图书简介
This book celebrates the career of Pierre L’Ecuyer on the occasion of his 70th birthday. Pierre has made significant contributions to the fields of simulation, modeling, and operations research over the last 40 years. This book contains 20 chapters written by collaborators and experts in the field who, by sharing their latest results, want to recognize the lasting impact of Pierre’s work in their research area. The breadth of the topics covered reflects the remarkable versatility of Pierre’s contributions, from deep theoretical results to practical and industry-ready applications. The Festschrift features article from the domains of Monte Carlo and quasi-Monte Carlo methods, Markov chains, sampling and low discrepancy sequences, simulation, rare events, graphics, finance, machine learning, stochastic processes, and tractability.
Part I Pierre L’Ecuyer: Biography, Part II Invited Contributions: Monte Carlo Methods for Pricing American Options.- Remarks on Levy Process Simulation.- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes.- Truncated Multivariate Student Computations via Exponential Tilting.- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints.- Geometric-Moment Contraction of G/G/1 Waiting Times.- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting.- Rare-Event Simulation via Neural Networks.- Preintegration is Not Smoothing when Monotonicity Fails.- Combined Derivative Estimators.- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting.- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling.- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs.- A Generalized Transformed Density Rejection Algorithm.- Fast Automatic Bayesian Cubature Using Sobol’ Sampling.- Rendering along the Hilbert Curve.- Array-RQMC to Speed Up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System.- Foundations of Ranking
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