Book Description
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.
This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.
The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.
The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.
Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context."
Customer Reviews:
Review for Monte Carlo Methods... by P. Glasserman.......2007-07-16
The book is just right for a reader who is looking for state-of-the-art techniques in Monte-Carlo methods in general. The fact that the book is specific to financial systems does not limit the usability of the book in the manner it is written. There are a lots of useful references one can get out of this book.
The book is for advanced readers in the sense that it requires rigorous mathematical ability to understand all the concepts. It is by no means for a novice reader and requires background in computational mathematics.
Best financial engineering book on MC.......2007-06-29
This is like the bible of Monte Carlo methods in financing. Both a good read and a good reference book. Must have! for any quant on wall street.
good book on Monte Carlo in Finance.......2007-04-02
But it seems the author is a little focused on selling his ideas, but not a very subjective overview of all topics in M-C method in finance.
Excelent choice on finance Monte Carlo.......2007-03-08
Clear and sound theoretical background on applied Monte Carlo for finance.
Brilliant.......2006-12-26
Almost everything related to Monte Carlo in Financial Engineering is covered at just the right level of detail. Quite easy to read too.
Book Description
Praise for
Financial Modeling with Crystal Ball(r) and Excel(r)
"Professor Charnes's book drives clarity into applied Monte Carlo analysis using examples and tools relevant to real-world finance. The book will prove useful for analysts of all levels and as a supplement to academic courses in multiple disciplines."
-Mark Odermann, Senior Financial Analyst, Microsoft
"Think you really know financial modeling? This is a must-have for power Excel users. Professor Charnes shows how to make more realistic models that result in fewer surprises. Every analyst needs this credibility booster."
-James Franklin, CEO, Decisioneering, Inc.
"This book packs a first-year MBA's worth of financial and business modeling education into a few dozen easy-to-understand examples. Crystal Ball software does the housekeeping, so readers can concentrate on the business decision. A careful reader who works the examples on a computer will master the best general-purpose technology available for working with uncertainty."
-Aaron Brown, Executive Director, Morgan Stanley, author of The Poker Face of Wall Street
"Using Crystal Ball and Excel, John Charnes takes you step by step, demonstrating a conceptual framework that turns static Excel data and financial models into true risk models. I am astonished by the clarity of the text and the hands-on, step-by-step examples using Crystal Ball and Excel; Professor Charnes is a masterful teacher, and this is an absolute gem of a book for the new generation of analyst."
-Brian Watt, Chief Operating Officer, GECC, Inc.
"Financial Modeling with Crystal Ball and Excel is a comprehensive, well-written guide to one of the most useful analysis tools available to professional risk managers and quantitative analysts. This is a must-have book for anyone using Crystal Ball, and anyone wanting an overview of basic risk management concepts."
-Paul Dietz, Manager, Quantitative Analysis, Westar Energy
"John Charnes presents an insightful exploration of techniques for analysis and understanding of risk and uncertainty in business cases. By application of real options theory and Monte Carlo simulation to planning, doors are opened to analysis of what used to be impossible, such as modeling the value today of future project choices."
-Bruce Wallace, Nortel
Customer Reviews:
goes beyond deterministic assumptions.......2007-06-24
The book is all about simulations. In financial modelling, as opposed to engineering or science. Readers from the latter 2 fields who have coded simulations will find much in common. The specific equations in the text for finance are largely different from what you've met before. But the basic treatment is essentially the same.
Typically, the text will describe some financial equation. The Crystal Ball program lets you easily generate random data as input to simulations, which it then runs.
Despite Excel in the book's title, the book is mostly about using Crystal Ball. Charnes shows how you can go well beyond a simple deterministic treatment of an income statement or balance sheet. Typically, most companies just use the deterministic approach. The danger is that this approach relies on certain assumptions. Using Crystal Ball and the book, you can test the effect of relaxing these assumptions on the balance sheet. A more robust approach to financial planning.
Financial Modeling with Crystal Ball and Excel.......2007-05-13
Acho que faltou um pouco mais de detalhes nos tópicos, porém o livro apresenta excelente modelos técnicos.
Book Description
This completely revised and updated edition of
Applied Risk Analysis includes new case studies in modeling risk and uncertainty as well as a new risk analysis CD-ROM prepared by Dr. Mun. On the CD-ROM you'll find his Risk Simulator and Real Options Super Lattice Solver software as well as many useful spreadsheet models.
