1 Introduction and Examples.- 2 Basic Principles: Rejection, Weighting, and Others.- 3 Theory of Sequential Monte Carlo.- 4 Sequential Monte Carlo in Action.- 5 Metropolis Algorithm and Beyond.- 6 The Gibbs Sampler.- 7 Cluster Algorithms for the Ising Model.- 8 General Conditional Sampling.- 9 Molecular Dynamics and Hybrid Monte Carlo.- 10 Multilevel Sampling and Optimization Methods.- 11 Population-Based Monte Carlo Methods.- 12 Markov Chains and Their Convergence.- 13 Selected Theoretical Topics.- A Basics in Probability and Statistics.- A.1 Basic Probability Theory.- A.1.1 Experiments, events, and probability.- A.1.2 Univariate random variables and their properties.- A.1.3 Multivariate random variable.- A.1.4 Convergence of random variables.- A.2 Statistical Modeling and Inference.- A.2.1 Parametric statistical modeling.- A.2.2 Frequentist approach to statistical inference.- A.2.3 Bayesian methodology.- A.3 Bayes Procedure and Missing Data Formalism.- A.3.1 The joint and posterior distributions.- A.3.2 The missing data problem.- A.4 The Expectation-Maximization Algorithm.- References.- Author Index.
Springer Book Archives
From the reviews: MATHEMATICAL REVIEWS "This book is an excellent
survey of current Monte Carlo methods. A strength of the book is
the inclusion of a number of applications to current scientific
problems. The applications amply demonstrate the relevance of this
approach to modern computing. There is a fairly thorough coverage
of wide variety of Monte Carlo algorithms that have arisen in
diverse fields such as physics, chemistry, biology, etc., and the
relationship among them. The book is highly recommended." SHORT
BOOK REVIEWS "This is a worthwhile reference to recent advances in
sequential Monte Carlo, primarily Bayesian and Markov Chain
methods. To those with an interest in these topics, it is worth a
read."
"This well written book discusses why Monte Carlo techniques are
needed, the importance of Monte Carlo in bioinformatics, target
tracking in nonlinear dynamic systems, in missing data analysis … .
The references are exhaustive. I enjoyed reading this book and
learned a lot about the genetic applications of Monte Carlo
techniques. I recommend this book highly to statisticians and
geneticists." (Ramalingam Shanmugam, Journal of Statistical
Computation and Simulation, Vol. 74 (8), 2004) "Markov chain Monte
Carlo … was introduced to tackle more sophisticated and realistic
statistical models as in the Bayesian approach of statistics. The
author is well known in the area of MCMC methods … . The book is
written in a proper style … . It provides an actual view of
theoretical developments complemented by applications … . It may be
highly recommended for scientists and graduate students who want to
gain some insight in either the theory or application of advanced
Monte Carlo methods." (Ernst Stadlober, Metrika, February, 2004)
"This book provides comprehensive coverage of Monte Carlo methods,
and in the process uncovers and discusses commonalities among
seemingly disparate techniques that arose invarious areas of
application. … The book is well organized; the flow of topics
follows a logical development. … The coverage is up-to-date and
comprehensive, and so the book is a good resource for people
conducting research on Monte Carlo methods. … The book would be an
excellent supplementary text for a course in scientific computing …
." (James E. Gentle, SIAM Review, Vol. 44 (3), 2002) "The strength
of this book is in bringing together advanced Monte Carlo (MC)
methods developed in many disciplines. … Throughout the book are
examples of techniques invented, or reinvented, in different fields
that may be applied elsewhere. … Monte Carlo Strategies in
Scientific Computing offers a large … variety of methods and
examples. Those interested in using MC to solve difficult problems
will find many ideas, collected from a variety of disciplines, and
references for further study." (Tim Hesterberg, Technometrics, Vol.
44 (4), 2002) "This recent addition to the Monte Carlo literature
is divided into 13 chapters and an appendix. It provides both the
methodology and the underlying theory for applying Monte Carlo
techniques to a broad range of problems. … In the Appendix the
author outlines the basics in probability theory and statistical
inference procedures. … this book is a valuable and recommended
reference to Monte Carlo methods; particularly it draws the
attention to recent work in sequential Monte Carlo." (Radu
Theodorescu, Zentralblatt MATH, Vol. 991, 2002) "The book gives a
good introduction to current Monte Carlo methods and explains the
terminology on a moderate level of abstraction. It becomes clear
that any specific problem needs a tailored algorithm to be
efficient. This is the reason for the emergence of variance
reduction methods, importance sampling, rejection, sequential MC,
Metropolis algorithms, Gibbs samplers, Markov Chain MC (MCMC), or
hybrid MC with molecular dynamics. … it is one of the first
attempts to showthe general principles behind an apparent zoo of
methods." (W. Wiechert, Simulation News Europe, Issue 34, 2002)
"The book targets a broader topic, namely all Monte Carlo methods.
… No prior MCMC knowledge is assumed, and the topics are introduced
and motivated along the way. … The book mentions plenty of real
life situations where the techniques discussed … may be applied. …
this book is sure to help the aspiring student eager to peep into
the world of Monte Carlo. At the same time its extensive
bibliography and references will make it useful as a handbook for
the more advanced researcher." (Arnab Chakraborty, Sankhya: Indian
Journal of Statistics, Vol. 64 (1B), 2002) “This book is an
introduction to Monte Carlo methods for graduate students and
researchers in applied fields, and can be used by graduate students
in mathematics for a first contact with the domain or for some
insight into the possible applications of Monte Carlo methods. In
this book, the focus of the Monte Carlo methods presented is the
evaluation of some high-dimensional integrals, such as an average
property of physical systems.” (Gabriel Stoltz, Mathematical
Reviews, Issue 2010 b)
![]() |
Ask a Question About this Product More... |
![]() |