Simulation and the Monte Carlo Method by Reuven Y. Rubinstein and Dirk P. Kroese provides a broad-range introduction to Monte Carlo simulation. The scope of the book goes well beyond topics seen in traditional statistics textbooks on Monte Carlo simulation. It is then no surprise that the intended audience of this book includes students in life sciences, engineering, and “simply anyone interested in Monte Carlo simulation” (p. xiii).
This third edition of the book introduces two new chapters, one on the splitting method and another on stochastic enumeration. Both use concepts from sequential importance (re)sampling, which is introduced previously in the book. I find these new chapters fit very well within the theme and new organization of the book. The added discussion on multilevel Monte Carlo is also appreciated. The presentation of algorithms in pseudo-code is helpful and makes the task of implementing the algorithms easier. I also find the introductory chapters very thorough, assisted by detailed examples and useful problems.
The authors consider a wide range of systems and examples that are helpful in understanding the conceptual arguments. However, they often make heuristic statements that need not apply for all such systems considered. The reader, being just introduced to these topics, is then left with the task of connecting the dots.
This book serves a great purpose in demonstrating the vast applicability of Monte Carlo simulation methods. For the statistical audience, it may not be as beneficial as the more standard text of Robert and Casella (Citation2013), but would make an excellent reference. It is thus quite correctly aimed towards the larger Monte Carlo simulation community and would serve them well.
References
- Robert, C. P., and Casella, G. (2013), Monte Carlo Statistical Methods, New York, Springer.