"Johnathan Mun's book is a sparkling jewel in my finance library. Mun demonstrates a deep understanding of the underlying mathematical theory in his ability to reduce complex concepts to lucid explanations and applications. For this reason, he's my favorite writer in this field."
—Janet Tavakoli, President, Tavakoli Structured Finance, Inc. and author of Collateralized Debt Obligations and Structured Finance
"A must-read for product portfolio managers . . . it captures the risk exposure of strategic investments, and provides management with estimates of potential outcomes and options for risk mitigation."
—Rafael E. Gutierrez, Executive Director of Strategic Marketing and Planning, Seagate Technology, Inc.
"Once again, Dr. Mun has created a 'must-have, must-read' book for anyone interested in the practical application of risk analysis. Other books speak in academic generalities, or focus on one area of risk application. [This book] gets to the heart of the matter with applications for every area of risk analysis. You have a real option to buy almost any book?you should exercise your option and get this one!"
—Glenn Kautt, MBA, CFP, EA, President and Chairman, The Monitor Group, Inc.
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Customer Reviews:
A 600-Page Advertisement.......2007-04-26
I found the discussion on nonparametric simulation, though brief, to be very helpful. The book also inspired me to use Excel's Solver in ways I had not considered before.
Beyond that, I was disappointed. The book is poorly edited and lacks a coherent structure. Once in a while, entire strings of paragraphs are repeated in two different parts of the book. More serious, however, is the fact that the book is largely an advertisement for the author's proprietary software.
If you are looking for a few techniques that you can apply in an Excel environment, you will find a few nuggets here and there. However, you will mostly be skimming through the 600 pages of rambling discussion.
Applying Monte Carlo Simulation.......2007-03-30
In the first few chapters the author is certainly successful in making the argument why one should use simulation rather than point estimates. However, the case studies are somewhat vague because the author presents the problem and discusses results of the simulation (using the Risk Simulator S/W) but leaves you wondering how he setup up the model to run this simulation and come up with the results(not even available in the excel examples on the CD). Clear examples of this case include the example on pages 75, 76 & the "Financial Planning" example on page 219.
As an IT Project manager, I didn't find it very helpul in addressing my problems and I wouldn't recommend it for people who want to learn about simulation without going too technical
Book Description
Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.
Book Description
This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
Customer Reviews:
not a good starting point.......2004-12-19
You need to be clear what you are looking for. If you have vaguely heard that MCMC (Monte Carlo Markov Chain) methods are a neat way to apply Bayesian ideas to practical problems, and you want to use them, then this is *not* the book for you. Go to the splendid Gilks et al, Markov Chain Monte Carlo in Practice. Also check out BUGS, which is free software, originally written by Gilks and co and improved by many others.
If you want a more general introduction to Bayesian methods, then Gelman et al, Bayesian Data Analysis is excellent.
If you are unclear about the controversies and want to know why the Bayesian approach is correct, and the others are flat wrong, then read Ed Jaynes book.
So what is this book for. Well, I think you have to be a specialist, interested in further development of the techniques, and in the maths. As a previous reviewer has commented (correctly), in that case you probably have easy access to the journal literature and need to think carefully what extra benefits this book gives you.
Same writer reviewed book 4 times!.......2004-12-19
I depend upon the Amazon reviews to help determine whether to purchase a book as most others do. When a reviewer posts four 5 star reviews of the book (out of 7 total) it biases the rating and makes one wonder whether if the reviewer has an agenda or is related to the authors. This may be a great book, but I have no confidence from the rating given here.
extensive book on MCMC.......2002-10-18
This is truly an oustanding book on MCMC methods for Bayesian
computation. The authors present a nice balance between technical
developments and applications. It covers several topics not covered by other MCMC books, such as HPD regions, model selection, and density estimation. This book is world class.
two great books.......2002-10-17
This is an outstanding book on MCMC methods. The book presents
novel and sophisticated methods for carrying out posterior
computations and summarizing posterior quantities of interest using novel MCMC techniques. The authors present a lot of their
groundbreaking work as well as summarizing the work of many others. The book presents a number of complex models used in real and interesting applications in the biomedical sciences. Two of the authors also have wirtten another outstanding book titled Bayesian Survival Analysis (Ibrahim et al., 2001), which presents modern methods for Bayesian survival analysis and provides a comprehensive and thorough treatment of the subject. The authors are to be congratulated on writing two very fine books. Both books get 5 stars from me.
two great books.......2002-10-15
This is a great book by the authors, covering a wide range of
topics in MCMC. The coverage of the material is deep and novel.
