Customer Reviews

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)

5 star:		(3)
4 star:		(1)
3 star:		(0)
2 star:		(1)
1 star:		(1)

 

 

This is an excellent textbook for a course on stochastic simulation

for senior and master students in science. It gives a comprehensive

treatment of all important aspects of dynamic discrete event

simulations with examples and applications in queueing and reliability

models. And each chapter concludes with many problems. In this respect

it is self-contained as it has a chapter on (basic principles of)

probability as well. Just a minor criticism is that the book handles

traditional simulation topics such as building simulation models

and verification/validation rather sketchy (in chapter 3). However,

there are many other topics that you quite often do not see in books on

simulation, like MCMC, optimization, rare-event simulation,

cross-entropy algorithms for combinatorial optimization. The authors

treat the mathematical background and details before giving the

simulation algorithms, which makes the book easy to use as a reference

and suitable for instruction and case studies.

Specifically, I enjoyed reading the last chapter on counting problems

and how to solve them (approximately) by Monte Carlo simulation. There

seems to be many open problems in that area and this chapter is a good

starting point for initiating interesting research.

Difficult computational problems often require solutions which adapt to the problem being solved. Such sequential methods are the focus of Simulation and the Monte Carlo Method, providing an algorithmic approach to hard counting and optimization problems, the simulation of rare-event probabilities through minimum cross-entropy, sensitivity analysis, and Markov Chain Monte Carlo.

This book, by two leading experts in the field, travels well-beyond the usual introduction to stochastic simulation and variance reduction to the heart of the adaptive tools required by the complex simulation and optimization problems of the next decade. I recommend the book for researchers and practitioners alike, interested in the extraordinary power and potential of modern Monte Carlo Methods for solving problems in modeling, statistics and optimization.

This book is supposed to be a revision of the classical book by Rubinstein 1981. As is pointed out in the Preface: "Dramatic changes have taken place in the entire field of Monte Carlo simulation [since 1981]". This edition includes a considerable amount of new, and important, material for which the authors were among principal developers. This alone makes this book a valuable addition to the recent literature on theory and applications of Monte Carlo methods. The book is written in a clear style and is a pleasure to read.

I purchased and read this book as a companion to purchasing Crystal Ball. This book was of no use whatsoever. The authors bombard the reader with complex equations without any examples of real world monte carlo applications. Highly theoretical and the theory is beyond comprehension for those who aren't expert statisticians. I did not even keep this book on hand as a reference. Total waste of money and I can only conclude the 5 star ratings are paid reviews.

This book is a revision of the classic first edition and is authoritative and up to date, including most of the interesting new advances in Monte Carlo methods including modern techniques like perfect sampling and Hit-and-Run algorithms.

This book is regarded as a classic, but the writing style is as soporific as can be. Some scientists should read Gamow or Feynman to learn that one can write a piece of work that's both authoritative and entertaining.