Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as ""Monte Carlo."" The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous ""Buffon's needle p

Monte Carlo method. --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Monte Carlo method --- Data processing --- Data processing. --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Meteorological optics --- Monte Carlo method --- Monte-Carlo, Méthode de --- Meteorological optics. --- Monte Carlo method. --- Monte-Carlo, Méthode de --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes --- Atmospheric optics --- Optics, Meteorological --- Atmospheric physics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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 presents essential tools for using quasi–Monte Carlo sampling in practice. The first part of the book focuses on issues related to Monte Carlo methods—uniform and non-uniform random number generation, variance reduction techniques—but the material is presented to prepare the readers for the next step, which is to replace the random sampling inherent to Monte Carlo by quasi–random sampling. The second part of the book deals with this next step. Several aspects of quasi-Monte Carlo methods are covered, including constructions, randomizations, the use of ANOVA decompositions, and the concept of effective dimension. The third part of the book is devoted to applications in finance and more advanced statistical tools like Markov chain Monte Carlo and sequential Monte Carlo, with a discussion of their quasi–Monte Carlo counterpart. The prerequisites for reading this book are a basic knowledge of statistics and enough mathematical maturity to follow through the various techniques used throughout the book. This text is aimed at graduate students in statistics, management science, operations research, engineering, and applied mathematics. It should also be useful to practitioners who want to learn more about Monte Carlo and quasi–Monte Carlo methods and researchers interested in an up-to-date guide to these methods. Christiane Lemieux is an Associate Professor and the Associate Chair for Actuarial Science in the Department of Statistics and Actuarial Science at the University of Waterloo in Canada. She is an Associate of the Society of Actuaries and was the winner of a "Young Researcher Award in Information-Based Complexity" in 2004.

Monte Carlo method. --- Numerical analysis. --- Mathematical analysis --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes --- Mathematical statistics. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- E-books --- Monte Carlo method --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Ce livre est une introduction aux techniques de simulation. Après un bref rappel des techniques fondamentales du calcul des probabilités, il expose divers procédés pour générer en grande quantités des nombres aléatoires. Les transformations de variables utilisées pour simuler des échantillons fictifs d'une variable aléatoire et les tests d'hypothèses font l'objet des chapitres suivants. La dernière partie porte sur la méthode de Monte Carlo et ses applications. Tout au long de l'ouvrage, le lecteur est guidé par de nombreux exemples qui illustrent les applications très concrètes des méthodes p

Numerical analysis --- Monte Carlo method. --- Probabilities. --- Mathematical statistics. --- Simulation methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical calculations --- Stochastic processes --- Mathematical analysis --- Statistical methods