Monte Carlo Simulation: The Art of Random Process Characterization

by D. James Benton

There are many textbooks devoted to the theory behind Monte Carlo methods. More often than not, these are heavy on theory and light on example. Rarely do they include the examples in their entirety, mostly presenting the final results in summary form. The aim of this text is to be light on theory and heavy on example. Each example is included in its input, output, and source code or spreadsheet. If you work through all the examples, you should be able to simulate whatever process is needed.

119 pages, Kindle Edition

Published March 15, 2018

About the author

I started writing novels (24 so far) and textbooks (47 so far) after semi-retiring from a long career in industry. In so doing, I hope to entertain, encourage, and also pass on what I have learned.

Reviews

[Ron · Rating 3 out of 5]

Not quite sure what this book had to do with Monte Carlo simulation, it spent most of its time on tangential topics. A lite introduction at best. The statistical underpinnings of some of the C code was questionable. The author used some magic numbers (looking at you, 12) in the random number generation section without motivation that are actually from the underlying calculus, but since there is no comments on the C code about them, the reader is left with the impression its just an arbitrary number.

[Arsev Eraslan · Rating 5 out of 5]

just what I needed to get started, clear to understand

[Eric Holloway · Rating 5 out of 5]

Fun and informative

Lots of simple fun examples that get the point across. All the code is straight forward and avoid architecture astronaut programs. A nice short read!