Introducing Monte Carlo Methods with R

Introducing Monte Carlo Methods with R
Author: Christian Robert
Publisher: Springer Science & Business Media
Total Pages: 297
Release: 2010
Genre: Computers
ISBN: 1441915753

This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.

Monte Carlo Statistical Methods

Monte Carlo Statistical Methods
Author: Christian Robert
Publisher: Springer Science & Business Media
Total Pages: 670
Release: 2013-03-14
Genre: Mathematics
ISBN: 1475741456

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing
Author: Simo Särkkä
Publisher: Cambridge University Press
Total Pages: 255
Release: 2013-09-05
Genre: Computers
ISBN: 110703065X

A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods
Author: Dirk P. Kroese
Publisher: John Wiley & Sons
Total Pages: 627
Release: 2013-06-06
Genre: Mathematics
ISBN: 1118014952

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

Sequential Monte Carlo Methods in Practice

Sequential Monte Carlo Methods in Practice
Author: Arnaud Doucet
Publisher: Springer Science & Business Media
Total Pages: 590
Release: 2013-03-09
Genre: Mathematics
ISBN: 1475734379

Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.

Exploring Monte Carlo Methods

Exploring Monte Carlo Methods
Author: William L. Dunn
Publisher: Elsevier
Total Pages: 594
Release: 2022-06-07
Genre: Science
ISBN: 0128197455

Exploring Monte Carlo Methods, Second Edition provides a valuable introduction to the numerical methods that have come to be known as "Monte Carlo." This unique and trusted resource for course use, as well as researcher reference, offers accessible coverage, clear explanations and helpful examples throughout. Building from the basics, the text also includes applications in a variety of fields, such as physics, nuclear engineering, finance and investment, medical modeling and prediction, archaeology, geology and transportation planning. - Provides a comprehensive yet concise treatment of Monte Carlo methods - Uses the famous "Buffon's needle problem" as a unifying theme to illustrate the many aspects of Monte Carlo methods - Includes numerous exercises and useful appendices on: Certain mathematical functions, Bose Einstein functions, Fermi Dirac functions and Watson functions

Monte Carlo and Quasi-Monte Carlo Methods

Monte Carlo and Quasi-Monte Carlo Methods
Author: Ronald Cools
Publisher: Springer
Total Pages: 624
Release: 2016-06-13
Genre: Mathematics
ISBN: 3319335073

This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. 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 theoretical 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.

Introduction to Rare Event Simulation

Introduction to Rare Event Simulation
Author: James Bucklew
Publisher: Springer Science & Business Media
Total Pages: 262
Release: 2013-03-09
Genre: Mathematics
ISBN: 1475740786

This book presents a unified theory of rare event simulation and the variance reduction technique known as importance sampling from the point of view of the probabilistic theory of large deviations. It allows us to view a vast assortment of simulation problems from a unified single perspective.