Computational techniques based on simulation have now become an
essential part of the statistician's toolbox. It is thus crucial to
provide statisticians with a practical understanding of those methods,
and there is no better way to develop intuition and skills for
simulation than to use simulation to solve statistical problems.
Introducing Monte Carlo Methods with R 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. While this book constitutes a
comprehensive treatment of simulation methods, the theoretical
justification of those methods has been considerably reduced, compared
with Robert and Casella (2004). Similarly, the more exploratory and less
stable solutions are not covered here.
This book does not require a preliminary exposure to the R programming
language or to Monte Carlo methods, nor an advanced mathematical
background. While many examples are set within a Bayesian framework,
advanced expertise in Bayesian statistics is not required. The book
covers basic random generation algorithms, Monte Carlo techniques for
integration and optimization, convergence diagnoses, Markov chain Monte
Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and
adaptive algorithms. All chapters include exercises and all R programs
are available as an R package called mcsm. The book appeals to anyone
with a practical interest in simulation methods but no previous
exposure. It is meant to be useful for students and practitioners in
areas such as statistics, signal processing, communications engineering,
control theory, econometrics, finance and more. The programming parts
are introduced progressively to be accessible to any reader.
