Table of Contents
INVITED PAPERS A Comparison of Some Monte Carlo and Quasi Monte Carlo Techniques for Option Pricing, PETER ACWORTH, MARK BROADIE, AND PAUL GLASSERMAN; Monte Carlo Methods: A Powerful Tool of Statistical Physics, KURT BINDER; Binary Search Trees Based on Weyland and Lehmer Sequences, LUC DEVROYE; A Survey of Quadratic and Inversive Congruential Pseudorandom Numbers, J RGEN EICHENAUER-HERRMANN, EVA HERRMANN, AND STEFAN WEGENKITTL; A Look at Multilevel Splitting, PAUL GLASSERMAN, PHILIP HEIDELBERGER, PERWEZ SHAHABUDDIN, AND TIM ZAJIC; On the Distribution of Digital Sequences, GERHARD LARCHER; Random Number Generators and Empirical Tests, PIERRE DECUYER; The Algebraic- Geometry Approach to Low-Discrepancy Sequences, HARALD NIEDERREITER AND CHAOPING XING; CONTRIBUTED PAPERS: A Monte Carlo Estimator Based on a State Space