Two of the authors also have published another outstanding book
titled Bayesian Survival Analyis, by Ibrahim et al., which presents
cutting edge and novel methods in the analysis of survival data.
Both books get 5 stars from me. A splendid job by the authors
in writing two very fine books.
Book Description
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available.
The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
Customer Reviews:
It ain't bad, it ain't great, it ain't complete, but it ain't wrong...........2007-03-27
What this book is:
1) Dated. PJ wrote this book in 2002, using thoughts and techniques applicable to a Pentium 4 Xeon world (2001). In 2002 folks often ran option book position MC simulations *overnight.*
* Also, GOOGLE Scholar wasn't out yet....if you wanted to collect all the papers and abstracts on MC methods in 2002 you had to talk to a librarian.
2) Basic. Well, now it is basic....but when it first came out it was sharply focused on finance and it was three years ahead of Glasserman's book.
3) This book is okay for what it is, which is a topical outline, some lecture notes introducing a reasonably well math trained audience to MC and finance. In 2001 MC was a cutting edge new thing. People forget what 2001 was like: Heck, one bank was flogging that it had a 200 node binomial model programmed in Excel available for customer use on an *appointment* basis. That was the state of things at the time.
What this book is not:
1) a cookbook. There is no "cut and paste" code in here. In 2001-2 believe it or not code was made by the sweat of your brow and was considered highly proprietary. Okay so in 2007 we just cobble together Franken-code and debug, but that wasn't the way it was in 2002. There weren't "Numerical Recipes in [code flavour of the month] sites. And folks were fired for showing code ot other people.
2) It won't teach you math. You are supposed to have learned a lot of the stuff this assumes you know.
3) It won't teach you programming in [pick your language] or its step-daughters.
4) Complete. This is expanded lecture notes. Is every low discrepancy method covered? (and all its weird names) No. Is every Greek covered and every possible expression? No. Is every application covered? Hmmm, still looking for that hybrid bond model in here.....not even in the index.
5) A replacement for work. This is a "topics in" and "helpful directions" and "friendly discussion" book. It does not solve your problem on your platform for your goals. It also won't wipe your rear end, buy you beers, tuck you in at night, or let you call it "Rosie." As in Rosie fingers and Harry palm.
So what is this book good for? Well, it is a not too bad a primer, it builds your vocabulary and helps your conceptualization of goals and purposes, and if you move on to Glasserman your comprehension will be much higher, although I'm not sure he covers that much more that much better.
But if you come from a science or math background that has used MC for other purposes, and you know programming, you can probably figure out most of what PJ covers on your own.
for Quants only.......2003-06-24
if you're a quant, you might really love this book
if you're a person who wants to have a "basic" understanding how to use MC for consulting or product pricing with examples, you got the wrong book (not mentioning that your maths must be pretty good).
if you're looking for an Excel example on how to price some basic options, i highly recommend Jackson & Staunton or Wilmott.
Good book.......2003-05-27
This book is pretty good as it covers lots of different areas of Monte Carlo simulation and some of the newer stuffs, such as copulae, etc. The math presentation is brief but to the point as application of the mathematics to Monte Carlo methods is the emphasis. Intuitive ideas behind the formula is explained pretty well as it tells you where certain formula can be used for. It would be helpful to have taken an advanced course in Monte Carlo methods in Finance to appreciate the book. I would personally suggest Glasserman's course at Columbia U. Prof Glasserman is also writing a book on the subject that he uses for lecture notes now. It would turn out to be an even better book to read.
An advanced approach to math methods behind finance.......2002-09-19
Very interesting and well written book reviewing more advanced mathematical concepts which might be relevant for finance engineering - not limited to Monte Carlo methods. The author seems to have a firm background in theoretical physics. Definitely not for simpletons.
CD does not work.......2002-08-29
It is a book for mathematics lovers not financial oriented profesionals. I would not recomend this book for those looking to gain more practical knowledge on this subject.
