Carlo Lesma

Quasi–Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the last two decades. Their successful implementation on practical problems, especially in finance, has motivated the development of several new research areas within this field to which practitioners and researchers from various disciplines currently contribute.

This book discusses the general principles of the Monte Carlo method with an emphasis on techniques to decrease simulation time and increase accuracy.

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as the textbook for a graduate-level course on Monte Carlo methods. Many problems discussed in the alter chapters can be potential thesis topics for masters’ or Ph.D. students in statistics or computer science departments.

This pocket-sized travel guide to Nice, Cannes & Monte Carlo is a convenient, quick-reference companion to discovering what to do, what to see and how to get around the destination. It covers top attractions like Promenade des Anglais, La Croisette, the Musee Matisse, Zplage, The Vieux Port, as well as hidden gems, including Cap d'Ail and Royal Fort. Our Nice, Cannes & Monte Carlo guide book will save you time and enhance your exploration of this fascinating region. This Nice, Cannes & Monte Carlo travel guide has been fully updated post-COVID-19.

In Volume 1, A Monte Carlo Primer - A Practical Approach to Radiation Transport (the "Primer"), we attempt to provide a simple, convenient, and step-by-step approach to the development, basic understanding, and use of Monte Carlo methods in radiation transport. Using the PC, the Primer begins by developing basic Monte Carlo codes to solve simple transport problems, then introduces a teaching tool, the Probabilistic Framework Code (PFC), as a standard platform for assembling, testing, and executing the various Monte Carlo techniques that are presented. This second volume attempts to continue this approach by using both custom Monte Carlo codes and PFC to apply the concepts explained in the Primer to obtain solutions to the exercises given at the end of each chapter in the Primer. A relatively modest number of exercises is included in the Primer. Some ambiguity is left in the statement of many of the exercises because the intent is not to have the user write a particular, uniquely correct piece of coding that produces a specific number as a result, but rather to encourage the user to think about the problems and develop further the concepts explained in the text. Because in most cases there is more than one way to solve a Monte Carlo transport problem, we believe that working with the concepts illustrated by the exercises is more important than obtaining anyone particular solution.

Rendering photorealistic images is a costly process which can take up to several days in the case of high quality images. In most cases, the task of sampling the incident radiance function to evaluate the illumination integral is responsible for an important share of the computation time. Therefore, to reach acceptable rendering times, the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples.

Der Text gibt eine Einführung in die Mathematik und die Anwendungsmöglichkeiten der Monte Carlo-Methoden und verwendet dazu durchgängig die Sprache der Stochastik. Der Leser lernt die Grundprinzipien und wesentlichen Eigenschaften dieser Verfahren kennen und wird dadurch in den Stand versetzt, dieses wichtige algorithmische Werkzeug kompetent einsetzen und die Ergebnisse interpretieren zu können.

This book is an illustration of the use of Monte Carlo methods applied to solve specific problems in mathematics, engineering, physics, statistics, and science in general.