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Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling, by David Vose
Download Ebook Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling, by David Vose
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This book concentrates on the accuracy of risk modelling rather than the management of risk analysis. It provides a comprehensive guide to modelling of uncertainty using spreadsheets and Monte Carlo software on standard PCs. It includes sufficient probability and statistics theory and provides the basic information necessary for a simple risk analysis model.
- Sales Rank: #3697788 in Books
- Published on: 1996-12
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.41" h x .94" w x 6.30" l,
- Binding: Hardcover
- 340 pages
From the Publisher
Based entirely on proven techniques, this superb guide is designed for those who do computer modelling. Stressing the accuracy of risk modelling rather than the management of risk analysis, it provides excellent guidelines for modelling uncertainty using spreadsheets and Monte Carlo software. Features sufficient probability and statistics theory for readers to be confident of the correctness of the methods and understand when and how the theories can be used. Includes scores of graphs, charts, practical exercises and model solutions.
Most helpful customer reviews
23 of 27 people found the following review helpful.
A great primer and reference text for simulation modelling
By mcassin@easynet.on.ca
Whether you are attempting Monte Carlo simulation for the first time or have plenty of practical experience, Vose's book is the best text available.
Vose has done a great job providing a brief but solid introduction to probability and statistics. It also serves as a great practical reference for the details and applications of all of the commonly used probability distribution functions.
He expertly summarizes each of the main steps a modeller will face in developing a model, providing very practical examples with helpful figures illustrating exactly how to implement the example in a spreadsheet.
The introduction could be improved by describing the context of his book, largerly directed towards cost analysis of projects, in the very broad field of Quantiative Risk Analysis. If I could add a section to the book, I would devote a chapter to common errors committed by the novice modeller. Minor criticisms aside, I do not hesitate in recommending this book as a reference for experts. I think it provides the fastest route up the learning curve for the novice modeller.
2 of 6 people found the following review helpful.
An excellent introduction to risk analysis
By A Customer
Vose's book "Quantitative Risk Analysis; A Guide to Monte Carlo Simulation Modelling" is an excellent introduction to practical quantitative risk analysis.
The book is written in a clear and concise style, refreshing to read and easy to assimilate. The text is practical and techniques are illustrated with useful, practical examples.The book has proven immensely useful in my work.
The text is marred by a number of typographical errors, 9 by my count. These typos are a reflection on the typesetting and editing rather than on the technical value of this excellent book.
I have no hesitation in recommending Vose's "Quantitative Risk Analysis" to anyone who wants to learn about the application of Monte Carlo simulation modelling to problems of risk analysis.
Stuart C MacDiarmid BVSc, Ph
7 of 10 people found the following review helpful.
An excellent guide and primer - clear and concise.
By A Customer
This text provides an excellent primer for those interested in the field of Monte Carlo modelling, but without a strong background in mathematics/statistics. The text is extremely concise and well expressed, and leads the reader through each aspect of quantitative risk analysis without burdening him/her with unnecessary detail nor explanation.
I believe that this book will prove to be tremendously useful for all those approaching the subject of quantitative risk analysis, or those who wish to be able to better understand the results of analyses conducted by others. Sam Beckett, Massey University, New Zealand
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