Book Description
A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience. The Monte Carlo method is a computer-based statistical sampling approach for solving numerical problems concerned with a complex system. The methodology was initially developed in the field of statistical physics during the early days of electronic computing (1945-55) and has now been adopted by researchers in almost all scientific fields. The fundamental idea for constructing Markov chain based Monte Carlo algorithms was introduced in the 1950s. This idea was later extended to handle more and more complex physical systems. In the 1980s, statisticians and computer scientists developed Monter Carlo-based algorithms for a wide variety of integration and optimization tasks. In the 1990s, the method began to play an important role in computational biology. Over the past fifty years, reasearchers in diverse scientific fields have studied the Monte Carlo method and contributed to its development. Today, a large number of scientisits and engineers employ Monte Carlo techniques as an essential tool in their work. For such scientists, there is a need to keep up-to-date with recent advances in Monte Carlo methodologies.
Customer Reviews:
An excellent book on Monte Carlo.......2006-05-04
Jun Liu has been a prominent researcher in MCMC since the mid 90's. His research has contributed a great deal to the development of Gibbs sampler, sequential Monte Carlo, weighting/importance sampling, missing data, and MCMC related applications in Bioinformatics. Not surprisingly, this book has them all, plus many other interesting topics. The final two chapters review some of the theories. This book has a strong flavor in statistical physics, which I like very much. It also contains some applications in, for examples, engineering (e.g. nonlinear filter, sequential Monte Carlo), biology (DNA sequencing), image analysis (clustering) and stochastic optimization.
Jun Liu presents things very clearly and concisely, and hopefully you can benefit from his book.
An awesome book on Monte Carlo methods.......2005-09-13
Now, I am reading this book. I would like to mark it 4.5 stars if possible.
[1] The author is an expert of computational statistics and Bayesian analysis, an active mathematician at Harvard.
[2] The background of this book is related to bioinformatics, physics, etc, which puzzles me a lot while reading.
[3] You can find the author's deep understanding of MC methods throughout the book.
[3] It is suitable for the graduate students of statistics.
[4] It's a little bit pity that this book is not purely written for mathematicians. Anyway, it is a witness of MC methods in development.
Solid theory in Monte Carlo, but less application examples.......2005-08-22
Solid theory in Monte Carlo, but less application examples
A First Rate Book on MC.......2001-08-07
The author is a top young gun from Harvard's Statistics Dept., and is an expert in many applied areas that utilize Monte Carlo, like the red hot bioinformatics. This book covers MC techniques developed in many different fields e.g., physics,structural biology, statistics. It has a wide range of examples, some of which are very new (e.g., bioinformatics) and non-standard. It contains many interesting ideas, and is concise mathematically and easy to read. Highly recommended.
Book Description
This highly accessible and innovative text (and accompanying CD-ROM) uses Excel (R) workbooks powered by Visual Basic macros to teach the core concepts of econometrics without advanced mathematics. It enables students to run monte Carlo simulations in which they repeatedly sample from artificial data sets in order to understand the data generating process and sampling distribution. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software.
Customer Reviews:
Blows Away All Other Intro Texts.......2006-05-31
I am only half finished with this book, but since there is only one other review, I want to get my thoughts up NOW. I may add to them when I have finished.
My wife is an econ major at a small school with very few econ majors. Econometrics is not offered as a course. Although as a practical businessman with a preference for Austrian school economic theory I have a healthy scepticism about quantitative macroeconomic (especially) formulas, I have told my wife that she can not be a part of today's theoretical discussions without a basic understanding of econometrics. I promised to help her self-study this topic, and have reviewed a number of supposedly "introductory" texts (to remain nameless, but they are standards)that have lost me within 50 pages. Neither my wife nor I have calculus or matrix algebra. However, even those texts that say they do not rely on such math knowledge are still confusing. Until now.
Barreto's text is a wonder. The other review gives solid examples of why this is. Let me just say that you will be able to see econometric principles in action. The explanations are incredibly clear, and the work on the beefed up excel spreadsheets effectively demonstrates those explanations. I know this will be difficult to believe, but the text is actually fun to read. My wife and I both have college algebra, business statistics, and basic excel. That's all you need to use this book.
