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.
Sobre o autor
Dirk P. Kroese, Ph D, is Australian Professorial Fellow in
Statistics at The University of Queensland (Australia). Dr. Kroese
has more than seventy publications in such areas as stochastic
modeling, randomized algorithms, computational statistics, and
reliability. He is a pioneer of the cross-entropy method and the
coauthor of Simulation and the Monte Carlo Method, Second Edition
(Wiley).
Thomas Taimre, Ph D, is a Postdoctoral Research Fellow at
The University of Queensland. He currently focuses his research on
Monte Carlo methods and simulation, from the theoretical
foundations to performing computer implementations.
Zdravko I. Botev, Ph D, is a Postdoctoral Research
Fellow at the University of Montreal (Canada). His research
interests include the splitting method for rare-event simulation
and kernel density estimation. He is the author of one of the most
widely used free MATLAB® statistical software programs for
nonparametric kernel density estimation.
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