Every university should adopt this book as the intro econometrics text. It provides an approach to learning the topic that is accessible to any intelligent econ student. Those going on to PhD work could supplement with calculus, matrix algebra, and one of the other so-called intro texts. Barreto provides a way for normal econ students to understand econometrics, something that all econ students should be required to do. (Even though much of econometrics is nonsense, knowledge of its applications and mis-applications is still the ticket to being taken seriously in economic debate.)
I only wish I could give this book more than 5 stars. It is a stunning achievement.
Interactive Guide to UNDERSTANDING econometrics.......2006-02-16
When I was a new graduate student I ended up buying several different econometrics texts. No one text had the best explanation for each topic. The problem remained that for many topics I never did find a book which translated the formal mathematical presentation into a practical worked out example, so that I could understand the procedure and how to implement it.
This book and accompanying CD-ROM does that and much more.
Every topic includes guided Microsoft Excel spreadsheets and add-ins which illustrate the topic being addressed. The text clearly explains not only the HOW, but the WHY. The economics and the econometrics are presented with such clarity and unity; bridging the two in a way that none of the other texts do.
In Barreto and Howlands book/CD package you interact with the data and the graphs (they include a superior add-in for creating histograms), and run Monte Carlo simulations to see the behavior of the estimators in repeated sampling. These are "live" spreadsheets that invite you to experiment. For example; there is an Excel workbook which illustrates the correlation coefficient. Rather than a dry recitation of formula and proof, you can interact with the spreadsheet and see exactly how the same coefficient can apply to data having very different patterns. It is one thing to see an illustration, and quite another to actually be the one creating the diagram, simply by running the macros and changing parameters. This "hands on" approach is so vital to actually getting an understanding of the material. I have only a basic understanding of Excel, and have had no difficulties in using the workbooks.
While the limits of Excel are pointed out by the authors, it is important to note the reason for using Excel. It is widely understood and available; there is no learning curve. By using Excel there is no software barrier between the student and understanding the principles of econometric modelling. In less than 1/2 hour I took the data and example of a Probit model using Maximum Likelihood estimation from a course web site from across the country and replicated the results using the add-in provided. Most of that time was used to extract the data from a .pdf file and get it formatted properly for Excel. Once I had the data in Excel, it took less than 2 minutes to run the Probit estimation (my first time using that add-in!) By the way, the results using the authors add-in for solving Probit models with ML estimation were the same as the results from GAUSS code to do the same. The add-in had a distinct advantage though in that a choice for Probit or Logit model estimation using either Non Linear Least Squares or the Maximum Likelihood estimation was just a radio button away! This text can complement any course, regardless of the software used.
Again, the beauty of the book is that you are not just left with Greek formulas that leave you wondering how to do the computations, and you are not left with computer output leaving you to wonder how to interpret that output. The text explains the meaning so powerfully that you are not only armed with an understanding which is useful for success in your course work, but also for applying the quantitative tools in real world analysis and applications. The text is like going to see your favorite professor who is sitting there with you one on one, giving you insight which only comes from experience.
I've been through courses that use Greene, and Judge, as well as introductory texts. This text stands alone in making use of the computing power we have at our disposal today, not to produce more computer printouts, but rather to increase our understanding--providing the sound reasoning for applying that power.
I should add that even after two years of statistics and econometrics I learned quite a lot from the statistics review chapters. Don't be misled by the "Introductory" title. I had learned and executed Artificial Neural Network models in graduate courses, but still learned a lot from the section on correlation in this book. For undergrad students this book will put you on the right path. For grad students it will correct blind spots and misconceptions.
I highly recommend this book/CD package to any econometrics student and to practicing analysts that use regression analysis. The authors have created a product that I wish I had when I was in school, but am glad I found now for applying in my career.
Detailed info on contents as well as the Excel files and add-ins are available from the authors' web site which I found prior to ordering from Amazon. Once I tried the workbooks, I knew I wanted the book. It's 800 pages of solid information and inspired teaching.
http://www.wabash.edu/econometrics/index.htm
Book Description
This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.
Customer Reviews:
Boring!!!.......2004-12-07
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.
Book Description
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation. Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains. Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.
Customer Reviews:
Okay........2005-05-06
First, I'll like to comment on the termiology. I'm PhD specializing in stochastic simulation in operations researcn and I've found the book is written in a language that's not quite standard (it might have something to do with his background in Statistics). Some people may argue that "names" are just "names" but it could cause confusion. And, in the chapter of stochastic approximation, the author failed to mention a couple of well-known existing methodology (somehow show a poor literature review in the field.) Strong emphasis has been given on importance sampling on that particular chapter, but author failed to mention in what context will importance sampling work. If you assume Bayesian approach and have prior on the parameters, then it works. But, if you're a frequentist, it's not necessarily working for your model.
Going back to the first chapter, I found the construction of MCMC is presented much more clearly in Sheldon Ross's Probability Model rather than this book.
great collection of articles on applications.......2000-12-20
Gilks, Richardson and Spiegelhalter edited this marvelous collection of papers on applications of Markov Chain Monte Carlo methods. There has been a big payoff for Bayesians as this method has been a breakthrough for dealing with flexible prior distributions. Most (but not all) of the articles deal with Bayesian applications. The editors themselves start out with an introductory chapter that covers the basic ideas and sets the stage for the articles to come. They provide many references including several of the articles in this volume.
The list of authors is quite impressive and many interesting examples are presented. The editors themselves contribute to other chapters. Spiegelhalter and Gilks co-authored a chapter on a Hepatitis B case study with Best and Inskip. Gilks has a chapter on full conditional distributions and co-authors a chapter on strategies for improving the MCMC algorithms. Richardson contributes a chapter on measurement error.
George and McCulloch deal with the use of Gibbs sampling to choose variables in a model based on a Bayesian approach. Raftery also has a chapter on Bayesian approaches in hypothesis testing and model selection. Green covers image analysis. There are many others (25 chapters in all). This is a great reference for anyone interested in MCMC methods.
The BUGS (Bayesian inference Using Gibbs Sampling)software was developed by Spiegelhalter, Thomas, Best and Gilks to implement Gibbs sampling in a variety of contexts. They illustrate its use along with the diagnostic software CODA in the application in Chapter 2. It is also mentioned in various other chapters in the book. There is currently a version called winBUGS which is designed for Windows operating systems.
Before jumping into the use of MCMC a user would be well advised to study this book.
Very Useful........1997-10-25
We recommend this book to anyone who is interested in learning MCMC methods. Contains a excellent selection of practical examples. Christopher Gordon and Steve Hirschowitz
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- Ordinary People in Extraordinary Times: The Citizenry and the Breakdown of Democracy
- Organizations: Behavior, Structure, Processes
- Organizing Plain and Simple: A Ready Reference Guide with Hundreds of Solutions to Your Everyday Clutter Challenges
- Overdosed America: The Broken Promise of American Medicine
- Principles of Corporate Finance + Student CD + Ethics in Finance PowerWeb + Standard and Poor's (McGraw-Hill/Irwin Series in Finance, Insurance, and Real Est)
- Principles of Marketing (Principles of Marketing)
- Quantitative Methods for Business (with Crystal Ball Pro 2000 v7.1, CD-ROM, and InfoTrac )
- Real Estate Development: Principles and Process 3rd Edition
- Recursive Macroeconomic Theory, 2nd Edition
Books Index
Books Home
Recommended Books
- America Alone: The End of the World as We Know It
- The Sight
- Mini House Style
- Reinventing the Skyscraper: A Vertical Theory of Urban Design
- Reference Guide to the International Space Station
- The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market
- The Harry Bosch Novels Volume 2: The Last Coyote, Trunk Music, Angels Flight
- The Working Class Majority: America's Best Kept Secret
- Suburban Nation: The Rise of Sprawl and the Decline of the American Dream
- The Bernini Bust
General
| Popular Economics
| Business & Investing
| Subjects
| Books
Stochastic Calculus for Finance II: Continuous-Time Models (Springer Finance)
Stochastic Calculus for Finance I: The Binomial Asset Pricing Model (Springer Finance)
Interest Rate Models - Theory and Practice: With Smile, Inflation and Credit (Springer Finance)
The Volatility Surface: A Practitioner's Guide (Wiley Finance)
Heard on the Street: Quantitative Questions from Wall Street Job Interviews
Mathematics for Finance: An Introduction to Financial Engineering (Springer Undergraduate Mathematics Series)
